Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Ship Target Detection Algorithms Based on Deep Learning

Ship target detection plays an important role in marine military and shipping.Deep learning allows us to extract deep features from large amount of data.In this paper, we select three different target detection algorithms based on deep learning, including Faster R-CNN, SSD, and YOLOv3, and apply the same dataset to these three algorithms.Then compare the results of the experiments and evaluate the performance of each algorithm.According to the result of the experiments, Faster R-CNN has a relatively better performance.The result of this paper would provide a reference for selecting a ship target detection algorithm.
A New Language-Independent Deep CNN for Scene Text Detection and Style Transfer in Social Media Images

Due to the adverse effect of quality caused by different social media and arbitrary languages in natural scenes, detecting text from social media images and transferring its style is challenging. This paper presents a novel end-to-end model for text detection and text style transfer in social media images. The key notion of the proposed work is to find dominant information, such as fine details in the degraded images (social media images), and then restore the structure of character information. Therefore, we first introduce a novel idea of extracting gradients from the frequency domain of the input image to reduce the adverse effect of different social media, which outputs text candidate points. The text candidates are further connected into components and used for text detection via a UNet++ like network with an EfficientNet backbone (EffiUNet++). Then, to deal with the style transfer issue, we devise a generative model, which comprises a target encoder and style parameter networks (TESP-Net) to generate the target characters by leveraging the recognition results from the first stage. Specifically, a series of residual mapping and a position attention module are devised to improve the shape and structure of generated characters. The whole model is trained end-to-end so as to optimize the performance. Experiments on our social media dataset, benchmark datasets of natural scene text detection and text style transfer show that the proposed model outperforms the existing text detection and style transfer methods in multilingual and cross-language scenario.
Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization

We evaluate texture quantified from ultrasound Nakagami parametric images for non-invasive characterization of breast tumors, as Nakagami images can more faithfully represent intrinsic tumor characteristics than standard B-mode images.Parametric images were formed using sliding windows applied to ultrasound envelope data. To analyze the trade-off between spatial resolution and stability of estimated Nakagami parameters for texture quantification, two different window sizes were used for image formation: (i) the standard square window with sides equal to three times the pulse length of incident ultrasound, and (ii) a smaller square window with sides equal to exactly the pulse length. Texture was quantified from two different regions of interest (ROIs) consisting of the tumor core and a 5 mm surrounding margin. A total of 186 texture features were analyzed for each ROI, and feature selection was used to identify the most relevant feature sets for breast tumor characterization.Texture quantified from parametric images formed using the two different windows did not outperform each other by a significant margin. However, when the mean pixel value within the tumor region of the parametric images was incorporated with the texture features, texture quantified from the tumor core and surrounding margin of images formed using the standard square window thoroughly outperformed other considerations for breast lesion characterization. The highest performing set of texture and mean value features yielded a significant AUC of 0.94, along with sensitivity of 90.38% and specificity of 89.58%.We establish that texture quantified from ultrasound Nakagami parametric images are diagnostically relevant and may be used to characterize breast lesions effectively.
A Study on Strategies to Improve Elementary School Teachers' Professionalism in the Context of Integrated Education

Integrated education is an educational concept of special education, which refers to the provision of equal quality education and related services for all children, including those with special educational needs, in regular classes in regular schools in the community, based on the belief of meeting the diverse learning needs of all children.In recent years, the elementary school level has become the main basis for the development of integrated education, and the number of children with special needs attending classes has gradually increased, placing higher demands on the professionalism of elementary school teachers.This study aims to improve the professionalism of elementary school teachers in the context of integrated education at three levels: educational administration, schools and teachers, which will not only inject new energy into the development of elementary school teachers' professionalism, but also better promote the development of "class attendance" in China.
Timeline Registration for Electronic Health Records.

