Reviewerly/exHarmony huggingface dataset:
RMFPN: End-to-End Scene Text Recognition Using Multi-Feature Pyramid Network

Scene text recognition (STR) plays an important role in various computer vision activities.STR has been a desirable research topic in the computer community, and deep learning-based STR methods have gained tremendous outcomes over the past few years.Earlier state-of-the-art scene text recognition approaches even deliver a notable quantity of inaccurate yields when applied to images caught in real-world environments.Because these images lose precise text content information, previous methods generate less robust features and semantic information about text content.To address this issue, we propose a new approach called Residual Multi-Feature Pyramid Network(RMFPN), which integrates ResNet and Multi-Feature Pyramid Networks to grab multi-level relations, enrich the functionality, and generalization of the feature extractor.We build RMFPN with two convolutional pyramids as a feature extractor, which improves the robustness of features and semantic information to endure scene text recognition of various scales.Comprehensive experiments on diverse datasets demonstrate that our proposed method can acquire significant performance accuracy.The proposed RMFPN acquires a 0.61%, 1.2%, 1%, and 0.2% improvement on SVT, IC15, SVTP, and CUTE datasets.
MapReduce for Graphs Processing: New Big Data Algorithm for 2-Edge Connected Components and Future Ideas

Finding connectivity in graphs has numerous applications, such as social network analysis, data mining, intra-city or inter-cities connectivity, neural network, and many more.The deluge of graph applications makes graph connectivity problems extremely important and worthwhile to explore.Currently, there are many single-node algorithms for graph mining and analysis; however, those algorithms primarily apply to small graphs and are implemented on a single machine node.Finding 2-edge connected components (2-ECCs) in massive graphs (billions of edges and vertices) is impractical and time-consuming, even with the best-known single-node algorithms.Processing a big graph in a parallel and distributed fashion saves considerable time to finish processing.Moreover, it enables stream data processing by allowing quick results for vast and continuous nature data sets.This research proposes a distributed and parallel algorithm for finding 2-ECCs in big undirected graphs (subsequently called ''BiECCA ′′ ) and presents its time complexity analysis.The proposed algorithm is implemented on a MapReduce framework and uses an existing algorithm to find connected components (CCs) in a graph as a sub-step.Finally, we suggest a few novel ideas and approaches as extensions to our work.
Revisiting type-2 triangular norms on normal convex fuzzy truth values

This paper studies t-norms on the space L of all normal and convex fuzzy truth values. We first prove that the only non-convolution form type-2 t-norm constructed by Wu et al. satisfies the distributivity law for meet-convolution and show that t-norm in the sense of Walker and Walker is strictly stronger than tr-norm on L, which is strictly stronger than t-norm on L. Furthermore, we characterize some restrictive axioms of tr-norms for convolution operations on L and obtain some necessary conditions for tr-(co)norm convolution operations on L.
Arabic sentiment analysis using GCL-based architectures and a customized regularization function

Sentiment analysis aims to extract emotions from textual data; with the proliferation of various social media platforms and the flow of data, particularly in the Arabic language, significant challenges have arisen, necessitating the development of various frameworks to handle issues. In this paper, we firstly design an architecture called Gated Convolution Long (GCL) to perform Arabic Sentiment Analysis. GCL can overcome difficulties with lengthy sequence training samples, extracting the optimal features that help improve Arabic sentiment analysis performance for binary and multiple classifications. The proposed method trains and tests in various Arabic datasets; The results are better than the baselines in all cases. GCL includes a Custom Regularization Function (CRF), which improves the performance and optimizes the validation loss. We carry out an ablation study and investigate the effect of removing CRF. CRF is shown to make a difference of up to 5.10% (2C) and 4.12% (3C). Furthermore, we study the relationship between Modern Standard Arabic and five Arabic dialects via a cross-dialect training study. Finally, we apply GCL through standard regularization (GCL+L1, GCL+L2, and GCL+LElasticNet) and our Lnew on two big Arabic sentiment datasets; GCL+Lnew gave the highest results (92.53%) with less performance time.
Distributed K-Anonymous Location Privacy Protection Algorithm Based on Interest Points and User Social Behavior

