Reviewerly/exHarmony huggingface dataset:
Horizontal Voting Ensemble Based Predictive Modeling System for Colon燙ancer

Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel.Thus, detecting benign at the earliest helps reduce the mortality rate.In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method.Identifying different patterns in microscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developed HVE algorithm can increase the system's performance according to the combined models from the last epochs of the proposed architecture.Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS.Results prove that the proposed PMS has ∼8% performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity.The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.
Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer (BCa) is a leading cause of death in the female population across the globe.Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed.However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries.Early diagnosis is the only option to minimize the risks of BCa.Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN).Hence, this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images.The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection.This proposed model is a multi-view-based computer-aided diagnosis (CAD) model, which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique (MLO) and cranio-caudal (CC).The digital mammogram images are collected from the digital database for screening mammography (DDSM) dataset.In preprocessing, median filter and contrast limited adaptive histogram equalization (CLAHE) techniques are utilized for image enhancement.After preprocessing, the segmentation is performed using the region growing (RG) algorithm.The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients (PHOG) and the AlextNet model.Finally, the classification is performed using the weighted k-nearest neighbor (WkNN) optimized with sequential minimal optimization (SMO).The classified images are evaluated based on accuracy, recall, precision, specificity, f1-score, and mathews correlation coefficient (MCC).Additionally, the false positive and error rates are evaluated.The proposed model obtained 98.57% accuracy, 98.61% recall, 99.25% specificity, 98.63% precision, 97.93% f1-score, 96.26% MCC, 0.0143 error rate, and 0.0075 false positive rate (FPR).Compared to the existing models, the research model has obtained better performances and outperformed the other models.
Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text. Neural word embedding has been employed in literature, since it is less labor-intensive than automatic feature engineering. By ignoring the context of sentiment, most of the word-embedding models follow syntactic data of words. The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets (DFODL-SAAT) model. The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language. At first, data cleaning and pre-processing steps are performed to convert the input tweets into a useful format. In addition, TF-IDF model is exploited as a feature extractor to generate the feature vectors. Besides, Attention-based Bidirectional Long Short Term Memory (ABLSTM) technique is applied for identification and classification of sentiments. At last, the hyperparameters of ABLSTM model are optimized using DFO algorithm. The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects. The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.
Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Fraud cases have been a risk in society and people's property security has been greatly threatened.In recent studies, many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis.These algorithms are also suitable for fraudulent phone text recognition.Compared to these tasks, the semantics of fraudulent words are more complex and more difficult to distinguish.Recurrent Neural Networks (RNN), the variants of RNN, Convolutional Neural Networks (CNN), and hybrid neural networks to extract text features are used by most text classification research.However, a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively.Therefore, a new model is proposed in this paper.In the fraudulent phone text, the knowledge that can be learned by the model includes the sequence structure of sentences, the correlation between words, the correlation of contextual semantics, the feature of keywords in sentences, etc.The new model combines a bidirectional Long-Short Term Memory Neural Network (BiLSTM) or a bidirectional Gate Recurrent United (BiGRU) and a Multi-Head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden layer.BiLSTM or BiGRU is used to build the encoding and decoding layer.Multi-head attention mechanism module with convolution (MHAC) enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this paper.The THUCNews data sets and fraudulent phone text data sets are used in experiments.Experiment results show that compared with the baseline model, the proposed model (LMHACL) has the best experiment results in terms of Accuracy, Precision, Recall, and F1 score on the two data sets.And the performance indexes on fraudulent phone text data sets are all above 0.94.
Contrastive Clustering for Unsupervised Recognition of Interference Signals

