Reviewerly/exHarmony huggingface dataset:
Design and Web Development of E-Voting System

Voting plays a very important role in electing a candidate in the government and private organizations these days. Voting system’s integrity and privacy are the main challenges in developing a voting system. The person who is voting a particular party should not be known by everyone which is a concern to privacy of the voter and the voting system should give correct results which shows the integrity of the voting system. One single vote may be responsible to change the entire result of the polling. So, voting system should be designed in such a way that each vote should be given its own importance and that should not get tampered by the hackers. So to solve this problem we are developing a full stack web application on voting system which does not compromises with integrity, privacy and security. This project has been developed using a server-side scripting language PHP. As the name server-side suggests the code which has been written cannot be accessible to the client thus it ensures privacy and security.
Deep Learning Techniques For Spamming And Cyberbullying Detection

Social media allows people to exchange their views on different topics. However, some users post offensive comments on social media, which is known as cyberbullying, and some scam users by posting fake links in the comment section which adversely affects the user experience on those platforms. Thus regulation of contents in social media has become a growing need. The metrics for the regulations can be achieved by detecting these comments using deep learning techniques and machine learning algorithms Gaussian Naive Bayes, Logistic regression, Decision tree classifier, Adaboost classifier, Random forest classifier, MBERT and BERT, Gaussian Naive Bayes, Logistic Regression for cyberbullying and spamming respectively.This paper aims at detecting cyberbullying and spamming with the help of the techniques mentioned above for spamming and cyberbullying detection in English and Tanglish. Furthermore, it compares a number of supervised techniques, including standard and ensemble methods. Ensemble supervised methods have the capability to outperform conventional supervised methods, according to the evaluation of the results. The fine tuned classification BERT model (Bidirectional Encoder Representations from Transformers) and MBERT(Multilingual BERT) are based on Transformers, are used for cyberbullying detection and machine learning algorithms which include Gaussian Naive Bayes and Logistic regression have been used for spam detection. We do a comparative study between deep learning and machine learning algorithms. Compared to machine learning algorithms, deep learning algorithm BERT performed better at detecting spamming comments.
Transductive Support Vector Machines (TSVM) With Hybrid Recurrent Neural Networks For Medical Image Filtering

Filtering an image is one of the most fundamental and consequential tasks in the field of image processing. As of now, scientists still face a very real and pressing dilemma. Images, whether in print or digital form, are a crucial criterion in many different industries, including geosciences, aerospace, education, agriculture, entertainment, surveillance, and many more. Background noise degrades image quality, despite extensive research aimed at finding a solution to this problem and the anticipation of a plethora of possible countermeasures. Methods for picture filtering, including those based on machine learning and those based on optimization, are detailed here. It has been possible to categorize these methods by the methods they employ. Gaussian noise, white noise, salt and pepper noise, speckle noise, and poison noise were all removed from lung CT scans using a combination of Recurrent Neural Networks (RNNs) and Transductive Support Vector Machines (TSVMs) in this study. Using TSVM, RNN can function better than ever before. The approach proposed here uses LSTM to normalize batches of data. By simulating a large number of individual beehives, the ABC (Artificial Bee Colony) algorithm may determine the optimal size of each batch. MATLAB was used to realize the suggested method. The experimental consequences demonstrate that the suggested system overtakes the baseline in terms of PSNR, MSE, and precision.
An Effective Recurrent Neural Network For Image Denoising With Long Short-Term Memory Founded Batch Normalization

These days, a high-quality medical image is essential for doctors and radiologists to provide a quick and accurate diagnosis. Noise is a common issue with medical images, both during their procurement and program, reaction, storing, and reclamation. The integrity of the medical imaging is compromised by noise. Edges, structural features, boundary sharpness, etc., are all lost in the degrading process. Researchers still face difficulties in solving the challenge of removing noise from the original image. The vast majority of researchers have developed efficient noise-reduction techniques. Denoising and restoring images is one of the most studied and fundamental problems in computer science. This is the first effort of its kind to introduce a RNN (Recurrent Neural Network) with LSTM (long short-term memory) -based batch normalization for medical image denoising. We start with noisy CT lung pictures as our input. The RNN is used to remove noise from the input image. It has become increasingly common to use batch normalization to rapidly train deep feed-forward neural networks. Batch normalization is used in the LSTM, which proves that it can hasten optimization and enhance generalization. The PSO (Particle Swarm Optimization) algorithm is used to determine the best batch size for batch normalization. MATLAB was used to create the proposed system. Experiment results are compared to those of the current system and shown to be superior.
Using license terms to streamline interlibrary loan and electronic resources communication

