Reviewerly/exHarmony huggingface dataset:
Triple Extraction with Generative Technique for Constructing Weighted Knowledge Graph

Extracting relational facts from unstructured text is crucial in natural language processing used in many applications, particularly in constructing knowledge graphs. Relational facts are represented as triples in which two entities are connected through a relation. This work introduces a new and effective end-to-end method to generate triples from the input text. In the proposed method, we develop an encoder-decoder-based transformer model and warm-start both the encoder and decoder with pretrained checkpoints that are publicly accessible. These checkpoints can be taken from models such as BERT, GPT-2, and RoBERTa. Experimental results show that our method achieves better results for triple extraction on publicly available datasets (NYT and WebNLG) than the other state-of-the-art techniques. Further, the extracted triples are processed and used to build a knowledge graph. Complete control of this process allows for determining the weights of the relations (triples). The weights reflect the frequency of occurrences of facts represented by the relations and provide the degree of confidence in the facts.
Large Language Models (LLMs): Hypes and Realities

Artificial intelligence (AI) has created a lot of buzz in recent years. Using machine learning and other AI techniques several intelligent initiatives have been tested. The large language model is one of them. A large language model (LLM) normally refers to a type of AI model that is trained on vast amounts of text data to understand and generate human-like language outputs. These models are designed to capture the statistical patterns and structures present in the training data, enabling them to generate coherent and contextually relevant responses. The widely known ChatGPT is one of the LLMs which can do several tasks and answer many questions. It is trained with a huge number of data sets and a large number of parameters. In addition to ChatGPT, many other LLMs such as the Google Bard, Claude v1, Bison 001, Cohere, Falcon, and Guanaco-65B have surfaced in recent times. In this paper, we study the basic principles and features of LLMs. We go through their brief history, abilities, limitations, challenges and future prospects.
Explainability of the Effects of Non-Perturbative Data Protection in Supervised Classification

The increasing availability of online data has meant that data-driven models have been applied to more and more tasks in recent years. In some domains and/or applications, such data must be protected before they are used. Hence, one of the problems only partially addressed in the literature is to determine how the performance of Machine Learning models is affected by data protection. More important, the explainability of the results of such models as a consequence of data protection has been even less investigated to date. In this paper, we refer to this very problem by considering non-perturbative data protection, and by studying the explainability of supervised models applied to the data classification task.
Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency.
A Survey Analysis on Decentralized Land Registry System Using Blockchain and Smart Contracts

This study investigates the potential use of blockchain technology and smart contracts to construct a decentralised land registry system. It looks at the problems with traditional land registry systems and provides a technical breakdown of how to create a blockchain-based land registry system. The research also evaluates the countries that have already adopted such systems and discusses the possible adoption of a blockchain-based land register system in India. The implementation of a blockchain-based land registration system, per the research, can increase the security and efficacy of land transactions, reduce fraud and mistakes, and promote economic growth.
Decentralised Blockchain as a Service Model for Cloud Based Security

In this paper, we develop a decentralized blockchain model to increase the security of cloud Software as a Service (SaaS). The proposed architecture makes the assumption that the virtualization layer of the cloud infrastructure will be the site of new and interesting activity. In addition, it paves the way for a standard privacy scale, which will enable users and businesses to construct a higher level of confidence with third parties in order to guarantee the dependability of the data while it is being stored. This will be accomplished in order to guarantee the data dependability. The simulation is conducted in cloudsim tool to test the efficacy of the model. The results of simulation shows that the proposed method achieves higher rate of trust based blockchain security than other methods.
Graph Auto Encoders for Content-based Document Recommendation System

In the modern world,recommendation system have a good potential to improve business outcomes and to improve user experience with better accuracy combined with more generalisation to new unseen query.We propose a content-based document recommendation system which retrieves the most similar documents to the query given by the user.The embedded documents are fed into Graph Auto Encoder (GAE) and query through sparse coder to do latent representation which are finally used to give the user with perfect recommendations.The proposed method got evaluated with an average accuracy value of 93.91%.
Optimizing Book Recommendations through Machine Learning: A Collaborative Filtering and Popularity-Based Framework

