Reviewerly/exHarmony huggingface dataset:
High-Performance Siamese Network for Real-Time Tracking

Target tracking algorithms based on deep learning have achieved good results in public datasets. Among them, the network tracking algorithm based on Siamese tracking has a high accuracy and fast speed, which has attracted significant attention. However, the Siamese tracker uses the AlexNet network as its backbone and the network layers are relatively shallow, so it does not make full use of the ability of the deep neural network. If only the backbones of target tracking are replaced, there will be no obvious improvement, such as in the cases of ResNet and Inception. Therefore, this paper designs a wider and deeper network structure. At a wider level, a mechanism that can adaptively adjust the receptive field (RF) size is designed. Firstly, multiple branches are divided by the split operator, and each branch has a different size of kernel corresponding to a different size of RF; then, the fuse operator is used to fuse the information of each branch to obtain the selection weights; and finally, according to the selection, the aggregation feature map is weighted. At a deeper level, a new kind of residual models is designed. The channel is simplified by pruning in order to improve the tracking speed. According to the above, a wider and deeper Siamese network was proposed in this paper. The experimental results show that the structure proposed in this paper achieves a good tracking effect and real-time performance on six kinds of datasets. The proposed tracker achieves an SUC and Prec of LaSOT of 0.569 and 0.571, respectively.
A cross-modal crowd counting method combining CNN and cross-modal transformer

Cross-modal crowd counting aims to use the information between different modalities to generate crowd density images, so as to estimate the number of pedestrians more accurately in unconstrained scenes. Due to the huge differences between different modal images, how to effectively fuse the information between different modalities is still a challenging problem. To address this problem, we propose a cross-modal crowd counting method based on CNN and novel cross-modal transformer, which effectively fuses the information between different modalities and boosts the accuracy of crowd counting in unconstrained scenes. Concretely, we first design double CNN branches to capture the modality-specific features of images. After that, we design a novel cross-modal transformer to extract cross-modal global features from the modality-specific features. Furthermore, we a propose cross layer connection structure to connect the front-end information and back-end information of the network by adding different layer features. At the end of the network, we develop a cross- modal attention module to strengthen the cross-modal feature representation by extracting the complementarities between different modal features. The experimental results show that the method combining CNN and novel cross-modal transformer proposed in this paper achieves state-of-the-art performance, which not only effectively improves the accuracy and robustness of cross-modal crowd counting, but also has good generalization under multimodal crowd counting.
A Deep Learning Approach to Detect Anomalies in an Electric Power Steering System

As anomaly detection for electrical power steering (EPS) systems has been centralized using model- and knowledge-based approaches, EPS system have become complex and more sophisticated, thereby requiring enhanced reliability and safety. Since most current detection methods rely on prior knowledge, it is difficult to identify new or previously unknown anomalies. In this paper, we propose a deep learning approach that consists of a two-stage process using an autoencoder and long short-term memory (LSTM) to detect anomalies in EPS sensor data. First, we train our model on EPS data by employing an autoencoder to extract features and compress them into a latent representation. The compressed features are fed into the LSTM network to capture any correlated dependencies between features, which are then reconstructed as output. An anomaly score is used to detect anomalies based on the reconstruction loss of the output. The effectiveness of our proposed approach is demonstrated by collecting sample data from an experiment using an EPS test jig. The comparison results indicate that our proposed model performs better in detecting anomalies, with an accuracy of 0.99 and a higher area under the receiver operating characteristic curve than other methods providing a valuable tool for anomaly detection in EPS.
Semantic-aware multi-task learning for image aesthetic quality assessment

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Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using Sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements.
Research on Aerial Autonomous Docking and Landing Technology of Dual Multi-Rotor UAV

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system's overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional-integral-derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.
Path reliability-based graph attention networks

Self-attention mechanism has been successfully introduced in Graph Neural Networks (GNNs) for graph representation learning and achieved state-of-the-art performances in tasks such as node classification and node attacks. In most existing attention-based GNNs, attention score is only computed between two directly connected nodes with their representation at a single layer. However, this attention score computation method cannot account for its multi-hop neighbors, which supply graph structure information and have influence on many tasks such as link prediction, knowledge graph completion, and adversarial attack as well. In order to address this problem, in this paper, we propose Path Reliability-based Graph Attention Networks (PRGATs), a novel method to incorporate multi-hop neighboring context into attention score computation, enabling to capture longer-range dependencies and large-scale structural information within a single layer. Moreover, path reliability-based attention layer, a core layer of PRGATs, uses a resource-constrain allocation algorithm to compute the reliable path and its attention scores from neighboring nodes to non-neighboring nodes, increasing the receptive field for every message-passing layer. Experimental results on real-world datasets show that, as compared with baselines, our model outperforms existing methods up to 3% on standard node classification and 12% on graph universal adversarial attack.
MDS2-C3PF: A Medical Data Sharing Scheme with Cloud-Chain Cooperation and Policy Fusion in IoT