Electronic Health Record (EHR) data are captured over time as patients receive care. Accordingly, variations among patients, such as when a patient presents for care during the course of a disease, introduce bias into standard longitudinal EHR data analysis methods. We, therefore, aim to provide an alignment method that reduces this bias. We structure this task as a registration problem. While limited prior research on longitudinal EHR data considered registration, we propose a robust registration method to provide better data alignment by estimating the optimum time shift at each time point. We validate the proposed method for mortality prediction. We utilize a Recurrent Neural Network (RNN), time-varying Cox regression model, and Logistic Regression (LR) for mortality prediction. Results suggest our proposed registration method enhances mortality prediction with at least a 1-2% increase in major evaluation metrics utilized.
Anomaly Attack Detection in Wireless Networks Using DCNN

The use of wireless devices in industrial sectors has increased due to its various advantages related to cost and flexibility. However, legitimate wireless communication systems are vulnerable to cybersecurity attacks, due to its inherent open nature. Detection of rogue devices therefore plays a crucial role in critical wireless applications. In this paper we design a deep convolutional neural network (DCNN) to classify legitimate and rogue devices using raw IQ samples as input data. An algorithm is presented to find the optimal number of convolutional layers and number of filters for each layer under an accuracy constraint, in order to enable fast prediction time. Furthermore, we investigate how wireless channel models affect the accuracy and prediction time of the designed DCNN model. Our obtained results are benchmarked against previous DCNN models. Moreover, we discuss how the systems should react to a detected rogue device, considering the IEC 62443 standard.
HSFV-based Action Recognition Using Recurrent Neural Networks

Human action recognition is one of the basic problems in the field of computer vision, which has a wide range of applications in video surveillance, sports analysis, medical care. Since the skeleton data can not be easily affected by background and views and has small computational cost, skeleton-based action recognition has attracted a lot of researchers’ interest. In recent years, some researchers have proposed to use the joints connection methods to mine the intrinsic correlation between skeleton joints as the feature representation of motion skeleton, and good results are achieved. However, there is still the problem of confusion when distinguishing actions with similar motion segments. To solve this problem, a human skeleton feature vector model (HSFV) is proposed in this paper. By constructing the feature extraction reference frame, the model uses OKS indicator to calculate and generate the feature vector describing the human posture. The feature vectors are input into the recurrent neural network, the experimental results on public and self-built datasets show that the human skeleton feature vector model based action recognition method proposed in this paper can distinguish actions with similar motion segments, and has the advantages of simple training and small calculation cost. It has broad application prospects in the fields of process detection, motion evaluation and so on.
Service Point Searching for Disabled People using Wheelchair based Robotic Path Planning and ArUco Markers

The relative growth in the number of elderly individuals, especially those who are incapable of self-movement, and the decreasing number of care personnel is a challenging issue to handle. An automated robotic Wheelchair with an expert system can provide the services to the elderly and physically challenged people. However, optimal path finding and object or obstacle identification are major concerns to be optimized. With this point in mind, this paper proposes an automated robotic Wheelchair with ArUco marker-based target service point's identification and an efficient robotic path planning algorithm. It can also avoid the obstacles and search for the service points using an image processing-based low computational system. An improved A * path planning algorithm makes the shortest path from the source to the target service point in this proposed method. An ArUco Marker-based low-competition system is used for obstacle avoidance and service point searching. It can also adjust the path if an obstacle appears on the planned route. The experimental results show the effectiveness of the proposed approach, including service point searching, obstacle avoidance, and shortest path finding.
Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing

We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This unified model borrows from the basic Dual Recursive Network (DRN) architecture developed by Cai et al. The proposed model makes use of cross-stitch units that enable multi-task learning across two separate DRN models, each tasked for single image deraining and desnowing, respectively. By fixing cross-stitch units at several layers of basic task-specific DRN networks, we perform multi - task learning over the two separate DRN models. To enable blind image restoration, on top of these structures we employ a simple neural fusion scheme which merges the output of each DRN. The separate task-specific DRN models and the fusion scheme are simultaneously trained by enforcing local and global supervision. Local supervision is applied on the two DRN submodules, and global supervision is applied on the data fusion submodule of the proposed model. Consequently, we both enable feature sharing across task-specific DRN models and control the image restoration behavior of the DRN submodules. An ablation study shows the strength of the hypothesized CMUDRN model, and experiments indicate that its performance is comparable or better than baseline DRN models on the single image deraining and desnowing tasks. Moreover, CMUDRN enables blind image restoration for the two underlying image restoration tasks, by unifying task-specific image restoration pipelines via a naive parametric fusion scheme. The CMUDRN implementation is available at https://github.comNCL3D/CMUDRN.
An ensemble approach for the identification and classification of crime tweets in the English language

