Reviewerly/exHarmony huggingface dataset:
A Secured Mobile Payment Transaction Handling System using Internet of Things with Novel Cipher Policies

Research into a mobile payment protocol within the context of the IoT has received significant attention due to the proliferation of smart gadgets. Current authentication mechanisms for mobile payments either place an undue burden on a smart device's limited resources or fail to protect users' anonymity when making a purchase, both of which are major roadblocks to wider adoption of mobile payment systems. This study provides a lightweight and privacy-preserving security mechanisms for mobile payment within the context of the IoT, which neatly addresses these problems. The top transaction scheme fails to maintain its stated security due to vulnerabilities in the underlying Certificate-less Signature System (CSS). In this paper, we detail a forgery attack on the CSS and show how it exposes the inability of the current method to provide the desired level of security. In addition to addressing the previously described vulnerabilities, a more secure CSS structure is shown. Under the random oracle model, where it is assumed that the discrete logarithm issue cannot be solved efficiently, the Forking lemma provides a formal proof of our enhanced method. Lastly, based on our enhanced CSS scheme, we offer a secure transaction protocol for android-based mobile payments. Our architecture decreases the computational and networking burden of other IoT devices inside a given administrative area by requiring only a single unit of high-effectiveness embedded computers to serve the whole domain.
Future Computing with the Rogues Gallery

The Vertically Integrated Projects (VIP) Program at Georgia Tech provides a multidisciplinary research experience aimed at engaging undergraduate and graduate research students in large-scale computing research projects. Since 2019, the Future Computing with the Rogues Gallery VIP course has engaged over 75 students in research on topics related to novel architectures and “post-Moore” computing platforms built around quantum, neuromorphic, near-memory, and reconfigurable computing.One of the key takeaways from this course for the course designers has been on the correlation between these novel computing platforms and traditional skills, techniques, and tools that are used in the HPC and parallel computing arenas. We discuss these parallels as well as the impacts of this course on general student success and research outcomes.
Performance Evaluation of Vedic Multiplier Using Hybrid Improvised High Utility Item Set Mining Using Fuzzy

The main goal of High Utility Itemset Mining (HUIM) is to find all High Utility Itemset (HUI) given a user-defined min util criteria. The utility of an itemset is expressed as a proportion of the database's overall utility. HUIM has a variety of uses. Finding all sets of items that have created a profit more than or equal to min util for business purposes is the process of high-utility itemset mining. To find HUI, many methods have been presented. Because the HUIM issue is broader than the FIM problem, any approach for identifying HUI may also be used to find frequent itemsets in a transaction database. By this approach the Performance Evaluation of Vedic Multiplier is analysed.
Intelligent Multimodal Sentiment Analysis Using a CNN-Based Framework

There are several social media analytics activities that rely heavily on sentiment analysis of user-generated textual information from the internet. Adding pictures and videos to one's social media posts has become common practise among today's internet users as a means of conveying one's point of view and experiences. Large-scale text and visual data sentiment analysis helps extract user feelings about brands or subjects. An intelligent multi-modal sentiment analysis framework is urgently needed to effectively mine information from several modalities in order to keep up with the explosion of massive multimodal data. Previous research mostly dealt on either textual or visual material. By using a "convolutional neural network" ("CNN") to extract features from several modalities, this study aims to provide a novel framework for multi-modal sentiment analysis. To showcase the sophistication of our models, we conduct "multi-model sentiment analysis" on two publicly accessible "datasets," "including text," "audio," and "visual input.
A Cooperative-Competitive Strategy for Autonomous Multidrone Racing

Autonomous multidrone racing is extremely challenging due to rich agent interactions and cluttered environments. Although there has been some progress, existing approaches often do not properly consider cooperative behavior among drones and are computationally expensive, deteriorating the racing performance. To address these issues, we propose a novel cooperative-competitive strategy based on the leader-wingman framework for autonomous multidrone racing. For the leader, a game-theoretic method is designed to compete against the opponent. For the wingman, a novel line of sight-based cooperative method is proposed to support the leader by blocking the opponent. As a result, the win rate can be improved even when the opponent has a significant speed advantage. Furthermore, our strategy has a lower computational cost than the existing game-based strategies. Finally, we verify our strategy by extensive comparison to the state-of-the-art strategy in simulation and in the physical world.
Medical Data Governance System using Blockchain

