Reviewerly/exHarmony huggingface dataset:
A Novel U-Net Based Deep Learning Method for 3D Cardiovascular MRI Segmentation

Medical multiobjective image segmentation aims to group pixels to form multiple regions based on the different properties of the medical images. Segmenting the 3D cardiovascular magnetic resonance (CMR) images is still a challenging task owing to several reasons, including individual differences in heart shapes, varying signal intensities, and differences in data signal-to-noise ratios. This paper proposes a novel and efficient U-Net-based 3D sparse convolutional network named SparseVoxNet. In this network, there are direct connections between any two layers with the same feature-map size, and the number of connections is reduced. Therefore, the SparseVoxNet can effectively cope with the optimization problem of gradients vanishing when training a 3D deep neural network model on small sample data by significantly decreasing the network depth, and achieveing better feature representation using a spatial self-attention mechanism finally. The proposed method in this paper has been thoroughly evaluated on the HVSMR 2016 dataset. Compared with other methods, the method achieves better performance.
Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.
Qualitative comparison of elemental concentration in soils and other geomaterials using FP-XRF

Field portable X-ray fluorescence (FP-XRF) has tremendous potential in geoenvironmental engineering as a qualitative assessment tool. Identification of the elevated concentrations of the selected elements (Cr, Cu, Mn, Ni, Pb, and Zn) in various geomaterials like soil-like-material (SLM), incinerated bottom ash (IBA), construction and demolition waste (CDW), zinc tailings (ZT) and fly ash (FA) was performed by FP-XRF and compared with the local soil-Delhi silt. Comparably higher concentrations (mg/kg) of Cr (401.0), Cu (499.0), Pb (532.0), Zn (608.0) in SLM, Cr (195.0), Cu (419.0), Ni (93.0), Pb (931.0), Zn (771.0) in IBA and Cr (195.0), Cu (4000.0), Pb (671.0), Zn (7122.0) in ZT were observed. CDW and FA showed similar concentrations range as in local soils. FP-XRF was also used in-situ on local soil at 11 sites to examine its ability to identify the elements with significant variations in concentrations. The results showed high variability in Cl and S concentration values across the 11 sites attributed to the changing moisture content and dissolved salts. The concentration range for the remaining elements were similar at all sites. The verification of the detected elements through visual inspection of the spectrum was also carried out.
Optimizing Automatic Text Classification Approach in Adaptive Online Collaborative Discussion–A Perspective of Attention Mechanism-Based Bi-LSTM

A text semantic classification is an essential approach to recognizing the verbal intention of online learners, empowering reliable understanding, and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as convolutional neural networks and recurrent neural network, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The bidirectional long short-term memory (Bi-LSTM) is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the verbal features and keywords, and the generated keywords were weighed in accordance with the attention mechanism. A total of 12 000 sentences generated in online collaborative discussion activities have been classified into six categories, namely, statement, negotiation, question, management, emotion, and others. Results showed that the classification accuracy of attention-Bi-LSTM reached 81.50%, which is higher than that of the baseline Bi-LSTM model. This study theoretically uncovers the features of collaborative discussion of onliners and practically provides an effective approach to automatic behavior analysis in an online context.
Modeling and Calibration of a Galvanometer-Camera Imaging System

The galvanometer-camera imaging system (GCIS), which mainly consists of a galvanometer and a camera, can drastically enlarge the field of view without decreasing imaging resolution by quickly switching viewing directions. However, the lack of adequate imaging model has hindered GCIS from being used to 3D vision tasks with accuracy requirements, such as 3D reconstruction and 6D pose estimation. A nonphysical parameter imaging model is proposed, which quantitatively depicts the relation among the input parameters of the galvanometer, the imaging position of a 3D point on the camera image plane, and the equation of the corresponding incoming ray. An ingenious method is put forward to calibrate the proposed imaging model. The GCIS with calibrated imaging model has much enlarged applicability. As an example, the method for object pose estimation by using the calibrated GCIS is presented and verified. Experimental results demonstrate the effectiveness and high performance of the proposed whole set of methods.
Cyber Risk Recommendation System for Digital Education Management Platforms

