Reviewerly/exHarmony huggingface dataset:
Exploring the Effectiveness of BERT for Sentiment Analysis on Large-Scale Social Media Data

Sentiment analysis is a crucial task in the field of natural language processing (NLP) and has gained significant attention due to the widespread use of social media platforms. Social media data presents unique challenges for sentiment analysis due to its unstructured nature, informal language, and abundance of noise and irrelevant information. To tackle these challenges, advanced techniques such as BERT have emerged as powerful tools for sentiment analysis. In our study, we aim to explore the effectiveness of BERT specifically for sentiment analysis on large-scale social media data. BERT is a state-of-the-art language model that has demonstrated impressive performance on various NLP tasks by capturing contextual information from both left and right contexts of a given word. By leveraging the pre-training and fine-tuning capabilities of BERT, we investigate its potential for sentiment analysis in the context of social media. To establish a comprehensive evaluation, we compare the performance of BERT with traditional machine learning algorithms commonly used for sentiment analysis. Our experimental results indicate that BERT surpasses the performance of traditional machine learning algorithms, achieving state-of-the-art results in sentiment analysis on the social media dataset. BERT's ability to capture intricate contextual information and understand the subtleties of social media language contributes to its superior performance. The model demonstrates exceptional accuracy, precision, recall, and F1-score, showcasing its effectiveness in classifying sentiment labels accurately.
Impact of Screens on How Users Think

The user interface (UI) and user experience (UX) of screens can greatly influence the thinking and decision-making of users. A well-designed UI/UX can enhance the user's cognitive abilities and improve their problem-solving skills. It can make it easier for users to find the information they need, navigate the application, and complete tasks quickly and efficiently. Conversely, a poorly designed UI/UX can lead to frustration, confusion, and decreased cognitive performance. Hence, screens can impact users' emotional responses by altering their mood and emotional state.
Design And Implementation of An Integrated IOT Blockchain Framework for Drone Communication

As more data is transmitted and processed, as it moves from its infancy to full maturity, the Internet of Things (IoT) has the possibility of developing quite quickly. This is because communication technologies are evolving so quickly. If the IoT can be balanced to match the characteristics of the block chain, it will have many uses. Cyber security regulations are becoming more intelligent, encouragingly with the potential of aerospace technologies. Cyberthreats and masquerading attacks to steal data have grown significantly in recent years thanks to advances in communication network technologies. As a result, safety-enhancing concepts and mechanisms are incorporated into regulations. There is an urgent demand for drones as they are being used for all purposes in applications like IoT, warfare and agriculture and thus to deliver better solutions and communications between control systems with the adoption of drones to protect data values and eliminate potential vulnerabilities that could cause huge losses. Unmanned vehicles, also increasingly called drones, bring new problems in fields as diverse as weather monitoring and forecasting as well as agricultural sensing and monitoring. A comprehensive IoTblockchain framework for drone communication has been designed and implemented to secure drone communication in receiving and transmitting data as well as to protect the predictability of received data. The idea is to combine cloud auditing of data and secure communication by drone with block chain to protect data integrity.
Swasthya - An EHR Built on Blockchain

An Electronic Health Record System (EHR) is a systematic collection of electronically-stored health records and information of a patient and population in a digital format. It is typically shared among healthcare stakeholders. While a lot of hospitals in developing nations still lack the facilities that are required to shift to an EHR system, its adoption has been on the rise globally. The traditional EHR systems however face a lot of problems. These problems are majorly related to privacy, security, and ease of use. The records are entirely controlled by the hospitals that have created them. To overcome such a system, the use of a decentralized system to store data is required. The project proposes a system built on top of a Blockchain Network which allows hospitals and patients across the world to be connected to each other. Blockchain has been an emerging technology for a few years now. The key features of blockchain which attract most developers are its immutability (data cannot be changed), decentralization (data is not centrally stored), transparency (data is uniform whenever it is accessed), and distributed ledger (data distributed over various computing machines).
Agriculture Basedfood Supply Chain Traceability Using Blockchain