The purpose of this research is to offer users individualized and varied book recommendations. The collaborative filtering component examines users’ prior interactions with books to identify users who have previously displayed similar tastes. The system suggests books based on this resemblance that the current user hasn’t yet read but that comparable users have appreciated. The popularity-based filtering, on the other hand, takes into account the system’s general appeal and reading preferences. In order to suggest popular books that are currently trending among users, it takes into account variables like the average ratings, the total amount of ratings, and recent trends. The goal of our system is to solve some drawbacks of conventional recommendation systems by merging these two filtering techniques. The “cold start” issue in collaborative filtering could lead to inaccurate recommendations for new users or books with scant historical data. On the other hand, popularity-based filtering could result in a lack of customization because it might simply suggest well-known and well-liked books. The suggested approach is made to adjust to shifting user trends and preferences over time, resulting in consistently better recommendations. Users can quickly receive personalized book recommendations, find new books to read, and enjoy an improved reading experience by incorporating the hybrid model into a website, especially in times when physical access to bookstores and libraries is constrained. Our system seeks to assist users in navigating this large sea of possibilities and discovering the ideal books that resonate with their interests and preferences in light of the expanding availability of books online.
Prediction of Movie Categories Using Randomized Sequences with Machine Learning

Prediction of movie categories from random sequences is tricky and challenging in several sense and has hot wide attention among research community. The study presented here suggests a novel method for categorizing movies based on the order of their frame sequences. The suggested method creates numerous sequences from the movie’s frames using a randomized sequence generator, and then utilizes machine learning techniques to forecast the movie’s categories based on these sequences. The method is tested against two movie datasets, and the findings reveal that it performs better in terms of efficiency and accuracy than a number of standard methods. The proposed method can be utilized as a promising tool for predicting movie categories, and it can have applications in content-based movie recommendation systems, according to the paper’s conclusion. More specifically, the suggested method creates randomised sequences by subjecting the movie’s frames to a variety of transformations, including random cropping, flipping, and colour jittering. Overall, using randomised sequences and machine learning algorithms, the research presents a fresh and successful method for predicting movie categories. The suggested method may be used in a variety of fields, including video summarization, content-based video retrieval, and movie recommendation systems. The proposed model will generate the result high accuracy of 92% which is more than another proposed models.
People Counting Based on YOLO

People counting is an important undertaking with many uses in areas including crowd control, transit, and retail. The final purpose of this project is to create a system that uses video footage to precisely count the number of persons entering and exiting a specified region. The method tracks and detects people in the video using object detection techniques, notably the YOLO (You Only Look Once) algorithm. There are various crucial phases in the project. In order to identify persons in the video frames, the YOLO model is first used. This model has been trained on a sizable dataset and comes with pre-trained weights. For each individual who is detected, the model returns bounding box coordinates, confidence scores, and class labels. The algorithm calculates the number of persons entering and departing the area of interest by comparing the quantity of individuals recognised in the most recent and earlier frames. Based on the change in the count, it is decided whether to go inward or outward. In addition to giving current updates on the population. And their movement status, the created system also combines visual feedback by presenting video frames with bounding boxes around detected individuals. Applications for this data include occupancy management, crowd management, and security surveillance. Experiments with various video datasets serve to confirm the system’s efficacy and accuracy. The results show that even under challenging and busy conditions, the system can reliably measure the number of persons entering and exiting. Overall, this study provides a reliable method for object-based video counting of individuals.
Word Sense Disambiguation Using Supervised Learning

Word sense disambiguation (WSD) holds significant importance within the field of natural language processing which involves the process of determining the accurate interpretation or meaning of a word within a specific context. Supervised learning has been widely used for WSD, where a labeled dataset is used to train a model to predict the sense of an ambiguous word in context. However, the accuracy of the WSD system can be improved by combining multiple approaches. This project proposes a novel approach that combines the mutual information(MI) statistical method and neural network classifier to improve the accuracy of WSD for the English language. Our system leverages the complementary strengths of these two approaches, with mutual information identifying the most informative features for distinguishing between different word senses and neural networks learning complex non-linear relationships between input features and target labels. We evaluate our approach on the Senseva1-2 benchmark corpus and demonstrate that our method outperforms existing state-of-the-art WSD methods. Our approach can potentially improve the accuracy of WSD systems. It can be applied to various NLP tasks where word sense disambiguation is required, such as machine translation, text classification, and information retrieval.
The Detection of E-Commerce Manipulated Reviews Using GPT-4

