Reviewerly/exHarmony huggingface dataset:
Transforming Text Generation in NLP: Deep Learning with GPT Models and 2023 Twitter Corpus Using Transformer Architecture

This paper presents the design, implementation, and evaluation of a Transformer-based Generative Pre-trained Transformer (GPT) model tailored for character-level text generation. Leveraging the robust architecture of the Transformer, the model has been trained on a corpus sourced from social media text data, with the aim of exploring the intricacies of language patterns within a condensed and informal text setting. Key aspects of the model include a multi-head self-attention mechanism with a custom head configuration, positional embeddings, and layer normalization to promote stability in learning. It operates with a defined set of hyperparameters: a batch size of 32, a block size of 128, 200 iterations, a learning rate of 3e-4, and employs 4 attention heads across 4 layers with an embedding dimension of 384. The model has been optimized using the AdamW optimizer and includes regularization through dropout to prevent overfitting.Through a series of training iterations, the model demonstrates a converging behavior in loss metrics, indicating effective learning, and showcases the capacity to generate coherent text sequences post-training. Training and validation losses have been reported, revealing the nuances in model performance and generalization capabilities. The generated text samples postulate the model's potential in capturing the contextual flow of the dataset. This study further plots the loss curves, visually representing the training dynamics and convergence patterns. The final model, encapsulated within a PyTorch framework, presents a step forward in the realm of neural text generation, contributing to the ongoing advancements in language modeling and its applications in understanding and generating human-like text.
Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs

Online Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity.&#x0D; This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods.&#x0D;
The Use of Code Switching in Efl Classroom

This article discusses code-switching used by teachers during the teaching and learning of English as a foreign language in the classroom. This is the result of a case study at a junior high school in Lembata, East Nusa Tenggara which aims to reveal the types and reasons for code-switching used by teachers in teaching. Data was obtained by observation and interviews with three English teachers who teach in six classes. The research results show that teachers use three types of code-switching, namely inter-sentential, intra-sentential, tag code-switching, and inter-participant code-switching. The use of these types depends on the teaching and learning situation in the classroom. The use of code-switching is based on the reason that it is used as a translation, to increase vocabulary, explain difficult words or topics, change topics, clarify subjects, avoid boring situations, and provide information. The use of code-switching helps teachers to teach and students to learn English, especially for new learners
Impact of Using Non-Verbal Communication on Studentsâ Learning at Secondary Level in Fazaia Schools System

Various researchers and educationists are of the view that effective use of teachers' non-verbal expressions has a positive effect on students' learning. The objectives of the study were to find out teachers' and students' perceptions towards the use of non-verbal expressions for effective English language learning and how helpful it is in making this process more productive. In order to achieve the desired objectives, a descriptive research method was chosen where data was collected through quantitative and qualitative methods. Moreover, data was analyzed through statistical and thematic analysis. Quantitative data was collected through questionnaires whereas qualitative data through classroom observations which were analyzed through SPSS and thematic analysis respectively. The analysis leads to the findings that both students and teachers have a positive perception and it is helpful for making the pedagogical process more productive. Both were of the opinion that efficient use of non-verbal communication leads to effective English language learning.
Combating Cyber Sextortion Crimes in International Law and National Legislation

Cyber sextortion, the malicious act of coercing individuals into providing sexually explicit content through online means, has emerged as a pervasive threat in the digital age. This article explores the challenges of combating cyber sextortion crimes through the lens of international law and national legislation. Analyzing the existing legal frameworks, jurisdictional complexities, and gaps in enforcement, the article proposes comprehensive strategies to address and mitigate the impact of these crimes on individuals and society. By examining recent case studies and legal precedents, it sheds light on the evolving nature of cyber sextortion and emphasizes the need for collaborative efforts at both international and national levels.
Development and Implementation of Educational Games as Science Learning Media