In Agriculture based Food Supply Chain (ABFSC) Consumerist remits for the manufactured goods by the farmer. In this process, Buyer intensify the Significance of the food product at the same time Producer anticipate to increase the Revenue. Agriculture based Food supply chain are developing into Self-acting and Sophisticated network. Buyer are nowadays more concerned about the quality of the Food. Despite that it’s difficult to trace the source of data and retain traceability across the ABFSC network. The Existing ABFSC is controlled by centralized server and they trust mediator for Trading. In our suggested system, we have demonstrated a solution for ABFSC. The main characteristic of Blockchain and smart contract implemented over the network. In the suggested system, each transaction is recorded in blockchain which finally load data in the Interplanetary File Storage System (IPFS). The IPFS remit the Hash Code of data that ensures efficiency, security and Reliability of the model. The suggested system presents algorithm for smart contract to illustrate operation of System.
Blockchain Framework For Securing Autonomous Vehicles

The use of blockchain technology has become crucial in securing applications that operate in real-time and their associated data. The automotive industries are one of the sectors that have embraced this technology to enhance their products, customer satisfaction, and overall experiences by incorporating distributed ledger technology in autonomous vehicles. This study seeks to investigate the importance of blockchain technology in autonomous vehicles, which includes Autonomous Electric Vehicles and Autonomous Driving. To determine the present state and future obstacles, a comparative analysis of blockchain-integrated autonomous vehicle systems is conducted. The study examines different aspects of blockchain technology, such as vehicle types, driving modes, tracking methods, intelligent contracts, intelligent data handling, and industry-specific use cases. The research is based on recent technologies and practices, with the goal of improving user experiences and industry practices in the future of intelligent transportation.
Fake Product Detection using Anonymity and Supply Chain in Blockchain

In recent times, fake products play a vital role in the marketing industry. And during recent times people tend to order more in online and that leads the manufacturers to cheat the customers. According to census taken recently, 3.3% of the products delivered through websites is counterfeited. To overcome the problem, Blockchain is used as this a tamper-proof technology and the data in the Blockchain is immutable. This paper is about Blockchain-based Product Ownership Management System for identifying the counterfeits products in the Supply Chain. QR codes are used to detect the counterfeit products. First the manufacturer has to verify himself as a manufacturer to add his product. After addition of the product the QR code of the product is generated. The retailer’s job is to activate the QR code once it is purchased by a customer. And the customer can just scan the QR code and verify the authenticity of the product by checking the supply chain.
Digital Certification – Certification Credential as Non Fungible Token (NFT)

Certificates are generally used all around the world. The certificates are given to those who successfully finish any specific courses. Certificate is used to validate the user has successfully completed particular course or competition that is been conducted by the respective organization. Nowadays Fake certificates can be made and used which is hard to find with the naked eye. This paper focuses on avoiding such practices, where the certificate is minted as NFT and it’s data cannot be tampered and the certificate cannot be added without the respective admins minting certificate. The certificate is then issued as a blockchain-stored NFT. Data that has been stored on a blockchain cannot be modified. As a result of this creating a forged certificate will become difficult. Crypto currency is used in the minting and transferring procedures. The industry that will provide the certificate must be confirmed before it is created and transferred. And these processes are smartly crafted contracts.
Towards Improving Breast cancer detection in Ultrasound Images using Deep Transfer Learning

Breast Cancer is a serious health issue that affects women all over the globe, and early detection of it is essential for effective treatment and better patient outcomes. Breast cancer detection techniques like mammography, ultrasound, magnetic resonance imaging, and biopsy have made major strides in recent years. These methods have demonstrated encouraging results in enhancing breast cancer detection accuracy and lowering false positives when used in alongside Artificial Intelligence (AI) and Machine Learning (ML) algorithms. But in this study, the images used belong to Elastography, Doppler Image and Ultrasound for the CNN, Inception v3 and Resnet-50 models. An accuracy of above 92% has been obtained using Inceptionv3 Transfer Learning. A Graphical User Interface (GUI) has been proposed for the detection of Breast Cancer.
ReplayMPC: A Fast Failure Recovery Protocol for Secure Multiparty Computation Applications using Blockchain