Interference signals recognition plays an important role in anti-jamming communication.With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and show better performance than traditional recognition algorithms.However, there is no unsupervised interference signals recognition algorithm at present.In this paper, an unsupervised interference signals recognition method called double phases and double dimensions contrastive clustering (DDCC) is proposed.Specifically, in the first phase, four data augmentation strategies for interference signals are used in data-augmentation-based (DA-based) contrastive learning.In the second phase, the original dataset's k-nearest neighbor set (KNNset) is designed in double dimensions contrastive learning.In addition, a dynamic entropy parameter strategy is proposed.The simulation experiments of 9 types of interference signals show that random cropping is the best one of the four data augmentation strategies; the feature dimensional contrastive learning in the second phase can improve the clustering purity; the dynamic entropy parameter strategy can improve the stability of DDCC effectively.The unsupervised interference signals recognition results of DDCC and five other deep clustering algorithms show that the clustering performance of DDCC is superior to other algorithms.In particular, the clustering purity of our method is above 92%, SCAN's is 81%, and the other three methods' are below 71% when jammingnoise-ratio (JNR) is -5 dB.In addition, our method is close to the supervised learning algorithm.
A General Linguistic Steganalysis Framework Using Multi-Task Learning

Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification.While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist.In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts.LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed model.In the proposed framework, convolutional neural networks (CNNs) are utilized as private base models to extract sensitive features for each steganalysis task.Besides, a shared CNN is built to capture potential interaction information and share linguistic features among all tasks.Finally, LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or non-steganographic.Experimental results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task, while average Acc, Pre, and Rec are increased by 0.5%, 1.4%, and 0.4%, respectively.More ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.
Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However, an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background, the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification (BROFDL-AASC) technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model, data pre-processing is performed at first to convert the input data into a useful format.Besides, the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine (DFRBM) model for the identification and categorization of sentiments.Furthermore, the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.
Adaptive Weighted Flow Net Algorithm for Human Activity Recognition Using Depth Learned Features

Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection, modelling of individual frame characteristics, and communication among them. Moreover, this work focuses on finding out the human actions from various video resolutions, invariant human poses, and nearness of multi objects. First, we identify the key and essential frames of each activity using histogram differences. Secondly, Discrete Wavelet Transform (DWT) is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space. Finally, an Adaptive Weighted Flow Net (AWFN) algorithm is proposed in this work for effective video activity recognition. Moreover, the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group (VGG-16) convolution neural networks for making performance comparisons. This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy. The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51. From these investigations, it is proved that the proposed AWFN approach gives higher detection accuracy of 96%. It is approximately 0.3% to 7.88% of higher accuracy than state-of-art methods.
A comparative analysis of quality of holistic education for pre-primary and primary education in Europe: A comparative review

The present study tries to compare the quality of holistic education for Pre-primary and Primary Education in Europe in terms of four indicators on the quality of school education are selected in this study.These indicators include Education Systems (Schooling System, Home Education, Curriculum, and Education Expenditure per Student), Resources and Structures (Education and Training of Teachers, and Participation in Pre-primary Education), Monitoring of School Education (Evaluation and Parent Participation), as well as Attainment (Reading, Science, Mathematics, and ICT).It needs to be noted that this study explains the educational context in Europe and Czech in each indicator.According to European Commission (2000), the selection criteria of these indicators is by the working committee of national experts give a full series of information, which draws a picture of quality in European schools.Based on the commission reports, the presentation of these indicators help countries to look outwards rather than inwards in order to examine their performance in comparison with other countries.Besides, Europe includes a mixture of different cultures and histories, form a union, have common problems and follow many common goals.Based on reports of OECD (2019), systematically comparing education indicators across countries give information regarding the state of education to policy makers, educators, students, parents and the public.
Aligning Software Architecture Training with Software Industry Requirements

The activities of software design, documenting, and evaluating the structure of software systems, referred to as Software Architecture, have been increasingly getting significant attention in industries. This situation is because of the explicit and prominent role assigned to quality attributes while developing software systems. By considering the high relevance of Software Architecture to industry, many academic institutes have introduced a course on Software Architecture as a part of undergraduate programs in Software Engineering. However, teachers offering this course face numerous challenges. Some of these challenges stem from how software architecture is practised in industries and others from teaching them in an academic setting. This paper describes an experience of designing a software architecture course that aligns the competencies expected from professional software architects with teaching practices imparting those competencies. Such an alignment is necessary to improve the employability of graduate students, make their progression from academic institutes to industries an effortless one, and for better learning outcomes. In the absence of such alignment, fresh graduates need to re-train, leading to training costs and delayed recruitment of fresh graduates by their prospective employers. The experience reports recurring challenges observed by earlier researchers, strategies to address them and our experience in implementing those strategies. The teaching strategies suggested are potentially helpful and practical, especially to less-experienced instructors teaching a course on Software Architecture.
Loss-Driven Rapid Warning Methodology for Seismic Risk Mitigation of a Target Railway Infrastructure