Background: Science learning using computer-based learning multimedia can increase student learning motivation. One of them is by using digital educational games. However, the use of computer-based science learning media is still limited. This research aims to develop digital educational games as a learning medium on the classification of living things. Methods: Learning media development refers to the ADDIE model (Analyze, Design, Development, Implementation and Evaluation). Results: After a series of tests, the product is considered "good" and suitable for use in learning. Conclusions: Digital Education Games show that the media developed successfully increases student learning motivation.
An Effort to Improve Visual Information through Creating a Sign System in the Watu Gong Tlogomas Cultural Heritage Site Area

Background: Cultural heritage preservation is carried out to improve the community's welfare, especially those who want to learn or understand cultural heritage. One way cultural heritage conservation is carried out is by providing researchers with supporting information and communication facilities. This activity aims to design and create a sign system to improve visual information on the Watu Gong Tlogomas cultural heritage site. Method: This activity is in collaboration with the management of the Watu Gong Tlogomas Cultural Heritage site. The method used is design thinking, starting from understanding the problem, determining the problem, designing a solution concept, creating the work, and testing. Results: The main media designed and created was a sign system in the Watu Gong area, which consisted of identification signs, directional signs, information signs, and attention signs in the form of GSM (Graphic standard manual). Apart from the media sign system as the main media, several supporting media (uniforms, map brochures, lanyards, stationary) support the strengthening of visual information. Conclusion: The sign system's design has provided convenience for visitors in and outside the Watu Gong site area.
AESN: A Database of Human Eyes, Noses, and Mouths for Person Identification or Facial Expression Recognition

In this paper, we have constructed AESN, a database for regions of human faces. AESN is an improved dataset of human eyes, noses, and mouths that are clipped from facial photos. It was constructed to identify people, in addition to recognizing expressions of individuals. This database was collected from several resources, some from the Internet, and others taken by our cameras. We have got it through several types of multimedia, either directly from pictures or by extracting frames from videos. The purpose for needing a dataset for eyes was due to the emergence of the Corona pandemic, which obligated people to wear masks. In our work, we need AESN during developing a monitoring system to increase their ability to discover the identity of people through the eyes area only. It was considered a great challenge due to the loss of a large part of the face. We have accomplished promising results when using AESN images in our studies. In addition to the eyes, the remaining parts of the face (nose and mouth) have many interests due to the diversity of the shape of the nose, which mainly serves medical applications and the gestures that occur in the mouth as a result of emotional reflections that help us clarify the emotional state of the person, as well as helping researchers in finding biometrics features for recognition systems.
A Comprehensive Survey on Different Machine Learning Approaches for Breast Cancer Prediction based on Medical Imaging Modalities and Microarray Gene Expression.

Cancer is a complex global health problem that causes a high death rate. Breast cancer (BC) is the second most common death-causing disease in women worldwide. BC develops in the cells of the ducts or lobules of the glandular tissue when breast cells become uncontrollably proliferative. It can be controlled if diagnosed early enough. There are many techniques used to diagnose or classify BC. Machine learning (ML) has a significant effect on BC classification. This article provides a comparative study of different ML approaches for BC prediction based on medical imaging and microarray gene expression (MGE) data. DT, KNN, RF, SVM, Naïve Bayes, ANN, etc. perform much better in their respective fields. Another method named ensemble, incorporates more than one single classifier to solve the same problem. The study shows how ML with supervised, unsupervised, and ensemble learning might help with BC prognosis. This paper observes ensemble methods provide better performance than a single classifier. Finally, a comprehensive review of various imaging modalities and microarray gene expression, different datasets, performance metrics and outcomes, challenges, and prospective research directions are provided for the new researchers in this fast-growing field.
A Profound Multitask System for Gender Identification face recognition, Confront Discovery, Point of interest Localization, and Head Position Estimation Hyperface