Although recent performance improvements to Secure Multiparty Computation (SMPC) made it a practical solution for complex applications such as privacy-preserving machine learning (ML), other characteristics such as robustness are also critical for its practical viability. For instance, since ML training under SMPC may take longer times (e.g., hours or days in many cases), any interruption of the computation will require restarting the process, which results in more delays and waste of computing resources. While one can maintain exchanged SMPC messages in a separate database, their integrity and authenticity should be guaranteed to be able to re-use them later. Therefore, in this paper, we propose ReplayMPC, an efficient failure recovery mechanism for SMPC based on blockchain technology that enables resuming and re-synchronizing SMPC parties after any type of communication or system failures. Our approach allows SMPC parties to save computation state snapshots they use as restoration points during the recovery and then reproduce the last computation rounds by retrieving information from immutable messages stored on a blockchain. Our experiment results on Algorand blockchain show that recovery is much faster than starting the whole process from scratch, saving time, computation, and networking resources.
Addressing Domain Shift in Pedestrian Detection from Thermal Cameras without Fine-Tuning or Transfer Learning

The use of thermal imaging to detect the presence of people in indoor and outdoor environments is gaining an increasing attention given its wide applicability in the tourism, security, and mobility domains. However, due to the particular characteristics of different contexts, it is necessary to train/finetuning specifically object detectors for each scenario in order to obtain accurate results. This is due to changes in appearance caused by camera position, scene size, environmental factors, etc. In this paper, we present a data augmentation method that can improve both versatility and robustness of pedestrian detection models based on thermal images. Thanks to our solution, the trained model can deal with unseen thermal data from both indoor and outdoor environments, reliably detecting pedestrians regardless of their apparent size and position in the image, without any fine-tuning or transfer learning, therefore avoiding time consuming labeling activities to fine-tune and deploy the system in different scenarios.
BlockID: Blockchain based Digital ID and Authentication System for Privacy Improvement

With every industry finding better applications of blockchain, it has become increasingly essential to explore its potential. Security and immutability are some of the properties of blockchain that make it very popular among companies moving to decentralized systems. An architecture for user identity management system based on blockchain that backs authentication has been proposed in this paper. A simulation justifies the use of blockchain for identity purposes. This provides a way to use this system in many industries such as storage of medical health records, a means to provide SSO ability, etc. Basic authentication of a user can take place in matter of seconds and the best part of such a system is that a user is in control of their identity.
Route Planning for an Autonomous Robotic Vehicle Employing a Weight-Controlled Particle Swarm-Optimized Dijkstra Algorithm

Planning the path an autonomous robotic vehicle will take is an essential part of developing and utilizing such a system.The task is deciding on the best route and navigating technique to get the vehicle to its destination quickly and safely.The purpose of route planning is to select a route for the vehicle that will result in the greatest fuel savings.The planner aids the vehicle in accomplishing its goals in the least amount of time and using the least amount of fuel by determining the optimal route by considering variables including traffic, road conditions, and distance.The result is higher production and lower operating expenses.An autonomous robotic vehicle (ARVs) is a self-driving vehicle that uses advanced technologies to navigate through the environment without human intervention.These vehicles can be used for various applications, including transportation, logistics, surveillance, and exploration.Route planning (RP) is the process of determining the most efficient and safe route for a vehicle, pedestrian, or any other mode of transportation to reach a destination.Route management is the process of selecting a collision-free path through an environment, which in practice is frequently crowded.Therefore, offering a RP solution for robotic systems is essential.The particle swarm optimization (PSO) method incorporates inertia weights and imitates the cooperative behavior of the flock's population as well as its predatory nature to address route modeling issues.The Dijkstra algorithm (DA) works by determining the shortest path among the closest vertices between the source and destination.To choose the best path, inertia weight is also taken into account.By analyzing algorithms, we presented the combination technique for RP.In order to give a reliable route planning method, we suggested the weight-controlled particle swarm-optimized Dijkstra algorithm (WCPSODA).MATLAB was used to run the simulation, and conventional tools were used to evaluate the results.The findings of the study show that the suggested systems are capable of performing well.
Context-Adaptive-Based Image Captioning by Bi-CARU

Image captions are abstract expressions of content representations using text sentences, helping readers to better understand and analyse information between different media.With the advantage of encoderdecoder neural networks, captions can provide a rational structure for tasks such as image coding and caption prediction.This work introduces a Convolutional Neural Network (CNN) to Bidirectional Content-Adaptive Recurrent Unit (Bi-CARU) (CNN-to-Bi-CARU) model that performs bidirectional structure to consider contextual features and captures major feature from image.The encoded feature coded form image is respectively passed into the forward and backward layer of CARU to refine the word prediction, providing contextual text output for captioning.An attention layer is also introduced to collect the feature produced by the context-adaptive gate in CARU, aiming to compute the weighting information for relationship extraction and determination.In experiments, the proposed CNN-to-Bi-CARU model outperforms other advanced models in the field, achieving better extraction of contextual information and detailed representation of image captions.The model obtains a score of 41.28 on BLEU@4, 31.23 on METEOR, 61.07 on ROUGE-L, and 133.20 on CIDEr-D, making it competitive in the image captioning of MSCOCO dataset.
Blockchain-Assisted Secure Smart Home Network Using Gradient-Based Optimizer With Hybrid Deep Learning Model