Due to historical, geographical and economic conditions, the population of the Italian peninsula live in zones of intermediate and high seismic hazard. Nevertheless, Italy has a railway network that is deployed throughout its national territory as customary in developed countries. This network plays a fundamental role in the Italian economy and contributes to the resilience of the big cities to seismic events. In this direction, an active strategy for seismic risk mitigation is achieved with the use of information systems, which alert the occurrence of a seismic event and, in some cases, estimate the state of damage, at local or territorial scale, and the economic loss (e.g. early earthquake warning, rapid response system, aftershock warning system). However, the seismic hazard scenario of the Italian country, with most of active faults along its central area of the peninsula, makes it difficult to implement a comprehensive seismic early warning system. In response to this situation, this study proposes a Railway Rapid Warning System (RRWS) for a railway viaduct of the Campania region of Italy. The case study viaduct is approximately 1 km long connecting Quarto Centro and Quarto stations of the Circumflegrea line. The decisional phase of the protocol is based on an economic loss minimization criterion in order to prevent both missing and false alarm states as a main requirement to the new generation of information systems. The minimization consists of a multi-cost analysis combining structural, environmental, urban context and other aspects associated with risk mitigation actions. From the point of view of structural vulnerability, the protocol uses dynamic analysis and kinematic analysis based on the viaduct elements. It is important to mention that the end user of the warning protocol is the institution in charge of the management of the viaduct, who receives the alarm from a web page, e-mail and SMS. Additionally, the alarm describes the intensity measure of the earthquake and, above all, immediately recommends a set of seismic risk mitigation actions specifically targeted on actual conditions of the reference structure.
A Trusted Remote Data Trading Scheme in Hybrid SDN for Intelligent Internet of Things

Intelligent internet of things (IIoTs) have these features: heterogeneous network patterns, significant differences in devices, dynamic variability of network topologies, etc. In the complex security situation, it is necessary to reject data from untrusted devices to guarantee the security data trading of IIoTs. In this paper, we focus on the trustworthiness and authentication in a hybrid SDN scenario of IIoTs. For the process of the trusted judgment, we firstly implement the standardized model for the nodes with the device attributes, network states, and operation behaviors. Based on the standardized model, we propose feature evaluation functions in SDN and IP domains, respectively, to calculate the intradomain node trust values to achieve the trusted judgment. To consider the demand for secure data trading for cross-domain devices, we propose a remote data trading scheme in which the data transmitter signs the node identity and its trust value by a group signature and the data receiver verifies the signature. The group signature is not only to protect the privacy of the group members but also to support the dynamic accession and revocation of group members, so it is more suitable for IIoTs where the nodes frequently access/exit. The security is proved under the standard model. We conduct the simulation experiments to evaluate the correctness of the trusted judgment mechanism. The evaluation shows that the scheme has lower computational cost and the higher efficiency of the group signature scheme.
Quantum simulation of the generalized-entangled quantum eraser and the related complete complementarity relations

We utilize IBM's quantum computers to perform a full quantum simulation of the optical quantum eraser (QE) utilizing a Mach-Zehnder interferometer with a variable partially-polarizing beam splitter (VPPBS) at the input. The use of the VPPBS motivates us to introduce the entangled quantum eraser, for which the path information is erased using a Bell-basis measurement. We also investigate the behavior of the wave aspect, i.e., the quantum coherence, as well as the particle character, represented by the predictability and entanglement, as delineated in complete complementarity relations (CCRs). As we show in this article, the utilization of the VPPBS uncover interesting aspects of the QE and CCRs. For instance, we can recover the full wave-behavior by the erasure procedure even when we have only partial knowledge about the path through entanglement.
Retracted: An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

[This retracts the article DOI: 10.1155/2022/8467349.].