Covid-19 pandemic has ushered in a new school and academic year for students in a distance learning regime. This new daily routine was unprecedented and undoubtedly unusual, especially for the younger ones. At this point and at these ages, the risk of cyber fraud is even greater. The transition from the physical environment to the Internet took place quickly without the appropriate time to control potential risks and the proper information and training of teachers and students. Some common threats that need to be addressed to protect learners and their data when using e-learning methods are malicious remote access, malware, phishing, cyber fraud, etc. Considering the above situation, this work presents an innovative cyber risk recommendation system for digital education management platforms. The system in question is a distributed two-stage algorithm based on game theory and machine learning, which is trained by the constant change in the choice of recommendations by users to maximize security. We examine the algorithm's ability to simulate a user system in which everyone independently selects a user recommendation, assesses the environment and the implications of this choice, and then concludes whether it will continue to have that recommendation fixed. The methodology with which we have represented the digital e-learning system has been done with an approach that directly corresponds with their general view as a cyber-physical-social system. We consider the digital school as an environment that brings limitations, leading us to a pretty demanding personalization problem. Users coexist in this environment, in which everyone acts voluntarily but influences and is influenced by the surrounding environment. Our results lead us to conclude that this algorithm responds in a fully effective, flexible, and efficient way to the needs of protection and risk assessment of e-learning education systems.
Sharing of Teaching Resources for English Majors Based on Ubiquitous Learning Resource Sharing Platform and Neural Network

Teachers in traditional English education devote a great deal of time and effort to developing excellent English teaching courseware and other teaching materials. However, because the network's powerful sharing capability was not fully utilized, poor resource sharing hampered the school's overall teaching efficiency. The goal of this paper is to investigate how to use a ubiquitous learning resource sharing platform and a neural network to analyze the method of English major education resource sharing. Because the BP neural network algorithm has better classification and prediction functions, it was proposed because it can classify and predict information in the sharing of teaching resources for English majors. This allows for seamless sharing of teaching materials. According to the findings of this study, there were 562.13 million people learning English in China in 2015, accounting for 37% of the population. By 2018, 945.61 million people were learning English, accounting for 85 percent of the population. Almost every student is learning English, demonstrating the language's importance. As a result, the importance of English instruction is clear, and the sharing of teaching resources should be prioritized.
Parameterization of Cross-token Relations with Relative Positional Encoding for Vision MLP

Vision multi-layer perceptrons (MLPs) have shown promising performance in computer vision tasks, and become the main competitor of CNNs and vision Transformers. They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers. However, the heavily parameterized token-mixing layers naturally lack mechanisms to capture local information and multi-granular non-local relations, thus their discriminative power is restrained. To tackle this issue, we propose a new positional spacial gating unit (PoSGU). It exploits the attention formulations used in the classical relative positional encoding (RPE), to efficiently encode the cross-token relations for token mixing. It can successfully reduce the current quadratic parameter complexity $O(N^2)$ of vision MLPs to $O(N)$ and $O(1)$. We experiment with two RPE mechanisms, and further propose a group-wise extension to improve their expressive power with the accomplishment of multi-granular contexts. These then serve as the key building blocks of a new type of vision MLP, referred to as PosMLP. We evaluate the effectiveness of the proposed approach by conducting thorough experiments, demonstrating an improved or comparable performance with reduced parameter complexity. For instance, for a model trained on ImageNet1K, we achieve a performance improvement from 72.14\% to 74.02\% and a learnable parameter reduction from $19.4M$ to $18.2M$. Code could be found at https://github.com/Zhicaiwww/PosMLP.
Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting

End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale has always been a tough trade-off since recognizing a small text instance usually requires enlarging the whole image, which brings high computational costs. In this paper, to address this problem, we propose a novel cost-efficient Dynamic Low-resolution Distillation (DLD) text spotting framework, which aims to infer images in different small but recognizable resolutions and achieve a better balance between accuracy and efficiency. Concretely, we adopt a resolution selector to dynamically decide the input resolutions for different images, which is constraint by both inference accuracy and computational cost. Another sequential knowledge distillation strategy is conducted on the text recognition branch, making the low-res input obtains comparable performance to a high-res image. The proposed method can be optimized end-to-end and adopted in any current text spotting framework to improve the practicability. Extensive experiments on several text spotting benchmarks show that the proposed method vastly improves the usability of low-res models. The code is available at https://github.com/hikopensource/DAVAR-Lab-OCR/ .
Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition.Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography.The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics.This paper has analyzed the features related to the computer vision of digital content to determine its integrity.This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method.By the proposed deep belief network with loss handling, the manipulation of video/image is found by means of a pairwise learning approach.This proposed approach has improved the accuracy of the detection rate by 98% on various datasets.
SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers, source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words' polysemy and the relationship between bag-of-words in keywords.To solve the above problem, Semantic Featured Convolution Neural Networks (SF-CNN) is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94% than the traditional algorithms.
Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