The Internet of Things (IoT) refers to a technology enabler to enhance the urban physical architecture and render public services. But, public access to accumulated heterogeneous IoT urban information is prone to hackers attacking connected devices to the internet intellectual property as well. IoT security serves a dynamic part in the smart city. Some IoT devices are connected in smart homes, and these connections were centred on gateways. In smart homes, the gateways gain a lot of significance; but their centralized structure causes many security vulnerabilities like availability, integrity, and certification. Unified “cloud-like” computing networks and Blockchain (BC) type systems should be used to sort out these problems. Therefore, this article develops a Blockchain-Assisted Secure Smart Home Network using Gradient Based Optimizer with Hybrid Deep Learning (BSSHN-GBOHDL) model. The presented BSSHN-GBOHDL technique employs BC technology to improve the confidentiality of the data in the smart home environment. In addition, the BSSHN-GBOHDL technique identifies malicious activities in the smart home environment via three sub-processes namely data preprocessing, hybrid deep learning (HDL)-based malicious activity classification, and GBO-based hyperparameter tuning. The GBO algorithm assists in the proficient hyperparameter selection of the HDL model, which aids in accomplishing increased detection efficiency. The experimental validation of the BSSHN-GBOHDL approach is tested on a benchmark NSL-KDD dataset with 65495 normal and 60743 attack samples. The results highlight the betterment of the BSSHN-GBOHDL approach over other recent methods with maximum accuracy of 98.29%.
Promise and Limitations of Supervised Optimal Transport-Based Graph Summarization via Information Theoretic Measures

Graph summarization is the problem of producing smaller graph representations of an input graph dataset, in such a way that the smaller <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">compressed</i> graphs capture relevant structural information for downstream tasks. One of the recent graph summarization methods formulates an optimal transport-based framework that allows prior information about node, edge, and attribute importance to be incorporated into the graph summarization process. However, very little is known about the statistical properties of this framework. To elucidate this question, we consider the problem of supervised graph summarization, wherein by using information theoretic measures we seek to preserve relevant information about a class label. To gain a theoretical perspective on the supervised summarization problem itself, we first formulate it in terms of maximizing the Shannon mutual information between the summarized graph and the class label. We show an NP-hardness of approximation result for this problem, thereby constraining what one should expect from proposed solutions. We then propose a summarization method that incorporates mutual information estimates between random variables associated with sample graphs and class labels into the optimal transport compression framework. We empirically show performance improvements over previous works in terms of classification accuracy and time on synthetic and certain real datasets. We also theoretically explore the limitations of the optimal transport approach for the supervised summarization problem and we show that it fails to satisfy a certain desirable information monotonicity property.
Knowledge Graph-Based Method for Intelligent Generation of Emergency Plans for Water Conservancy Projects

In response to the issues of poor content correlation and insufficient intelligent decision support in emergency plans for water conservancy projects, a method for intelligent generation of emergency plans based on knowledge graphs is proposed. Utilizing pre-trained language models (PTM) based on entity masking, the accuracy of entity recognition tasks is enhanced by uncovering contextual features surrounding the masked entities. By employing translations, rotations, and superpositions within the vector space, a multiview convolutional neural network (MCNN) is constructed to enhance the accuracy of relation extraction through complementary and integrated feature representation. Integrating PTM with MCNN enables the construction of an emergency entity relationship extraction method based on PTM-MCNN. Neo4j is utilized for storing entity relationship triplets to construct an emergency knowledge graph. Through the utilization of the mutual information criterion, knowledge retrieval and matching are performed to accomplish the intelligent generation of emergency plans. The results indicate that PTM-MCNN achieves high recognition accuracy (F1 score of 92.2%), ensuring the reliability of the generated emergency plans. Related studies can effectively improve the intelligence of emergency management of water conservancy projects.