Reviewerly/exHarmony huggingface dataset:
Information Fusion Channel Pruning for Accelerating Neural Networks

Convolutional Neural Networks (CNNs) have succeeded highly in various computer vision tasks. Recently, with the growing size and complexity of CNNs, many works have used magnitude-based information to introduce structured sparsity for more efficient and energy-saving models. Unlike previous works, in this paper, we argue that the distribution information within the Batch Normalization layer (BN) indicates which channel generated by the previous Convolutional layer (Conv) is worth removing or preserving. Intuitively, there must be another feature with a similar distribution for the redundant feature. Therefore, we propose BatchNorm Prunning (BNP), which utilizes the BatchNorm layer to analyze the distribution similarity between features for better structured pruning. Through comprehensive evaluation, BNP has been demonstrated in modern CNNs on the CIFAR-10 and ImageNet datasets, achieving the balance between performance and efficiency over state-of-the-art algorithms.
Enabling Fuzzy Matching in Privacy-preserving Bilateral Task Recommendation

Privacy-preserving bilateral task recommendation is an emerging functionality in mobile crowdsensing, enabling two-sided matching for both task publishers and workers while ensuring the confidentiality of both participants’ attributes and task-matching policies. However, existing approaches rarely integrate fuzzy matching into privacy-preserving bilateral task recommendations, limiting the efficiency of two-sided matching because they can hardly find intersections between matched task publishers and matched workers. Intuitively leveraging fuzzy matching leaks whether the attributes of a participant satisfy a task recommendation policy. Motivated by this, this paper proposes PBTR, the first privacy-preserving bilateral task recommendation scheme that supports fuzzy matching while preserving matching privacy. Specifically, we leverage the Lagrange interpolation theorem-based secret sharing to enrich the matchmaking encryption technique to achieve fuzzy matching in bilateral task recommendations. To preserve matching privacy, we design a privacy-preserving matching proxy mechanism, where policy matches successfully if and only if the policies of both parties are satisfied. Formal security analysis proves the security of PBTR under the chosen-plaintext attack in the random oracle model. Experimental results demonstrate the feasibility of PBTR, which only requires several milliseconds to achieve privacy-preserving bilateral task recommendations.
ARCHITECTURAL REFERENCE MODELS USED IN PROCESS MODELING

All organizations start their activity with the desired existence as long as possible, and in case they will have notoriety on an international level or, at least, on a national level for the activity carried out, it means that the management of that organization is a successful one. Central public authorities strive to identify solutions to make their activities more efficient, not necessarily because of a strategic management policy, but, above all, because of reduced budgets, year after year, even though the specific attributions remain the same. In this context, architectures can provide some solutions for government organizations, as they have the role of rendering all processes in a conceptual structure, with the inclusion of each element that is part of the system and can help to optimize the modelling of processes, so that they can eliminate what is redundant and unnecessary within a system.
Recontextualization of the teacher’s formation in the learning of mathematic students

Continuing education is an essential part of teaching practice because the movements and advances in society change the ways of living, coexisting and acting, making it necessary for the teacher's practice to be congruent with the changes resulting from these movements. This article aims at self-regulating the training of teachers who teach mathematics based on students' learning, through teachers' reflection-action. Teachers who participated in the training studied, planned and applied the knowledge learned in the training with their students, and then discussed in the group how to self-regulate their training. The quantitative methodology was based on the analysis of pre- and post-tests that included numbers and algebra as thematic units applied to the students of the teachers in training. The analyses show that the students had better results in the post-test, allowing the teachers to reflect on the relationship between participation in the training, as well as openness to new ways of teaching, with the students' learning.
Health Information Exchange Using Blockchain

In the contemporary landscape, the demand for a distributed digital content system integrating Blockchain technology is burgeoning. Initially gaining prominence in finance with the advent of cryptocurrencies, Blockchain is now permeating diverse sectors such as IoT, Healthcare, Banking, and Supply Chain. This technology provides a decentralized framework, employing a peer-to-peer authentication mechanism to authenticate access management. This study explores the transformative potential of Blockchain in healthcare, offering a secure and tamperproof platform for storing and sharing patient data, thereby enhancing stakeholder management of healthcare information. The focus is on its application in a Health Information Exchange (HIE) system, ensuring secure and efficient access management of Electronic Health Records (EHR). These Blockchain-based solutions have the capacity to revolutionize healthcare information management, elevate patient outcomes, and foster improved stakeholder collaboration, marking a significant stride in the industry’s evolution.
Research on UAV Path Planning based on LSTM