Licenses dictate how libraries can lend electronic interlibrary loan materials to other institutions. The nuances of these licenses are often varied and difficult to track, particularly for non-electronic resource librarians. University of Kentucky Libraries’ Interlibrary Loan and Electronic Resources units streamlined the communication of licensing terms by using the license term module within Alma.
Multi-Keyword Graded Exploration in Encrypted Cloud Data for Industries Based on Rc4+ and Forest

The vast amounts of storage space offered by the cloud encourage businesses to store their most sensitive files and data on remote servers accessible over the internet. When comparing a local server and personal computer to a cloud environment, it's clear that many more tasks can be accomplished with the former. Cost, speed, global scalability, productivity, and performance are just a few of the six reasons why cloud services have gained popularity among businesses and consumers alike in recent years. This paper presents the River cypher RC4+ (RCPF) scheme for fast, efficient, and secure multi-keyword search indexing in an encrypted cloud environment. In order to encrypt and decrypt data, the RC4+ algorithm uses a final symmetric secret key that is created by a pseudorandom byte generator. In order to generate an index list for each file in a shorter amount of time, a minimal spanning tree is formed. After that, the encrypted index list is searched by the cloud server, and relevant files are ranked according to the search query. The user's decryption of the encrypted data using the symmetric key behaves as a whole.
Medical Image Synthesis Workbench Supporting Multiple Modalities With Feature-Level Transforms

Multi-modal In recent years, medical image synthesis has arisen as a holistic analytic strategy that typically employs many transformation methods. When it comes to medicine, multi-modal image synthesis refers to the technique of combining images from several imaging modalities to enhance image quality while maintaining detail. Image processing, computer apparition, pattern acknowledgment, machine knowledge, and artificial intellect are just few of the many cutting-edge fields that fall under the umbrella of medical image synthesis. The synthesis of many medicinal imaging modalities has been used extensively in the clinic to help doctors better understand the lesion. This paper presents the firefly algorithm, a multi-learning model workbench that minimises squared errors and maximises synthesis quality via weighted entropy. MATLAB simulations are used to compare the proposed workbench against state-of-the-art methods in terms of peak signal-to-noise ratio, Mean square error, correlation, and precision. The proposed workbench performs well across the board and requires less computing power.
Identification and Labeling of Textual Cyberbullying using BiLSTM and BERT

In this aeon of digitalization, people are inclining more and more towards technology. This has both positive and negative aspects, and one of the negative effects is cyberbullying. Cyberbullying allows people to comment negatively on various social media platforms, this project aims at detecting and classifying such text. Using BiLSTM (Bidirectional Long Shortterm Memory) and BERT(Bidirectional Encoder Representations from Transformers), the cyberbullying text on various social media platforms is identified and classified into classes such as religion, age, gender, ethnicity, not_cyberbullying and other_cyberbullying. This helps in keep the record of types of cyberbullying that occurs on social media and whether it’s reduced over time after reporting it and taking strict measures against it.
MonuAR:M.A.R Application for Visualising 3D Monuments

The Art and Heritage has been an integral part of our Indian Culture. These art forms and heritage are left unnoticed just because they are very much away. Therefore these sites can be brought back to life in any environment. The heritage properties of art and craft of India can be brought to life from anywhere by using an AR application on the phone. There are times when people visit the monuments and they are in need of a third party to show them the main attractions of the monument. Instead of relying on a third party, this can be done using this app where an AR marker appears on important tourist spots. The software that are used are Unity, Blender, Python and Visual Studio for C#. The software development kit used are Vuforia AR SDK and Mapbox SDK.The software that are used are Unity, Blender, Python and Visual Studio for C#. The software development kit used are Vuforia AR SDK and Mapbox SDK
Natural Language Interface to the Database Management System using NLTK in Python

Data is a very important factor in every domain. To manage this data efficiently, we need Database management systems (DBMS). To store and manage the relational database SQL (Structured Query Language) is used. So, while interacting with such a relational database the user should familiarize themselves with SQL and its syntax for writing the query. The Non-expert user needs a lot of time to learn SQL queries and they face a lot of difficulties if the syntax of such queries is wrong. The best solution to overcome this problem is to create a natural language interface for the database system. It allows the non-expert user can interact with the Database using the queries in a natural language like English and get easy access to the database system. The main objective of this paper is to introduce a new approach to retrieving data from the database without using SQL queries. We have proposed and implemented an NLIDB system with a web-user interface that takes the input queries in the English language and converts them into an SQL query. The system was designed for University Database Schema. The system successfully generates a query of select, where clause, aggregate function, AND OR operators and order by keyword based on query in natural form and also display proper output through GUI.
Query Fuel - In-house Query Solver