Twitter is a famous social media platform, which supports short posts limited to 280 characters. Users tweet about many topics like movie reviews, customer service, meals they just ate, and awareness posts. Tweets carrying information about some crime scenes are crime tweets. Crime tweets are crucial and informative and separate classification is required. Identification and classification of crime tweets is a challenging task and has been the researcher’s latest interest. The researchers used different approaches to identify and classify crime tweets. This research has used an ensemble approach for the identification and classification of crime tweets. Tweepy and Twint libraries were used to collect datasets from Twitter. Both libraries use contrasting methods for extracting tweets from Twitter. This research has applied many ensemble approaches for the identification and classification of crime tweets. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) Classifier assigned with the weights of 1,2,1,1 and 1 respectively ensemble together by a soft weighted Voting classifier along with term frequency – inverse document frequency (TF-IDF) vectorizer gives the best performance with an accuracy of 96.2% on the testing dataset.
Research on the Enlightenment of Sports Literacy to Graduate Education and Training

Graduate education is the key stage of high quality talent cultivation.However, based on the characteristics of graduate students, the work itself has its particularity and importance.This paper focuses on postgraduate education, from the beginning of sports literacy, putting forward the question whether sports literacy would produce irreplaceable influence in the process of graduate education and training.Finally this paper make a reasonable response.At the same time, it gives some enlightening strategies for sports literacy to graduate education, in order to cultivate all-round development of innovative high-quality talents to increase efficiency and expand ideas.
Exploring Self-Distillation Based Relational Reasoning Training for Document-Level Relation Extraction

Document-level relation extraction (RE) aims to extract relational triples from a document. One of its primary challenges is to predict implicit relations between entities, which are not explicitly expressed in the document but can usually be extracted through relational reasoning. Previous methods mainly implicitly model relational reasoning through the interaction among entities or entity pairs. However, they suffer from two deficiencies: 1) they often consider only one reasoning pattern, of which coverage on relational triples is limited; 2) they do not explicitly model the process of relational reasoning. In this paper, to deal with the first problem, we propose a document-level RE model with a reasoning module that contains a core unit, the reasoning multi-head self-attention unit. This unit is a variant of the conventional multi-head self-attention and utilizes four attention heads to model four common reasoning patterns, respectively, which can cover more relational triples than previous methods. Then, to address the second issue, we propose a self-distillation training framework, which contains two branches sharing parameters. In the first branch, we first randomly mask some entity pair feature vectors in the document, and then train our reasoning module to infer their relations by exploiting the feature information of other related entity pairs. By doing so, we can explicitly model the process of relational reasoning. However, because the additional masking operation is not used during testing, it causes an input gap between training and testing scenarios, which would hurt the model performance. To reduce this gap, we perform conventional supervised training without masking operation in the second branch and utilize Kullback-Leibler divergence loss to minimize the difference between the predictions of the two branches. Finally, we conduct comprehensive experiments on three benchmark datasets, of which experimental results demonstrate that our model consistently outperforms all competitive baselines. Our source code is available at https://github.com/DeepLearnXMU/DocRE-SD
Planning for Learning Object Properties

Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.
Beam Search Optimized Batch Bayesian Active Learning

Active Learning is an essential method for label-efficient deep learning. As a Bayesian active learning method, Bayesian Active Learning by Disagreement (BALD) successfully selects the most representative samples by maximizing the mutual information between the model prediction and model parameters. However, when applied to a batch acquisition mode, like batch construction with greedy search, BALD suffers from poor performance, especially with noises of near-duplicate data. To address this shortcoming, we propose a diverse beam search optimized batch active learning method, which explores a graph for every batch construction by expanding the highest-scored samples of a predetermined number. To avoid near duplicate beam branches (very similar beams generated from the same root and similar samples), which is undesirable for lacking diverse representations in the feature space, we design a self-adapted constraint within candidate beams. The proposed method is able to acquire data that can better represent the distribution of the unlabeled pool, and at the same time, be significantly different from existing beams. We observe that the proposed method achieves higher batch performance than the baseline methods on three benchmark datasets.
The Effect of Preferences in Abstract Argumentation under a Claim-Centric View