In recent years, the exponential growth of e-commerce has transformed consumer purchasing behavior, with online reviews playing a crucial role in shaping buying decisions. Unfortunately, the prevalence of manipulated reviews has become a significant challenge, undermining the integrity of consumer feedback and eroding trust in e-commerce platforms. This research paper focuses on developing an advanced solution to address the issue of manipulated reviews using the innovative language model, GPT-4. The primary objective of this study is to investigate the effectiveness of GPT-4 in identifying and flagging manipulated reviews within the context of e-commerce platforms. GPT-4 is a state-of-the-art language model with superior natural language processing capabilities, making it an ideal candidate for automated review analysis. The research methodology encompasses a large-scale data collection process, where diverse e-commerce reviews are gathered from various platforms. To simulate real-world scenarios, manipulated reviews are artificially injected into the dataset, representing different degrees of sophistication. The dataset is then annotated by expert reviewers to establish ground truth labels for comparison. Next, GPT-4 is fine-tuned using transfer learning to specialize in detecting manipulated reviews. The fine-tuning process involves exposing the model to both genuine and manipulated review samples, allowing it to learn patterns and features indicative of manipulation. The fine-tuned model's performance is then evaluated using various metrics, including precision, recall, F1 score, and accuracy, against the ground truth dataset. The results of the experiment demonstrate the efficacy of GPT-4 in distinguishing between authentic and manipulated reviews. GPT-4 showcases remarkable accuracy and robustness in detecting increasingly sophisticated manipulation techniques, outperforming previous iterations of language models and traditional detection methods. Furthermore, the research delves into the characteristics of manipulated reviews that render them distinguishable from genuine reviews. These insights aid in understanding the vulnerabilities of e-commerce platforms to manipulation and offer valuable information for future research and platform enhancement efforts. The implications of this study are significant for e-commerce businesses, consumers, and platform administrators alike. By leveraging GPT-4's detection capabilities, e-commerce platforms can implement more robust review validation systems, ensuring the authenticity and reliability of consumer feedback. This, in turn, fosters greater trust among consumers, leading to informed purchasing decisions and improved overall user experience. In conclusion, this research paper contributes to the existing body of knowledge by showcasing the potential of GPT-4 in combatting manipulated reviews and strengthening the integrity of e-commerce platforms. With the rise of AI-driven technologies, the proposed approach presents a promising step towards a more transparent and trustworthy e-commerce ecosystem.
Vehicle Detection Using CNN and YOLOv3

This research paper aims to develop a vehicle detection system using a YOLOv3 deep learning frameworks. The main objective of the study is to detect and localize the vehicles from the images or a video streams. The proposed algorithm is powerful enough to do the above task using the CNN(Convolutional Neural Networks) along with the YOLOv3(You Only Look Once) architecture to acheive the maximum possible outcome showcasing the results. This methodology involves us to train the YOLOv3 model on custom datasets divided into different types containing different environmental conditions. And another approach is to apply CNN based algorithm to greatly acquire the features accurately from the input images and also differentiating between the size, shape, wheels and orientation. Through extensive experimentation and evaluation on benchmark datasets, the effectiveness of the developed vehicle detection system is demonstrated. The results showcase a high detection accuracy and real-time performance, making it suitable for applications such as traffic monitoring, autonomous driving, and surveillance. The results of this research contribute to the field of computer vision and object detection by providing an accurate and efficient solution for the vehicle detection.
Investigating the Security Trade-Offs of 5G Deployment using Block-chain based Encryption Model

The increasing demand for mobile data transfer has been driving major changes in communication technology, such as the emergence of 5G networks. The 5G deployment, however, brings with it trade-offs in terms of security. Many researchers have proposed employing block-chain based encryption models to improve the security and privacy of 5G networks. This paper examines the security trade-offs of 5G deployment using such models. It identifies both the potential security benefits that blockchain-based encryption can offer and the trade-offs in terms of scalability, performance, and security risks that must be considered. Possible implementation and deployment scenarios for such block-chain based encryption models are discussed. This paper concludes that block-chain based encryption is a promising approach to enhancing the security of 5G networks, but that further research into scalability and performance issues must be completed before it can be deployed in a production 5G network.
Research on the Integration Path of Computer-Aided Technology and Higher Vocational English Teaching