Machine learning is a technology that has risen in its usage and popularity in the last few years. A huge number of people from around the world are learning this technology and putting the knowledge to various use. Machine learning algorithms are capable of learning from the provided data with high accuracy. Even though a significant amount of research has been conducted on face recognition, the integrated model of face recognition, landmark localization, head posture estimation, and gender identification that is capable of high accuracy and speed has not yet been investigated. As a result, we have developed a face recognition system that can make predictions about photos that are comparable to those made by humans. The principal component analysis PCA and the SVM were used here to accomplish facial recognition. In feature extraction, to reduce the dimensionality of large datasets, principal component analysis is performed. After the data have been preprocessed, they are entered into the SVM classifier to be used for image classification. The study of this is done via visualization, and it is used to measure the effectiveness of the model. This face recognition algorithm has an accuracy of at least 80% when it comes to classifying people's portraits. The findings of the experiments show that the suggested technique can successfully identify faces since it employs a feature-based algorithm that combines PCA classification and SVM detection.
Context-Preserving Sentiment Classification Using Bi-TCN And BI-GRU with Multi-Head Self-Attention

In natural language processing, sentiment classification is the recently used topic. Specifically, the objective of the sentiment analysis is to categorise the polarity expressed on the sentence's target. However, there are some researches for classifying the polarity of the target which outperforms well in their way. Yet, there are some limitations, such as apparent and in-apparent issues, gradient problems, etc., to overcome these issues the context-preserving sentiment classification using BI-TCN (Bidirectional Temporal Convolutional network) and BI-GRU (Bidirectional Gated Recurrent Unit) with Multi-head self-attention is proposed to extracts both the local dependent and global dependent information from the sentence, then it will incrementally extract the supervision information of the target to train the model. Formerly, the model is tested and trained using four datasets and the performance is compared with four existing methods, its accuracy is evaluated using the F1-score, precision, recall, specificity, and MCC (Matthews Correlation Coefficient). Consequently, the proposed approach provides the best accuracy level of 98%..
Study on Deep Learning Techniques for Finding Suspicious Violence Detection in a Video Surveillance

Detecting suspicious visual objects is essential to applying automatic violence detection (AVD) in video surveillance. Continuous monitoring of objects or any unusual things is a tedious task. Learning about video surveillance is an emerging research problem in AVD applications. Deep learning is an intelligent and trustworthy technique for detecting or classifying suspicious data objects. It classifies suspicious video frames by modeling specific categories of videos. The current deep models convolutional neural network (CNN), convolutional long-term and short-term memory (ConvLSTM), AlexNet, VGG-16, MobileNet, and GoogleNet, are wildly succeeded in real-time violence detection with the input of video clips. This paper presents the findings of experimental studies for deep models using classification measures to demonstrate the models' efficacy for our AVD application. Benchmarked violence (V), non-violence (NV), and weapon violence (WV) video datasets are used in the experiment to describe the model's performance while classifying suspicious videos for public safety.
Stylometric Analysis of Genre in Hindi Literature

The field of stylometry is the quantitative study of writing style. It is used for analyzing various patterns in writing text such as vocabulary, parts of speech, punctuations etc. In this paper a detailed analysis of various techniques of Stylometry analysis is being done and some of their applications are discussed. In this research, stylometric analysis of Genre in hindi literature is being done. , we have used a dataset of four Hindi stories each from four different famous Indian authors. Features are extracted from the preprocessed text data using the TF-IDF (Term Frequency-Inverse Document Frequency) technique. PCA is applied for dimensionality reduction. The results were good, effectively creating different groups that stand for different literary motifs or styles within the dataset.
Automated Leopard Alert And Reporting Mechanism Using Deep Learning

Today, rapid infrastructure development is taking place in major metropolitan cities, but unfortunately, this progress often involves the destruction of forest reserves, leaving wild animals homeless. The resulting environmental invasion forces these animals to venture into the cities, posing threats to citizens. In Mumbai, there have been numerous sightings of leopards and other wild animals near forested areas. Leopards have been known to attack street dogs, people, and vehicles, making it necessary to work on this problem. This paper suggests the utilization of deep learning models and object detection techniques to detect leopards and other potential threats. By integrating this technology with security applications, citizens can be made aware of the existence of wild animals in their vicinity. This research primarily focuses on addressing the concern of leopard sightings in Mumbai. The objective is to automate leopard detection and reporting using an object detection algorithm. In the proposed system, images of leopards are collected from an existing dataset available on Roboflow, comprising a total of 1000 samples. The proposed model's performance is evaluated using Mean Average Precision (mAP) &amp; detection speed. The proposed method achieves an impressive mAP of 95.9% at a speed of 37 frames per second.
IoT Applications, Platforms, Systems, And Framework based on Blockchain