The traditional electronic health records where there is centralized system the doctor will entry the particular patient diagnoses in the website in those case where the information with in the same organization. There is no way if person admitted in another hospital to retrieve the medical records in efficient way and fast way to access. The privacy is also one of the main issues in this way of transferring the medical information. An efficient way of storing the data in decentralized way in the blockchain is introduced, where the doctor initiates a new request to entry the patient information the blockchain creates a new block and then add to the chain which is linked to previous blocks. It is impossible to break the chain to update the information at particular block. The data is secured with asymmetric cryptography method to encrypt or decrypt the data. The doctor can upload documents for their patients, download and view documents for their patients to which they have access, and is a user in the organization with the right role. The patient can examine them, view document access logs, and manage access to their documents on their dashboard because they are an organisation user with the necessary role.
Detecting Singular Value Decomposition-based WatermarkEmbedded Medical Images using ResNet Neural Network

Digital products copyright protection is a severe issue in the growth of network technology. The security of data, especially medical data, is of utmost importance. The growth of the internet gives easy access to transactional medical data. It causes image distortion and a high risk of misclassification among medical experts. Watermarking aims to protect information and provide authentication for data during transmission. Instead of the conventional method, the deep learning-based watermarking method gained benefits in information security. In this paper, Singular Value Decomposition transformation is applied for embedding watermark, and Resnet a deep-learning model is modified for detecting watermarked images. Watermarking algorithm ensures robust watermarking for medical images to detect changes even after some manipulations like cropping, compression, filtering rotation, and more. MSE, SSIM, and PSNR are some metrics for assessing image quality.
A Lightweight Transformer Model using Neural ODE for FPGAs

A transformer is an emerging neural network model that employs an attention mechanism. It has been adopted to various tasks and achieved a favorable accuracy compared to CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks). Although the attention mechanism is recognized as a general-purpose component, many of the transformer models require a significant number of parameters and thus they are not suited to low-cost edge devices. Recently, a resource-efficient hybrid model that uses ResNet as a backbone architecture and replaces a part of its convolutional layers with an MHSA (Multi-Head Self-Attention) mechanism was proposed. In this paper, we significantly reduce the parameter size of this approach by using Neural ODE as a backbone architecture for the MHSA mechanism. The proposed hybrid model reduces the parameter size by 97.3% compared to the original model without degrading the accuracy. Since the model size is quite small, it is implemented on Xilinx ZCU104 FPGA (Field Programmable Gate Array) board so that it can fully exploit on-chip BRAM/URAM resources. The FPGA implementation is evaluated in terms of resource utilization, accuracy, performance, and power consumption. The results demonstrate that it speeds up the model by up to 2.63 times compared to a software execution without accuracy degradation.
Proxy-Based Re-Encryption Design for the IoT Ecosystem

Protect data in transit with a proxy-based re-encryption solution designed specifically for the Internet of Things ecosystem's needs. Because of identity-based encryption and proxy re-encryption can safely outsource to the cloud with proxy-based re-encryption technology. To compensate for the limitations of IoT devices, it delegated complex task processing to a proxy device located closer to the network's edge. Furthermore, distributing cached material through the proxy using information-centric networking features, improves service quality and network capacity utilization. The proposed research and security strategy review demonstrate that our method protects data in every sense, from granular control over who can access what to the elimination of key system bottlenecks.
Authentication of legitimate checks and detection of fake checks for reducing the Scams using Block chain based technology

Fake scams are one of the most common attacks used to combat customer scams. Currently, there is no immediate response to test certification and detection of fake tests. Instead, banks have to wait longer to detect fraud. More specifically, our approach allows banks to provide percent of public and used test without disclosing private information provided by financial institution customers. Fake tests come in many forms. They are like corporate or individual tests, cashier tests, checkout order tests, or email tests. These scams paint like a real test because the test is a fake even for employees of financial institutions. The name and title of a valid financial institution is regularly stamped. For your security, the bank will deal with you promptly and offer security offers for financial transactions that require your approval. Some charities want to register, approve, and send to users through headquarters and charities. If you want to help a charity, you can help by submitting the charity to an account sent to your financial institution. Amounts are charity transactions, and incoming charity amount transactions are sent to top-level workstations and controlled by top-level workstations. The transaction completes when it receives a response from the affected customer.
Billion-scale Detection of Isomorphic Nodes