Computers have entered thousands of households as a result of the ongoing development of high technology and the progressive rise in people’s living standards. As a result, computers have become an essential component of people’s lives and a must for students. The education sector has once again started off a wave of teaching reform, supporting the flipped classroom teaching style and the merging of online and offline teaching in response to the rapid expansion of the information age and the full support of computers. Many different teaching platforms have grown in response to the requirements of the times and have developed into the fundamental tools for education and instruction in higher education institutions such as colleges and universities. The current way of instruction has experienced a radical transformation in comparison to the more conventional approaches of education. English instruction in higher vocational schools is not, of course, an exception. Teaching English with the assistance of computers in higher vocational colleges has the potential to both boost students’ interest in learning and the effectiveness of English instruction in higher vocational institutions. Teaching English with the assistance of computers has emerged as the dominant methodology in the expansion of higher-level vocational English education. However, despite the fact that computers make studying more convenient, they also provide a number of temptations and difficulties. In this paper, BP neural network method is proposed, after an in-depth analysis has been conducted on the basis of computer-aided technology in higher vocational English education. The goals of this initiative are to advance the logical application of computers in higher vocational English instruction.
An Enhanced English to Marathi Translator using sequence-to-sequence Transformer

Machine translation is a key application in study of natural language processing that enables the translation of text or speech between different languages automatically. In this research, we provide a brand-new sequence-to-sequence transformer model for translating between English and Marathi. Our model can produce translations that are accurate and fluid because it was trained on a big dataset of parallel English-Marathi utterances. We used a test set to analyse the performance of our model, and the result was a 41.99 BLEU score. Our findings show that even on low-resource datasets for English to Marathi language translation, our model performs well.
An Investigation of Advancements in Blockchain Consensus Algorithms &amp; Leading Protocols

By providing decentralized, transparent, and secure solutions, blockchain technology has revolutionized a number of sectors. The consensus algorithm is one of its key elements that has a substantial impact on both its performance and dependability. The most recent developments in blockchain consensus algorithms are reviewed and analyzed in-depth in this research paper. It assesses well-known protocols, highlighting their advantages, disadvantages, and uses in diverse fields. This work helps to the continuous improvement of blockchain systems by illuminating the evolution of consensus algorithms, promoting wider usage.
Enhancing Texture Classification through GLCM and LBP Feature Extraction with Modified PCA Dimensionality Reduction

Texture identification and classification is a critical function in image analysis, which has numerous applications in a variety of areas such as clinical imaging, pattern detection, and modern quality control. Experts in these fields extract valuable information from images that would otherwise be difficult to recognise by classifying textures. This information can then be used to make informed decisions, diagnose medical conditions, and optimise manufacturing processes. Therefore, the ability to accurately classify textures is a necessary skill for professionals in a wide range of domains. As a result, this paper makes use of the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) techniques to extract relevant features for accurate texture classification from two different datasets, TC12 and KTH-TIPS. The modified principal component analysis (MPCA) approach is used to minimise the dimensionality of features. Dimensionality reduction is frequently utilised to reduce the complexity of the proposed approach, thereby improving performance by reducing the complexity of the approaches and making data more easily visualised.
Comparison of Pseudo Random Numbers generated by LFSR and Multivibrator Circuits on Xilinx ISE

Random numbers are an essential element in various applications, ranging from simulations to cryptography. While true random numbers can be generated using physical processes, pseudo random numbers can be generated using deterministic circuits. This paper focuses on two popular methods for generating pseudo random numbers: Linear Feedback Shift Registers (LFSR) and multivibrator circuits. This paper explores the principles of LFSR and multivibrator circuits for generating pseudo random numbers. It discusses the design considerations, such as area, time and power considerations taken by an LFSR and multivibrator circuits, to achieve desired randomness. The paper also discusses the limitations of pseudo random number generation using LFSR and multivibrator circuits. It highlights the deterministic nature of these circuits, which can result in periodicity and vulnerability to cryptanalysis. It emphasizes that these methods are suitable for non-security critical applications that require efficiently generated pseudo random sequences.