In recent years, drones have become popular in the commercial and military fields due to their simple structure, high cost performance and easy operation. At the same time, the mission scenarios faced by drones have gradually become more complex and diverse, introducing more uncertain factors. Therefore, when facing more complex task scenarios, one of the current main strategies is to establish communication links and use neural networks to conduct path planning research to complete tasks in complex environments more efficiently. To address the challenge of cluster path planning in complex environments, we propose a new approach. During the path search phase, we take the drone’s kinematics and flight coordinate transformation into consideration to ensure that the resulting path is dynamically feasible. At the same time, we combine the LSTM neural network to memorize the flight trajectory and generate a judgment on the future path to make subsequent path planning, at the same time, the particle swarm optimization algorithm was used to optimize the results. Finally, we find that the accuracy of the proposed method is significantly improved compared with the traditional neural network method through experiments.
Recommendation of College English Education Information Based on Fuzzy

In order to better cope with the explosive growth of network teaching information more accurate extraction of user required education information. In this study, a college English teaching information recommendation system based on multiple mixed criteria fuzzy algorithm is designed. Through English educational information retrieval, database design and recommendation display application, higher recommendation information can be better extracted by combining user preferences. Through the system application test results, it can be seen that the English educational information recommendation system involved in this article can achieve a wider range of content coverage. In addition, when the number of system users increases to 500, the accuracy of system users’ discovery is stable at 95%, and the recommendation recall rate is always above 90%. The recommendation effect of education information system is good, it fully shows that the recommendation effect of the English education information system proposed in this paper is good.
Spammer detection and fake user detection using modified Naïve Bayes and decision Tree classifier

Earlier, when Online Source Networks (OSNs) were not popular among most of the people the fraud or spamming activities used to happen through fake calls or SMSs. As the Online Source Networks have become popular platforms for people to share their views, ideas, news, information etc. People’s involvement in these platforms are increasing over these platforms. We know that if some people are using these platforms to share true information or ideas, then there must be some people using these platforms to spread fake news, information or malicious messages. Commonly we can divide these two users in two category, Non-Spammers and Spammers. Non-Spammers will be linked to OSNs for true information while Spammers will be linked to fake content, URL based malicious activities, spam in trending topics and fake identification. Twitter is the main example of widely used OSN platform where most of the Legitimate as well as Spammers are present, unreasonable number of spams on tweets, trending topics disturbing the real source. In this research work a integrated approach of three machine learning algorithms namely Forest Research, Decision Trees and Naïve Bayes is being proposed. Results will show the accuracy of combinational approach is more than the classical approach.
Automated Speech Correction Assistive Technology for Malayalam Articulation Errors

Individuals with speech impairments confront substantial challenges affecting their communication capabilities and overall quality of life. Among these, articulation errors pose a significant barrier to effective communication, often resulting in frustration and social isolation. The articulation process entails the modification of sound waves generated by the airstream, phonation, and oral-nasal processes [Glossary of Linguistic Terms]. Speech production occurs in the oral cavity, involving key organs such as the tongue, upper and lower teeth, upper and lower lips, and elements of the roof of the oral cavity (e.g., alveolar ridge, palate, and velum). These organs can act as either active or passive articulators. Speech sounds are differentiated based on their place and manner of articulation. Articulation errors arise from disorders affecting these specific attributes. The present paper introduces an assistive technology to enhance Malayalam speech impacted by articulation errors. Malayalam is primarily spoken in southern India and has its phonological complexities. The technology employs a rule-based system for real-time analysis of the speaker's speech to offer targeted assistance. The system has been trained on a dataset of Malayalam speech samples with articulation errors collected from an individual diagnosed with cerebral palsy. The interface is user-friendly and web-accessible. Experimental evaluations corroborate the system's efficacy, evidenced by a Word Error Rate (WER) of 8.2% and a Signal-to-Distortion Ratio (SDR) of 20.6. The proposed solution aims to improve the speech capabilities of individuals with articulation errors, bolstering their communication skills and self-assurance in social interactions.
Blockchain Technology to Improve Cybersecurity: Opportunities and Challenges