This paper presents an algorithm for detecting attributed high-degree node isomorphism. High-degree isomorphic nodes seldom happen by chance and often represent duplicated entities or data processing errors. By definition, isomorphic nodes are topologically indistinguishable and can be problematic in graph ML tasks. The algorithm employs a parallel, “degree-bounded” approach that fingerprints each node’s local properties through a hash, which constrains the search to nodes within hash-defined buckets, thus minimising the number of comparisons. This method scales on graphs with billions of nodes and edges. Finally, we provide isomorphic node oddities identified in real-world data.
On the Implications of Heterogeneous Memory Tiering on Spark In-Memory Analytics

Today, the rise of big data has driven a growing demand for efficient and scalable computing solutions that can handle the massive amounts of data generated by modern applications. To address this challenge, application developers have embraced the use of novel distributed frameworks, such as Apache Spark, which enable efficient, in-memory processing for large amounts of data. Moreover, providers are seeking for new alternatives to cope with this increasing need for “infinite” memory resources, that can provide analogous performance efficiency, while also reducing operational costs. In this direction, novel multi-tier and disaggregated memory architectures emerge, which combine heterogeneous memory technologies that tradeoff between performance, cost and energy efficiency. This diptych of evolution imposes new challenges and open questions on how to optimally configure software and hardware as a whole, for maximizing resource efficiency. In this paper, we examine the implications of heterogeneous memory tiering on Spark in-memory analytics. Our study considers a multi-tier heterogeneous DRAM/NVM memory system with contrasting access latency, bandwidth and energy consumption capabilities. By using a set of 7 diverse applications from the HiBench benchmarking suite, we first explore the impact of these memory configuration setups on their performance and energy efficiency. Then, we perform a detailed analysis on how the system's low-level performance metrics correlate to higher level metrics of interest (i.e., performance/energy), which aims to provide deeper insights w.r.t. the relationship between software and hardware events. Driven by the obtained results, we identify a set of guidelines derived by deploying Spark in-memory analytics over heterogeneous memory tiered systems.
Strain Measurement Using Digital Image Correlation Technique

Strain is a critical parameter within engineering and construction of projects. The objective of the paper is to investigate a material’s crack propagation and deformation. The automobile industry has an increasing demand for lightweight composite materials, improved product performance, efficiency and increased safety. Composites are also extensively used in aerospace and automotive applications due to their high specific stiffness and strength. It is critical to understand the influence of high strain rate on the mechanical behaviour of composite materials in order to develop durable composite structures. In this project, influence of high strain rate on the tensile strength, tensile modulus, and failure strain of any material is determined. Strain is a measure of the deformation of a body when subjected to an applied force. There are various methods for the measurement of strain, one of them is the UTM machine used for measuring the strain of any material, the other one is the traditional method-extensometer which only gives an average value of the strain. In order to overcome the problems faced by the older methods, the Digital image correlation technique is proposed. It is a novel optical approach for sensing strain and displacement that is non-contact. Digital Image Correlation is easier to use and less expensive than other techniques like speckle interferometry, and it is more precise and subjective than manual measuring methods, opening up a wide range of possible applications. Digital Image Correlation is applied in the developing systems to characterise the material and to monitor engineering components
Using Machine Learning to Analyze Twitter’s Sentiment

This paper revolved around sentiment analysis from tweets and methods used for it. Negative and positive emotions, as well as other elements like as emoticons, quotations, hashtags, mentions, etc., may all be retrieved in this way. In order to improve efficiency and make the transformation from words to vectors possible, the Word2Vec Model is used. Random Forest and Naive Bayes are two examples of machine learning algorithms that make use of Bag of Words. The last step is to use the XGBoost model with its improved parameters to determine the best classification strategy for Sentiment Analysis.
Optimizing the Critical Path of Distributed Dataflow Graph Algorithms

Executing graph algorithms in a parallel or distributed context is a challenging problem. Solving race conditions with locks is usually prohibitively expensive and some algorithms opt for a strategy that ignores the race condition altogether and corrects later the derived solution if it is invalid. Alternatively, dataflow algorithms solve the synchronization problem by executing the algorithm by following a partial order on the graph. While removing the cost of locks or avoiding a checking phase improves performance, it is possible that the algorithm picks a partial order with long chains, which limit parallelism. In this paper, we investigate how distributed dataflow graph algorithm obtain a partial order and how one could favor orders with shorter long chains. Most dataflow algorithms obtain their order by having each vertex of the graph pick a uniformly random number in [0;1) and order the vertices based on that number. We believe that this type of order could lead to long chains in graphs with dense regions such as small world graph. We design two alternative ways of generating the order to make it similar to a largest degree first order. We study the behavior of these different algorithms on a wide range of randomly generated RMAT graphs and on a set of real world graphs. And we show that our ordering methods can significantly reduce the length of the longest chain.
An Empirical Analysis of Collaborative and Content Based Recommender System by using AI