In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Recent work has revealed that semantical and computational properties can change when reasoning is performed on claim-level rather than on the argument-level, while under certain natural restrictions (arguments with the same claims have the same outgoing attacks) these properties are conserved. We now investigate these effects when, in addition, preferences have to be taken into account and consider four prominent reductions to handle preferences between arguments. As we shall see, these reductions give rise to different classes of claim-augmented argumentation frameworks, and behave differently in terms of semantic properties and computational complexity. This strengthens the view that the actual choice for handling preferences has to be taken with care.
Optimised Machine Learning-based Translation of Indian Sign Language to Text

Sign language translation through deep learning is a popular topic among researchers nowadays.It opens the doors of communication for deaf and mute people by translating sign language gestures.The translation of input sign language gestures into text is called the sign language translation system (SLTS).In this paper, optimised machine learning-based SLTS for Indian sign language (ISL) has been proposed to facilitate deaf-mute persons.Further, this paper presents a simulation analysis of the impact of the number of convolution layers, size of stride function, epochs, and activation function on the accuracy of translation of ISL gestures.An optimised ISL translation system (ISLTS) for fingerspelled alphanumeric data of 36 classes using a convolution neural network (CNN) with a novel RADAM_NORM optimiser has been proposed.The proposed system has been implemented using two datasets-the first customised ISL alphanumeric dataset has been taken from Kaggle and the second dataset has been prepared by the author consisting of 36 classes and nearly 50K images.The accuracy of the proposed ISLTS on the first dataset is 99.446 % and on the second dataset is 97.889%.
Learning qubo Models for Quantum Annealing: A Constraint-Based Approach

Quantum Annealing is an optimization process taking advantage of quantum tunneling to search for the global optimum of an optimization problem, although, being a heuristic method, there is no guarantee to find the global optimum. Optimization problems solved by a Quantum Annealer machine are modeled as Quadratic Unconstrained Binary Optimization (qubo) problems. Combinatorial optimization problems, where variables take discrete values and the optimization is under constraints, can also be modeled as qubo problems to benefit from Quantum Annealing power. However, defining quadratic penalty functions representing constraints within the qubo framework can be a complex task. In this paper, we propose a method to learn from data constraint representations as a combination of patterns we isolated in $$Q$$ matrices modeling optimization problems and their constraint penalty functions. We actually model this learning problem as a combinatorial optimization problem itself. We propose two experimental protocols to illustrate the strengths of our method: its scalability, where correct pattern combinations learned over data from a small constraint instance scale to large instances of the same constraint, and its robustness, where correct pattern combinations can be learned over very scarce data, composed of about 10 training elements only.
Investigation of the variability in the tool marks produced by modified blank firing pistols on fired 7.65 mm projectiles

Firearms identification has an important place in forensic ballistic investigations since the weapons are widely used in criminal offences. Firearm examiners resolve many case files, through the use of automatic systems or comparison microscopes. Advanced forensic technologies like BALİSTİKA helps to record and analyze non standard ballistic evidence. In today's world, with the ease of access to materials and production technique information, pistols modified from blank firers are frequently encountered as crime tools. In this study, the characteristics of 7.65 mm fired cartridge cases obtained by controlled shots from blank firing modified pistols were examined, and their detection performances were compared by means of the Balistika system. Although distinctive differences are not expected after successive test shootings, balistically important changes were seen after the use of blank firing modified pistols and the 3D imaging system proved to be useful in observing such differences. The analyses showed that the modifications in weapons lead to variation in the ballistic characteristics and reduce the accuracy of the detection performance, which may result in flawed forensic decisions. It was also found that the deviations in ballistic impressions of modified blank firing pistols were greater than that of standard fabricated and hand-made pistols. This unique study contributed to the forensic sciences literature by focusing on the impact of modified weapons on ballistic characteristics.