Location-based services have become an important part of our daily lives, and while users enjoy convenient Internet services, they also face the risk of privacy leakage. K-anonymity is a widely used method to protect location privacy, but most existing K-anonymity location privacy protection schemes use virtual locations to construct anonymity zones, which have the problem of being vulnerable to attackers through background knowledge, while the improved collaborative K-anonymity scheme does not sufficiently consider whether collaborating users share similar attributes. We propose a distributed K-anonymity location privacy-preserving algorithm based on interest points and user social behaviors to solve these problems in existing K-anonymity schemes. The method determines the similarity of users by their interest points and social behaviors and then selects users with high similarity to build an anonymous set of collaborative users. Finally, to ensure the relatively uniform distribution of collaborative users, a homogenization algorithm is used to make the anonymous location points as dispersed as possible. The experimental results showed that our algorithm can effectively resist background attacks, and the uniformly distributed anonymous location points can achieve higher-quality anonymous regions.
Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Breast cancer is one of the most common types of cancer that kills women.When cells become uncontrollably large, cancer develops.As a result, detecting and classifying breast cancer in its early stages is essential so that patients can take the appropriate precautions.On the other hand, mammography images have relatively low sensitivity and effectiveness in detecting breast cancer.Furthermore, MRI (Magnetic Resonance Imaging) has higher detection sensitivity for breast cancer than mammography.In this research, a novel Radial Basis Function Networks model (RBFN) with a Mayfly Optimization Algorithm (MAO) mechanism has Breast MRI scans aid in the early detection of breast cancer.Following the system's training on Magnetic Resonance Imaging (MRI) breast images, a unique Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter is developed for pre-processing noisy MRI image material.Backgrounds were removed before recovering breast cancer photos with a Contrast Limited Histogram Equalization (CLAHE) filter.Furthermore, the new study effort's performance is compared to earlier studies and this model is simulated using Python.The proposed model, RBFN-MAO, also outperforms previous models in terms of performance and precision with an accuracy of 97.54%.In comparison, it is 85.28, 80.95, 76.94, 85.39 and 90.32% for Convolution Neural Networks You Only Look Once (CNN-YOLO), Residual Networks (ResNet50), Diffusion Convolution Neural Networks (DCNN), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models, respectively.
Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy

Various attempts have been conducted to improve the performance of text-based sentiment analysis. These significant attempts have focused on text representation and model classifiers. This paper introduced a hybrid model based on the text representation and the classifier models, to address sentiment classification with various topics. The combination of BERT and a distilled version of BERT (DistilBERT) was selected in the representative vectors of the input sentences, while the combination of long short-term memory and temporal convolutional networks was taken to enhance the proposed model in understanding the semantics and context of each word. The experiment results showed that the proposed model outperformed various counterpart schemes in considered metrics. The reliability of the proposed model was confirmed in a mixed dataset containing nine topics.
Automated speech scoring of dialogue response by Japanese learners of English as a foreign language

Purpose: This study builds a new system for automatically assessing learners’ speech elicited from an oral discourse completion task (DCT), and evaluates the prediction capability of the system with a view to better understanding factors deemed influential in predicting speaking proficiency scores and the pedagogical implications of the system. Methodology: We developed a system with a tripartite structure using an automatic speech recogniser, a set of modules that compute a number of speech features, and a scoring model. In total, 210 participants with intermediate English language proficiency level were administered a multi-turn oral DCT, a task which closely resembles discourse in real-life situations. The collected speech and transcribed files were eyeballed and rated by human raters first. Eighty percent of the original dataset was then used to train and examine our prediction model against the remainder of the dataset. Findings: The exact agreement between human and machine scores was 72%, moderately high, and comparable to the literature on automated speech scoring. It could provide a basis for deploying the system in a low-stakes practice environment. Originality/value: This study makes a unique contribution to the wider scholarship, where the use of single-turn DCTs remains prevalent, by presenting a new reliable scoring system for learner speech using an automated DCT with multiple turns. It offers useful insight into ways in which the system could be used in a low-stakes environment, including foreign language classroom settings.
Aspect sentiment mining of short bullet screen comments from online <scp>TV</scp> series

Abstract Bullet screen comments (BSCs) are user‐generated short comments that appear as real‐time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect‐level sentiment analysis framework. Our framework, BSCNET, is a pre‐trained language encoder‐based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi‐supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine‐tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%–19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect‐level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.
A deep learning-based side channel attack model for different block ciphers