A system that attempts to predict our preferred movies based on our own past selections and preferences is referred to as a “movie recommendation system.” Machine Learning algorithms like content-based filtering, feature selection with its subset method Vectorization is one of those key components which acts as the base of this movie recommendation system. Collaborative filtering mechanism is also used widely in recommender systems. In this paper, we have presented an analysis of Collaborative and Content based recommender system which will help in selection of an algorithm based on the requirements of the developer.
A Stratified Approach to Monitoring Cloud Services

Cloud services have become an essential part of modern computing, providing on-demand access to computing resources and services over the internet. However, ensuring the reliability and performance of cloud services is a challenging task, as these services are highly complex and dynamic. Traditional monitoring techniques, such as threshold-based monitoring, are not suitable for cloud services, as they cannot handle the dynamic nature of these services.A stratified approach to monitoring cloud services using machine learning is a novel approach that uses machine learning algorithms to monitor cloud services in a more effective way. This approach is based on the principle of stratification, which involves dividing a complex system into several layers or strata, each with its own monitoring strategy.The first layer of the stratified approach involves monitoring the infrastructure layer, which includes infrastructure, both real and digital used to provide Cloud computing. This layer is monitored using machine learning algorithms that can detect anomalies in the network traffic, CPU usage, memory usage, and other infrastructure-related metrics.The second layer involves monitoring the platform layer, which includes the software platforms and frameworks used to develop and deploy cloud services. This layer is monitored using machine learning algorithms that can detect anomalies in the application logs, database usage, and other platform-related metrics.The third layer involves monitoring the application layer, which includes the actual cloud services provided to users. This layer is monitored using machine learning algorithms that can detect anomalies in the user requests, response times, and other application-related metrics.By using machine learning algorithms to monitor cloud services at different layers, the stratified approach can provide a more comprehensive and effective monitoring solution than traditional monitoring techniques. The approach can also adapt to the dynamic nature of cloud services, as the machine learning algorithms can learn from past data and adjust their monitoring strategies accordingly.In conclusion, a stratified approach to monitoring cloud services using machine learning is a promising approach that can help improve the reliability and performance of cloud services. By monitoring cloud services at different layers and using machine learning algorithms to detect anomalies, this approach can provide a more effective and comprehensive monitoring solution than traditional monitoring techniques.
Efficient Removal of Real Time Rain Streaks from A Image using Novel Naive Bayes (NB) Compare over Linear Regression (LR) with Improved Accuracy

Proposed study will examine the efficacy of removing real-time rain streaks from an image using novel NB and LR with the support of ML. Materials and Methods: A Realistic Single Image Dehazing (RESIDE) dataset has been collected from kaggle.com, which is a repository for our study. For all groups, a total sample of 22 was used. The proposed Naive Bayes algorithm is compared to the existing linear regression algorithms. The sample size is 44. Our proposed method includes steps for removing noise from images. For simulation, a pre-test power of 80% is used. Results: The proposed NB achieved an accuracy and sensitivity of 88.5% and 95.2%, whereas LR achieved an accuracy and sensitivity of 86.3% and 93%. The samples which are required for this investigation are calculated with the G power tool by fixing the minutest power to 0.8. In descriptive statistics, the observed effect size (p<0.05) in reference to the Naive Bayes and linear regression methods appeared significant. Conclusion: According to the experimental results, the novel NB algorithm performs significantly better than the existing LR.
Combatting Counterfeit Currency: The Power of EURion Constellation and Other Anti-Fraud Measures

This paper depicts the analysis and survey done on banknotes of various foreign currencies. Without permission and in violation of the legislation of the country, it is strictly forbidden to photocopy and reproduce bank notes. EURion constellation and Counterfeit Deterrence System (CDS) are the key methodologies used in fake currency detection and photocopy prevention. Photocopying the banknotes and currency might result you involving in criminal endeavour. All modern photocopy machines and printers have this built-in firmware detection mechanism which detects the presence of EURion constellation when they try to copy. Some machines resulted in printing white lines across the printing area and some leave empty white spaces. After the invention of EURion constellation, almost all the countries started to use this technique in their currency notes in order to prevent the reproducing of banknotes. But some countries use this to detect fake currencies and some use this to prevent photocopy of notes. In this study, we offers a system that prevents photocopying of Indian currencies, which already uses the EURion constellation and Counterfeit Deterrence System to detect the fake notes.