The concept of peer learning dates back centuries, and with the increasing technology, it has become more accessible and easier for everyone to interact and learn from others. In such a situation, a common ground that brings everyone together plays a crucial role. Through sharing knowledge and experiences, people can build on the accomplishments of those who came before them and progress in various fields such as science, technology, medicine, and more. Various attempts have been made to make such a common platform, Quora and StackOverflow are two major players in this domain. Query fuel-an interactive community platform that aims to provide a similar solution with some features better than the existing solutions. The platform works on an organizational basis, where a registered user can post a query or any topic of discussion and let others participate. The organization and topic can vary from being a college to a support group where people feel safe discussing their discrete issues. Built on the MERN stack and having a custom ‘Query Searching Algorithm,’ the web application takes in the query as text input and passes through a search engine where we use the Probabilistic Ranking Algorithm and log-Linear Model Ranking Algorithm, which sets criterions for each query and rank them. This minimizes each query’s search time and enables the ‘Search as Type’ feature, which is not present in the existing systems. After thorough testing, we have come up with several metrics which prove that our solution is much more secure compared to the existing ones. Once we test the scalability with data in millions, we will be ready to ship this to the commercial market.
An Exploration Of An Operational Multi-Data-Set Kernel Culture Scheme For Practice In Data Mining

Data-centric solutions are vital to all businesses, media, and research. Since data size matters, knowledge mining from massive data requires automation. The world awaits machine learning with AI. Industrial productions to purchase decisions rely on knowledge-mined solutions. Machine learning benefits medical research. This study examines the elements that influence the effectiveness of machine learning algorithms and suggests approaches for improving the knowledge-gathering process. In this study, we experiment with a solution based on support vector machines. Oversampling and under-sampling are both managed by carefully keeping an eye on the sampling methods utilised. An improved SVM is conducted after many time series data have been combined, validated, and analysed. When working with data from several sources, researchers have found that SVM kernel learning has its limits; hence researchers have turned to the dual SVM notion as a workaround. Multiple kernels were tried out for use in classification difficulties, and a co-variance based computing approach was put into practise for processing data from various sources. Dual SVM is shown to be more efficient than single SVM in terms of both processing time and classification accuracy, according to independent validation on many datasets. In this study, we provide the WMKL (Weighted Multi Kernel Learning) technique, which may be used to effectively learn from multidimensional, massive data sets. Computational workloads that involve many algorithms are typically labor-intensive and need for human intervention throughout the kernel selection process. We compare our new method to the standard method and find that it performs better and has less of an impact on the available time.
Panchi: A women’s security app

The domain is the primary focus of this project (Web Application Development). In India, it is difficult for women to report the issues they are having because it either takes a long time to document them and convey them to the appropriate authority or because of cultural pressure. In order to solve their issue by presenting complaints to the appropriate authority immediately, a web application is suggested. It has a complaint module that enables women to report emergencies so that authorities can be dispatched to those who need assistance. It is an interface for registering complaints and following them up. The main problem statement tried to be worked upon is the fact that when women want to register any grievance they are not able to do so because of various reasons out of which two stand out as the most common. The way to reach the authorities responsible to take action is way too long and time-consuming and this discourages them to go ahead with it as they’re unsure of even being heard or what the outcome of the situation might be even after spending a long time and effort for the same. The other factor is the fear of being judged by the society which is one of the leading factors as to why women do not go ahead with reporting their grievances. This has been overcome by our solution by having only end-to-end access to the data/complaints between the user and the admin and ensuring secrecy.
Discrete and Continuous Time Chaos Based on True Random Number Generators