Blockchain technology has gained widespread acceptance in numerous fields, most notably finance, thanks to the use of cryptocurrencies. The technology, on the other hand, can be used in cybersecurity. In order to enhance business processes and transform business operations, BCT is investigating cybersecurity issues and opportunities. This scoping review study was conducted with the intention of determining the scope of the current research and classifying various kinds of cybersecurity opportunities and challenges in BCT. A combination of phrases was used to search the Research Gate, IEEE, Science Direct, Elsevier, and ABI/INFORM Collection (Pro Quest) databases. After a thorough screening, 55 research studies were determined to be relevant. According to our findings, the majority of publications discuss cybersecurity issues in BCT as a whole, whereas only a small number of papers discuss BCT's opportunities and challenges in cybersecurity in relation to specific industry sectors. In addition, previous research only briefly examined BCT's cybersecurity opportunities and challenges and solutions. Future study directions for researchers were suggested based on gap identification.The primary objective of this scoping review was to ascertain the current survey's size, scope, and gaps regarding BCT cybersecurity challenges.
Analyzing the Performance of Machine Learning and Deep Learning Models in Detecting Cyberbullying Comments

With the proliferation of social media platforms, there has been a concerning escalation in cyberbullying and abusive language. Detecting such toxic comments in large volumes of user-generated data presents challenges. This research conducts a comparative analysis to evaluate the impact of natural language processing techniques on identifying cyberbullying. Using a dataset of 1,59,000 comments labelled across seven classes of toxicity, conventional machine learning and deep learning models are benchmarked across performance metrics. The study aims to quantify algorithmic capabilities to accurately classify bullying comments. The study finds that Support Vectors with a linear kernel surpass Logistic Regression in accurately identifying toxic comments. For deep learning techniques, the transformer models (BERT and Distil-BERT) deliver the highest performance among neural network architectures tested. The empirical evaluation provides insights into leveraging computational linguistics for automating the detection of online bullying at scale.
Deep and Transfer Learning-based Research Article Recommendation System for Healthcare Services

To address the growing volume of confidential biomedical data in the healthcare sector, we propose an inventive Deep Learning (DL) approach for suggesting relevant articles, tailored specifically for the healthcare field. The proposed approach addresses the challenge of information curation by presenting a personalized assistant that aids both patients and medical professionals in navigating the extensive scholarly landscape. In situations where individuals face limitations in accessing healthcare services, such as remote or restricted circumstances, our approach demonstrates significant value in providing direct medical consultation. By acquiring insights into symptoms and potential diseases through medical articles, it provides a foundation for informed decision making. It harnesses advanced pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), and eXtreme Learning Machine Network (XLNet). These models undergo rigorous training using diverse medical article datasets, extracting nuanced semantics through tokenization, embeddings, self-attention mechanisms, and transformations. The system's reliability is enhanced by conducting a thorough assessment of optimizers, including Root Mean Square Propagation (RMSprop), AdamW, and Stochastic Gradient Descent (SGD). Finally, the proposed approach is evaluated against various matrices such as accuracy and loss curves, F1, recall, and precision comparison.
An Approach for Ranking Cyber Crime using Fuzzy AHP and Fuzzy TOPSIS

Cybercrime is a major obstacle in both the IT community and the law community. In this paper, we are trying to rank different types of cybercrime using Fuzzy AHP and Fuzzy TOPSIS. Not all types of cybercrime cause financial loss to an individual. In this paper, we have considered eleven different types of cybercrime all these criteria were selected on the basis of the extent of financial losses incurred upon a person. We have used Fuzzy AHP to rank the criteria and Fuzzy TOPSIS was mainly used to see whether there were any financial incurred by a person. The main aim of this paper is to rank the criteria that can help in future research in cybercrime
Development of 21st-Century Skills and Competencies in High School Students Through the Interactive E-Books Creation

Currently, the need to prepare students for the challenges of the 21st-Century is being recognized, referred to as Education 4.0. This paradigm aims to redefine the education model by fostering the development of 21st-Century skills and competencies, such as problem-solving, creativity, collaboration, communication, and learning to learn. Therefore, this article explores the uses of the Teacher Assistance Educational Process (TAEP4.0) in a real educational scenario. TAEP4.0 is a student-centered process inspired by Education 4.0, which assists teachers in creating projects in the Informatics Lab. The TAEP4.0 process was implemented through a case study focused on fostering 21st-Century skills and competencies among High School students, using the Book Creator platform for interactive e-book creation projects. TAEP4.0 consists of a series of steps that include suggested activities, tools, and support materials. However, teachers have the flexibility to adapt the TAEP4.0 process, exercising their creativity and making decisions regarding project elements, such as the choice of Information and Communication Technology (ICTs) for project creation and monitoring, with the aim of promoting skills and competencies. In this case study, five classes of two High School teachers participated, involving a total of 189 students in the projects. The data collected were analyzed both quantitatively and qualitatively. During the projects, students engaged in activities and reported difficulties, primarily related to communication and teamwork. This feedback enables teachers to gain insight into students' challenges in applying the necessary skills to produce a project. Such evidence can assist teachers in addressing these issues in the future, allowing for project reuse and the creation of new projects that focus on improving specific skills and competencies.
Secure Data Transmission for Communication Systems Using IoT