The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access control, processing, and storage between doctors and patients, which results in medical data sharing face many challenges such as privacy leakage and malicious feedback from cloud servers on queries. To solve these asymmetry problems, this paper proposes a medical data sharing scheme with cloud-chain cooperation and policy fusion in the IoT. Regarding asymmetrical access control rights, a conflict resolution and fusion algorithm that enables co-authorization of medical data by the doctor and the patient is introduced. To balance the symmetry of medical data storage and processing, a cloud-chain cooperation ciphertext retrieval method is proposed by means of two-stage joint searching from cloud servers and the blockchain, which can not only detect malicious medical data feedback from cloud servers, but also improve the data search efficiency. The security analysis showed that this scheme satisfies the confidentiality and verifiability of the retrieved information, and the feasibility of the proposed scheme was demonstrated through experiments.
CONVERSATIONS: Conversations with a prominent propagator

column Free Access Share on CONVERSATIONS: Conversations with a prominent propagator: Beth Simon Authors: David P. Bunde Knox College Knox CollegeView Profile , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderView Profile , Cynthia Taylor Oberlin College Oberlin CollegeView Profile Authors Info & Claims ACM InroadsVolume 13Issue 4December 2022 pp 9–12https://doi.org/10.1145/3571090Published:18 November 2022Publication History 0citation51DownloadsMetricsTotal Citations0Total Downloads51Last 12 Months51Last 6 weeks41 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks.
A Semi-Supervised Object Detection Algorithm Based on Teacher-Student Models with Strong-Weak Heads

Semi-supervised object detection algorithms based on the self-training paradigm produce pseudo bounding boxes with unavoidable noise. We propose a semi-supervised object detection algorithm based on teacher-student models with strong-weak heads to cope with this problem. The strong and weak heads of the teacher model solve the quality measurement problem of pseudo label localization to obtain higher-quality pseudo labels. The strong and weak heads of the student model are decoupled to reduce the negative impact of pseudo label noise on classification and regression. We reach 52.5 mAP (+1.8) on the PASCAL visual object classes (PASCAL VOC) dataset and even up to 53.5 mAP (+3.2) by using Microsoft common objects in context (MS-COCO) train2017 as additional unlabeled data. On the MS-COCO dataset, our method also improves about 1.0 mAP with the experimental configurations of 10% COCO and COCO-full as labeled data.
Discursive Practices of Internet Communication in the Light of Linguistic Research: A Systematic Review

The review aims to systematise scientific works in the field of linguistics for the 2000-2022 period devoted to the analysis of discursive practices of Internet communication and their characteristics. The review is novel in that it is the first to actualise the latest data obtained by Russian and foreign researchers of Internet communication in the framework of a cognitive-discursive approach to the study of linguistic phenomena, as well as to assess the degree of development of this scientific field in works of modern linguists. Special attention was paid to the corpus of linguistic works of the last few years. At the same time, despite the presence of a fairly large number of works devoted to the study of the Internet language and its various aspects, no comprehensive study of discursive practices of Internet communication has been undertaken to date. As a result, approaches to the formation of the terminological apparatus of Internet linguistics have been described, linguists’ views on the discursive nature of Internet communication and consideration of Internet discourse as a separate type of discourse have been summarised, discursive practices of Internet communication and their characteristics have been examined, possible directions for further research of Internet discourse have been outlined.
Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers

Scene text image super-resolution aims to improve readability by recovering text shapes from low-resolution degraded text images. Although recent developments in deep learning have greatly improved super-resolution (SR) techniques, recovering text images with irregular shapes, heavy noise, and blurriness is still challenging. This is because networks with Convolutional Neural Network (CNN)-based backbones cannot sufficiently capture the global long-range correlations of text images or detailed sequential information about the text structure. In order to address this issue, this paper proposes a Multi-task learning-based Text Super-resolution (MTSR) Network to approach this problem. MTSR is a multi-task architecture for image reconstruction and SR. It uses transformer-based modules to transfer complementary features of the reconstruction model, such as noise removal capability and text structure information, to the SR model. In addition, another transformer-based module using 2D positional encoding is used to handle irregular deformations of the text. The feature maps generated from these two transformer-based modules are fused to attempt improvement of the visual quality of images with heavy noise, blurriness, and irregular deformations. Experimental results on the TextZoom dataset and several scene text recognition benchmarks show that our MTSR significantly improves the accuracy of existing text recognizers.
On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks

Unsupervised image segmentation is one of the most important and fundamental tasks in many computer vision systems. Mixture model is a compelling framework for unsupervised image segmentation. A segmented image is obtained by clustering the pixel color values of the image with an estimated mixture model. Problems arise when the selected optimal mixture model contains a large number of mixture components. Then, multiple components of the estimated mixture model are better suited to describe individual segments of the image. We investigate methods for merging the components of the mixture model and their usefulness for unsupervised image segmentation. We define a simple heuristic for optimal segmentation with merging of the components of the mixture model. The experiments were performed with gray-scale and color images. The reported results and the performed comparisons with popular clustering approaches show clear benefits of merging components of the mixture model for unsupervised image segmentation.
An Optimized Crossover Framework for Social Media Sentiment Analysis

AbstractAbstractUsing deep learning, a new sentiment analysis model is designed in our article. The original data (input) is first pre-processed by stemming, stop-word removal, and tokenization. The projected technique includes 4 phases: "pre-processing, feature extraction, feature selection, and sentiment classification." The characteristics "Bigram-BoW (B-BoW), Threshold Term Frequency-Inverse Document Frequency (T-TFIDF), Unigram, and N-Gram" are then retrieved from the pre-processed data. Utilizing the self-improved Honey Badger Algorithm, the best features out of the chosen features will be chosen (SI-HBA). The basic Honey Badger Algorithm (HBA) has been conceptually improved by this SI-HBA model. The review classification will then be conducted via the proposed optimized crossover framework, which is constructed by hybridizing the optimized Bi-Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) trained with Transfer learning, respectively. The SI-HBA model's optimally chosen features are used to train the hybrid classifier within the optimized crossover architecture. A self-improved Honey Badger Algorithm is used to fine-tune the weight of the Bi-LSTM classifier to improve the classification performance of the gathered reviews (SI-HBA). The final results will indicate whether the reviews are mostly good, negative, or neutral. The proposed sentiment classification model is then validated by a comparison analysis.Keywords: B-BoWDBN with transfer learningoptimized Bi-LSTMsentiment analysisSI-HBAsocial mediaT-TFIDF
Design of language assisted learning model and online learning system under the background of artificial intelligence

Abstract This paper aiming at the problems of scattered operation types, poor extraction effect of learning resources and incomplete assignment of learning tasks in the online language learning platform designs a language assisted learning model under the background of artificial intelligence, proposes an English learning resource extraction algorithm based on the integration of LSA and N-gram models and then proposes a weighted multi-task learning model and it is optimized to improve the sparsity of learning variables. Finally, based on the construction of the assisted learning model, the online language learning system is designed, including demand analysis, overall design and architecture design, to improve the pertinence and efficiency of English learning.
A deep learning-based expert finding method to retrieve agile software teams from CQAs

Currently, many software companies are looking to assemble a team of experts who can collaboratively carry out an assigned project in an agile manner. The most ideal members for an agile team are T-shaped experts, who not only have expertise in one skill-area but also have general knowledge in a number of related skill-areas. Existing related methods have only used some heuristic non-machine learning models to form an agile team from candidates, while machine learning has been successful in similar tasks. In addition, they have only used the number of candidates’ documents in various skill-areas as a resource to estimate the candidates’ T-shaped knowledge to work in an agile team, while the content of their documents is also very important. To this end, we propose a multi-step method that rectifies the drawbacks mentioned. In this method, we first pick out the best possible candidates using a state-of-the-art model, then we re-estimate their relevant knowledge for working in the team with the help of a deep learning model, which uses the content of the candidates’ posts on StackOverflow. Finally, we select the best possible members for the given agile team from among these candidates using an integer linear programming model. We perform our experiments on two large datasets C# and Java, which comprise 2,217,366 and 2,320,883 posts from StackOverflow, respectively. On datasets C# and Java, our method selects, respectively, 68.6% and 55.2% of the agile team members from among T-shaped experts, while the best baseline method only selects, respectively, 49.1% and 40.2% of the agile team members from among T-shaped experts. In addition, the results show that our method outperforms the best baseline method by 8.1% and 11.4% in terms of F-measure on datasets C# and Java, respectively.