Recently, there has been renewed interest in the combination of deep learning and side-channel analysis (SCA). Many previous studies have transformed the traditional SCA into a classification problem in deep learning. This paper considers it as a regression problem based on the principle that the changes of some circuit states are related to the special operation in cipher. We proposed a regression model which consists of an initial layer, a deep feature mining dense layer, and a regression layer. In the term of dataset, there are two sources of data: the raw ASCAD power traces and the data sampled from FPGA implementation of AES and PRESENT. The mainly advantages of this model and regression task processing method is that it can adapt to different cryptographic algorithms on the same hardware device. Moreover, the experimental result that the model can significantly improve the attack accuracy of SCA. In ASCAD, its prediction accuracy achieves 2.90% and 3.63% for two different intermediate values, and their correlation coefficient evaluation 0.873, 0.840. In FPGA power dataset, their prediction and correlation coefficient are 3%, 4%, and 0.963, 0.987 respectively.
Human–object interaction prediction in videos through gaze following

Understanding the human-object interactions (HOIs) from a video is essential to fully comprehend a visual scene. This line of research has been addressed by detecting HOIs from images and lately from videos. However, the video-based HOI anticipation task in the third-person view remains understudied. In this paper, we design a framework to detect current HOIs and anticipate future HOIs in videos. We propose to leverage human gaze information since people often fixate on an object before interacting with it. These gaze features together with the scene contexts and the visual appearances of human-object pairs are fused through a spatio-temporal transformer. To evaluate the model in the HOI anticipation task in a multi-person scenario, we propose a set of person-wise multi-label metrics. Our model is trained and validated on the VidHOI dataset, which contains videos capturing daily life and is currently the largest video HOI dataset. Experimental results in the HOI detection task show that our approach improves the baseline by a great margin of 36.3% relatively. Moreover, we conduct an extensive ablation study to demonstrate the effectiveness of our modifications and extensions to the spatio-temporal transformer. Our code is publicly available on https://github.com/nizhf/hoi-prediction-gaze-transformer.
Digitalization Transformation: Essence of Virtual Reality Indulging in the Mental Health of People

Virtual reality as a powerful tool of Industry 4.0 is working alongside traditional therapy methods for the treatment of mental health problems as an effective treatment strategy for mental conditions. It has extensive uses custom- tailored for treatment of various mental issues. In this study, the review of the use of Virtual Reality in the treatment of mental health disorders is examined and changes in the procedure have been suggested in accordance with the problems faces in therapeutic procedures. The respective benefits arising from the use of such technology in the treatment are also studied. The study also contributes to how the psychiatric health of the person is affected by the alteration of the markers during the treatment procedure done in a Virtual environment and the factors responsible for such change. The combined usage of the technology and traditional methods hasresulted in a positive outcome. Based on the examination of the technology within the mental health sector, the study concludes that VR technology with slight modifications and in conjunction with traditional therapy methods can be integrated into the mental health sector with higher efficiency to improve the treatment procedure of various mental health diseases.
Enabling Technologies for Wildlife Conservation

Wildlife conservation has always been a majorconcern on a global scale, but with the advent of Industry 4.0, or the fourth Industrial Revolution, the use of smart, intelligent technology to safeguard biological wildlife has increased significantly. The technology also facilitates to monitor species and their habitat in novel ways. The paper reviews various methodologies of wildlife conservation using drones which includes radiotracking, GPS and Non- GPS transmitters, Unmanned aerial vehicles or Robotics aircrafts. The Artificial Intelligence reviews the methods including Convolutional Neural Network, Machine learning and Deep learning algorithms. Lastly, the paper demonstrates recommendation and the limitations which are possessed by various models of wildlife conservation and come up with different suggestions for the assurance of wildlife heritage effectively and efficiently. The current state of wildlife conservation is largely based on various technological models of Industrial revolution which was earlier continued by sociocultural conventional sources based on human intelligence only.
Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder

Data is a crucial component in machine learning. However, many datasets contain sensitive information such as personally identifiable health and financial data. Access to these datasets must be restricted to avoid potential security concerns. Synthetic data generation addresses this problem by generating artificial data that are similar to, and thus could be used in place of, the original real-world data. This research introduces a synthetic data generation approach called CT-VAE that uses Combinatorial Testing (CT) and Variational Autoencoder (VAE). We first use VAE to learn the distribution of the real-world data and encode it in a latent, lower-dimensional space. Next, we use CT to sample the latent space by generating a t-way set of latent vectors, each of which represents a data point in the latent space. A synthetic dataset is generated from the t-way set by decoding each latent vector in the set. Our experimental evaluation suggests that machine learning models trained with synthetic datasets generated using our approach could achieve performance that is very similar to those trained with real-world datasets. Furthermore, our approach performs better than several state-of-the-art synthetic data generation approaches.
Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error

Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Outof-distribution (OOD) data is known to increase the probability of error and research into metrics that identify what dissimilarities in data affect model performance is on-going. Recently, combinatorial coverage metrics have been explored in the literature as an alternative to distribution-based metrics. Results show that coverage metrics can correlate with classification error. However, other results show that the utility of coverage metrics is highly dataset-dependent. In this paper, we show that this dataset-dependence can be alleviated with metric learning, a machine learning technique for learning latent spaces where data from different classes is further apart. In a study of 6 open-source datasets, we find that metric learning increased the difference between set-difference coverage metrics (SDCCMs) calculated on correctly and incorrectly classified data, thereby demonstrating that metric learning improves the ability of SDCCMs to anticipate classification error. Paired t-tests validate the statistical significance of our findings. Overall, we conclude that metric learning improves the ability of coverage metrics to anticipate classifier error and identify when OOD data is likely to degrade model performance.
Human Activity Recognition based on Transformer in Smart Home

With the advancement of artificial intelligence, smart home has attracted much attention from scholars. Human Activity Recognition (HAR) is a crucial foundation for various applications in smart home. In this paper, to improve the accuracy of HAR and promote the development of applications and services in smart home, we propose a Transformer-based approach that integrates multiple sensor sequence inputs for HAR. We integrate sequence features, collect contextual information, and employ Transformer to recognize various activities for the CASAS Aruba dataset that uses environmental sensors. The validation results on real-world dataset demonstrate its effectiveness compared to traditional machine learning and deep learning methods.
Deep Reinforcement Learning with Copy-oriented Context Awareness and Weighted Rewards for Abstractive Summarization

This paper presents a deep context-aware model with a copy mechanism based on reinforcement learning for abstractive text summarization. Our model is optimized using weighted ROUGEs as global prediction-based rewards and the self-critical policy gradient training algorithm, which can reduce the inconsistency between training and testing by directly optimizing the evaluation metrics. To alleviate the lexical diversity and component diversity problems caused by global prediction rewards, we improve the richness of the multi-head self-attention mechanism to capture context through global deep context representation with copy mechanism. We conduct experiments and demonstrate that our model outperforms many existing benchmarks over the Gigaword, LCSTS, and CNN/DM datasets. The experimental results demonstrate that our model has a significant effect on improving the quality of summarization.
HCS: A Hybrid Data Security Enhancing Model Based on Cryptography Algorithms

With the advancement in technology, plenty of data is being shared between users using various mediums. And with this excessive data transmission, it needs to be supervised that the data transmitted needs to be secured to maintain the confidentiality and integrity of the data. There is a technique that allows secure data transmission without losing its integrity and classification, and that is Cryptography. In general, Cryptography is about correct encryption, which gives the safe transmission of information and can be interpreted only by a specific source and recipient. For data security, the encryption techniques should meet a set of necessary criteria. This paper proposes a new hybrid cryptographic model and its data security comparison on various parameters with existing Cryptography Algorithms AES and DES. The results indicate better performance as compared to existing algorithms.
Comparative Analysis of Convolutional and Long Term Short Memory Architectures in Machine Learning

Training machines to develop intelligence is one of the popular studies of computer science. As long as machines with human intelligence is used for the benefits of humans, this technology will flourish civilization. Digital data capturing may process numerous challenges as documents may contain a combination of multiple unstructured data types. Such documents are easily readable to a human but machines may be unable to interpret the data as humans do. Neural network approaches like multilayer perceptron, CNN, RNN and LSTN are effective recognition methods as it can effectively deal with complex computing problems related to training networks and in the process of automating and producing predictive results. In deep neural networks, machines can detect prominent features of a given object without human interventions. The classification and recognition rate of all present algorithms depend chiefly on input images. This paper presents two approaches of deep learning for document recognition problem, convolutional neural network and the result of CNN is continued to RNN and LSTM approaches for synthesizing document. The study attempts to compare the results of CNN vs CNN-LSTM in terms of precision rate.