E-/m-healthcare, or electronic/mobile healthcare, has profited greatly from the widespread use of the Internet of Things (IoT), cloud computing, and wireless body-area networks (WBANs). However, there are still a number of challenges that are impeding the development of e-/m-Healthcare, including as concerns about the privacy and security of patient information. To address these concerns, a framework for healthcare systems (HES) is developed. Through a gateway, it collects medical data from WBAN s, sends it through a large wireless sensor network architecture, and makes it available in WPANs. HES also employs the Groups of Send-Receive Model (GSRM) method for key distribution, the Homomorphic Encryption Based on Matrix (HEBM) scheme for privacy protection, and an expert system that can automatically analyze the encrypted medical data and present the findings. HES is compared to other systems and approaches in terms of security, privacy, and better performance, both theoretically and empirically. Finally, the feasibility of implementing the HES prototype is analyzed.
Map Reduce Based K-Means Genetic and C- Means Fuzzy Clustering Formation for Community Detection and Community Validation

The networks that are constantly a part of the Cloud infrastructure exist in the real world with different representations, further these networks exchange information with each other. Nowadays, the real-time data stored in Terabytes and Petrabytes must be processed by using an effective mechanism. The HDFS combined with Machine Learning Techniques frequently provides an intelligent technique for processing the big data. For a better understanding of the network's complex structure, the real-time structure in the networks can be represented as a community structure. However, it is necessary to detect the links in a distributed heterogeneous complex network for community detection. This research study proposes a novel algorithm for community detection by employing a genetic algorithm based K-Means Unsupervised Technique and C-Means Fuzzy Clustering. The Map Reduce framework is used to provide scalability and parallelism in the proposed work. The performance analysis metrics such as accuracy and convergence are measured. The Folkes and Mallows Coefficient with Hyperparameter Tuning is used to further assess the community development. The Fuzzy C-Means Clustering Algorithm is used to define the Objective Function. The proposed method has additionally included a Map Reduce Framework and is more efficient when compared to other existing research regarding community recognition over a Cloud Based environment, based on an experimental evaluation of five real-time datasets.
Evaluating the Effectiveness of Spam Message Classification and Detection based on Deep Bi-GRU Method

More and more people in the modern information era rely on SMS because of its low cost, great mobility, and ease of usage. However, as SMS has gotten more popular, spam messages have emerged as a serious problem, with detrimental effects on not only people's daily lives but also public safety and social harmony. In light of this, research into the creation of technology for the intelligent classification of spam communications has assumed a higher priority, and spam message filtering has become an urgent issue demanding quick attention. The first three parts of the proposed method are preprocessing, feature selection, and model training. Chi-square and document frequency are employed for preprocessing. This study employs a genetic algorithm to select features. The models are then trained after feature selection with D-Bi-GRU. The proposed approach surpasses the two most common alternatives, GRU and Bi-GRU.
Smoothing Convolutional Factorizes Inception V3 Labels and Transformers for Image Feature Extraction into Text Segmentation

In the concept of computer vision, object detection in video understanding cannot provide a contextual picture in the form of a semantic description of the video/image. For this reason, an object detection and feature extraction mechanism is needed and a video and image conversion technique into text using the Inception-V3 and Transformer methods. Inception-V3 is a deep convolutional architecture that is a development model of Google-Net or Inception-V1. Improved system performance by adding additional factorization at the convolution stage to reduce existing connections or parameters without reducing the network used to extract image features with an input image size of 299 x 299 x 3 pixels. With a transformer architecture that uses a multi-head self-attention mechanism to predict words and recover words sequentially with an RNN encoder-decoder architecture. The research was carried out using 5 minute videos which produced a Tensorflow dataset of 1000 images and 5000 sentence captions. The model was evaluated with BLEU (Bilingual Evaluation Understudy), with average scores of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 obtained at 0.418, 0.367, 0.245, and 0.165 to produce predicted captions and real captions.