The Internet of Things (IoT) has lately evolved as a new technology capable of providing real-time and cutting-edge sensing capabilities to numerous industries such as healthcare, agriculture, smart cities, smart homes, and supply chain. Because of this technology's inherent promise, it has already seen exponential growth in a wide range of use-cases across numerous application domains. As academics around the world continue to examine its capabilities, there is widespread consensus that in order to get the most out of this technology and fully realise its potential, IoT must be built on a flexible network architecture with strong support for security, privacy, and trust. Blockchain (BC) technology, on the other hand, has lately emerged as a breakthrough technology with the promise to give several beneficial qualities such as robustness, support for integrity, anonymity, decentralisation, and autonomous control. Several BC systems are offered, which may be appropriate for various use-cases, including IoT applications. As a result, the integration of IoT with BC technology is seen as a potential solution to some critical concerns. To do this, a good grasp of the requirements of various IoT applications and the viability of a BC platform for a specific application satisfying its underlying requirements is required. This project explains many ways such as the gateway process and sensor device. By addressing the present blockchain concerns, IoT may enable a variety of security services, all of which are described in detail. Various authors present some common facts on the use of blockchain in IoT, which aids in a thorough understanding of the concept. Blockchain improves security and privacy in IoT platforms. In this project, an extra immutable ledger is created using all of the resources and information mentioned in the existing procedure. [1].
Performance Evaluation of Deep Learning Autoencoder in Single and Multi-Carrier Systems

An entire system of Single Carrier Communication and Orthogonal Frequency Division Multiplexing is modelled using an autoencoder. The model employs Deep Neural Networks (DNNs) as the transmitter and receiver, responsible for tasks such as encoding, modulation, demodulation, and decoding. The effectiveness of this approach is demonstrated by its ability to outperform traditional communication systems in real-world scenarios that involve channel and interference effects, as measured by the Block Error Rate. AI-enabled wireless systems can overcome limitations of traditional communication systems by learning from wireless spectrum data and optimizing performance for new wireless applications. The aim of this paper is to examine how autoencoder-based deep learning can enhance the performance of a communication system that employs Single Carrier and OFDM. The architecture effectively addresses channel impairments and improves overall performance. The simulation results suggest that even when the autoencoder's channel layer is affected by impairments, autoencoders still outperform traditional communication systems in terms of BLER performance.
Comparison of the Execution Step Strategies on Scheduling Data-intensive Workflows on IaaS Cloud Platforms

The IaaS platforms of the Cloud hold promise for executing parallel applications, particularly data-intensive scientific workflows. An important challenge for users of these platforms executing scientific workflows is to strike the right trade-off between the execution time of the scientific workflow and the cost of using the platform. In a previous article, we proposed an efficient approach that assists the user in finding this compromise. This approach requires an algorithm aimed at minimizing the execution time of the workflow once the platform configuration is set. In this article, we compare two different strategies for executing a workflow after its offline scheduling using an algorithm. The algorithm that we proposed in the previous study has outperform the HEFT algorithm.&#x0D; The first strategy allows some ready tasks to execute earlier than other higher-priority tasks that are ready later due to data transfer times. This strategy is justified by the fact that although our scheduling algorithm attempts to minimize data transfers between tasks running on different virtual machines, this algorithm does not include data transfer times in the planned execution dates for the various tasks of the workflow. The second strategy strictly adheres to the predetermined order among tasks scheduled on the same virtual machine.&#x0D; The results of our evaluations show that the best execution strategy depends on the characteristics of the workflow. For each evaluated workflow, our results demonstrate that our scheduling algorithm combined with the best execution strategy surpasses HEFT. The choice of the best strategy must be determined experimentally following realistic simulations, such as the ones we conduct here using the WRENCH framework, before conducting simulations to find the best compromise between cost and execution time of a workflow on an IaaS platform..