Data security has become more crucial to individuals from all walks of life as a result of the current advancements in communication engineering and the widespread use of the internet. Numerous communication protocols with high levels of security are now in use. There are also critical management, user identity recognition, and secret image encoding, and random number requirements for various applications. Compared to figures that were shown to be pseudorandom, true random numbers offered more irregular number patterns in the form of codes with significant advances (PRNs). Sensitivity to initial circumstances, unpredictability, and periodicity are the hallmarks of chaos. These predictions line up with the development of TRN generating techniques in the chaotic sector. In order to provide a thorough assessment of chaos-based TRNGs, this overview research seeks to address these concerns. The formation of True Random Numbers depends on two types of chaotic systems: uninterrupted chaotic systems and separate time chaotic systems. To help researchers choose the ways and difficulties that best suit their requirements or goals, the fundamental procedures and difficulties are presented. The evaluation of existing methods emphasizes both their contributions, their significance to the subject. Additionally, consider TRNGs for this issue that are based on current-mode chaos in a portion of the research. Lastly, quantitative results are shown. Explanation of the results for the strategies outlined in the previous sentence once they have been put to use. Finally, the purpose of this study is to present some hypotheses for future research that may be more beneficial in various applications and lead to the creation of novel chaos-based TRNGs.
Reducing Adversarial Vulnerability Using GANs

The Cyber-Threat industry is ever-growing and it is very likely that malware creators are using generative methods to create new malware as these algorithms prove to be very potent. As the majority of researchers in this field are focused on new methods to generate better adversarial examples (w.r.t. fidelity, variety or number) just a small portion of them are concerned with defense methods. This paper explores three methods of feature selection in the context of adversarial attacks. These methods aim to reduce the vulnerability of a Multi-Layer Perceptron to GAN-inflicted attacks by removing features based on rankings computed by type or by using LIME or F-Score. Even if no strong conclusion can be drawn, this paper stands as a Proof-of-Concept that because of good results in some cases, adversarial feature selection is a worthy exploration path.
OMT: A Run-time Adaptive Architectural Framework for Bonsai Merkle Tree-Based Secure Authentication with Embedded Heterogeneous Memory

Prospects of novel flash-based, crash-tolerant, non-volatile memory (NVM) such as Intel’s Optane DC memory [17] and future CXL-based persistent memory [28] bring about new and exciting usage scenarios for both general-purpose and embedded computing systems involving FPGA-enabled Trusted Execution Environment (TEE) [35], [43]. However, the NVM modules demonstrate high write latency and limited write endurance and therefore, are more suitable for a hybrid NVM + volatile DRAM setup [15]. Furthermore, different memory-based adversaries in NVM including integrity-based attacks demand the use of a robust authentication method such as Bonsai Merkle Tree (BMT) [4]. Conventional BMT authentication schemes should not be directly applied to such hybrid, embedded NVM platforms as the typical frequent update process of a BMT affects runtime performance even when persistence is unnecessary. On the contrary, the latest intermittent BMT update techniques can provide better run-time throughput, but lack crash-consistency [27]. Therefore, a heterogeneous memory-based system would greatly benefit from an authentication mechanism that can change its update method on-the-fly and provide a good balance between the persistence and run-time performance.In this paper, we propose a unified and hardware-friendly BMT framework called opportunistic Merkle tree (OMT). OMT is both modular and run-time adaptive by 1) merging the logic for two different BMT update schemes while still allowing for parallel updates through separate update cores and 2) streamlining the BMT read/verification for both of the update methods with a common datapath to support both recovery-critical and general data, therefore eliminating the need for individual authentication subsystems for different memory modules in a heterogeneous memory platform. Most interestingly, through the use of its adaptive Data and Address Management Unit (DAMU), OMT allows for a run-time switch between the update methods depending on the request type (persistent/intermittent). Extensive testing of OMT in a heterogeneous embedded memory system provides 44% lower memory overhead & up to 22% faster execution in synthetic benchmarks compared to a baseline.
Federated Learning with Internet of Things for Data Privacy and Security

In recent days, internet connectivity is highly increased due to the mobile technology advancements and internet rapid growth that leads to the generations of vast amount of data and makes Machine Learning’s (ML) training more difficult. In 2018, a data breach is occurred in Facebook and provides some major issues on privacy and security of user’s data that are used to train the ML models. To solve these issues, a recent approach called Federated Learning (FL) is developed by various researchers. Over the traditional ML methods and its classification, recent research mainly focused on the advantages of FL. But certain challenges are occurred in the FL and it must be solved for next stage development. The main aim of this study is to present a solution for the problem of FL with complete assessment in terms of vulnerability. The current development of FL in Internet of Things (IoT) and its applications are studied in this work. The user’s data privacy is protected by different privacy preserving techniques and their importance are also surveyed in this work. The attacks and challenges faced by FL is discussed, where the latest research on the FL’s application with comprehensive investigation is presented in this research work.