Nowadays, deepfake is wreaking havoc on society.Deepfake content is created with the help of artificial intelligence and machine learning to replace one person's likeness with another person in pictures or recorded videos.Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information.These manipulated pictures and videos will undoubtedly have an enormous societal impact.Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human eye.Therefore, automated solutions employed by DL can be an efficient approach for detecting deepfake.Though the "black-box" nature of the DL system allows for robust predictions, they cannot be completely trustworthy.Explainability is the first step toward achieving transparency, but the existing incapacity of DL to explain its own decisions to human users limits the efficacy of these systems.Though Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of these systems.This work proposes to provide a comprehensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explanations (LIME) to assure its validity and reliability.This study identifies real and deepfake images using different Convolutional Neural Network (CNN) models to get the best accuracy.It also explains which part of the image caused the model
$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge.In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics.Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications.However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data.Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems.In this paper, we propose an approach, named JOWL (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.
CG-Recognizer: A biosignal-based continuous gesture recognition system

Gesture is a new communication form of human–computer interaction access because of its abundance and diversity. Continuous gesture recognition based on biosignals has gained widespread attention. However, there are two challenges: the movement epenthesis of continuous gestures leads to the deformations of original gestures, and the different signing speeds among various people lead to the diversity of signal length. To solve them, CG-Recognizer: a biosignal-based continuous gesture recognition system is proposed. To the first challenge, gesture signals are transformed into spectrograms, and a feature generator based on a channel-separated convolutional neural network is constructed to extract the spatio-temporal features of gesture signals. For the second challenge, a standard deviation-based signal segmentation algorithm is first proposed to segment signals and label the features of signals. Then, the labeled signal features are sent to the You Only Look Once version 5 (YOLOv5) model for gesture recognition. The experimental results indicate that the mean accuracy of CG-Recognizer is over 94% on 50 commonly used discrete gestures and over 98% on 40 continuous gestures composed of the above gestures.
A Survey on Detection of Cyberbullying in Social Media Using Machine Learning Techniques

Nowadays, in this technologically sound world, the use of social media is very popular. Along with the advantages of social media, there are many terrible influences as well. Cyberbullying is a crucial difficulty that needs to be addressed here. Cyberbullying influences both men and women victims. Harassment by way of cyberbullies is a big issue on social media. Cyberbullying affects both in terms of the mental and expressive manner of someone. So there's a need to plan a technique to locate and inhibit cyberbullying in social networks. To conquer this condition of cyberbullying, numerous methods have been developed using Machine Learning techniques. This paper presents a brief survey on such methods and finds that Support Vector Machine (SVM) is a very efficient method of cyberbullying detection that provides the highest accuracy.
Analysis and Classification of Abusive Textual Content Detection in Online Social Media

With every passing day, the amount in which social media content is being produced is enormous. This contains a large amount of data that is abusive. Which in turn is responsible for disturbing the peace, affecting the mental health of users going through that kind of data and in some cases is responsible for causing riots and chaos leading to loss of life and property. Knowing the sensitivity of such situations, it becomes necessary to tackle the abusive textual content. This paper presents the analysis of abusive textual content detection techniques. And for these, researchers have been developing methods to automatically detect such content present on social media platforms. This study also discusses the domains of automatic detection that have been investigated utilizing various methodologies. Several machine learning techniques that have recently been implemented to detect abusive textual material have been added. This paper presents a detailed summary of the existing literature on textual abusive content detection techniques. This paper also discusses the categorization of abusive content presented in the research, as well as potential abusive communication methods on social media. Deep learning algorithms outperform previous techniques; however, there are still significant drawbacks in terms of generic datasets, feature selection methods, and class imbalance issues in conjunction with contextual word representations.
Sentiment Analysis Using CatBoost Algorithm on COVID-19 Tweets

Sentimental analysis is a study of emotions or analysis of text as an approach to machine learning. It is the most well-known message characterization device that investigates an approaching message and tells whether the fundamental feeling is positive or negative. Sentimental analysis is best when utilized as an instrument to resolve the predominant difficulties while solving a problem. Our main objective is to identify the emotional tone and classify the tweets on COVID-19 data. This paper represents an approach that is evaluated using an algorithm namely—CatBoost and measures the effectiveness of the model. We have performed a comparative study on various machine learning algorithms and illustrated the performance metrics using a Bar-graph.