Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis at Document level of Telugu Data from Multi-Domains

Finding common threads of optimism or negative in text is the goal of sentiment analysis. In the business world, it is used to learn about a product's reception, a customer's identity, and their expectations of a firm by monitoring the tone of online conversations on forums like Reddit and Twitter. Sentiment analysis in various languages has arisen as a separate topic of NLP as businesses seek for client feedback in previously untapped markets. The stakes could not be greater, since Telugu is a Dravidian language spoken by almost 82 million people. Little effort, such as annotated data and software, is put towards supporting Telugu, hence the language is often overlooked. To better understand the sentiment of the data, we conducted our research using the "Sentirama" dataset and, as you'll see in the paper, we used a variety of Machine Learning models (including SVM-linear, SVM-quadratic, SVM-polynomial, Random Forest, Naive Bayes, and KNN) and Featured concepts (including word2vec+ (CBOW or skip-gram), TF-IDF, and Fastext). To find the best Deep-learning model for Telugu sentiment analysis, we also tried out LSTM, Bidirectional-LSTM, and 1D-CNN.
noQ: An Innovative Approach to Virtualize and Automate Queues

It has been observed that waiting has been a constant trouble in our increasingly fast-paced world where the traditional waiting in lines/queues is not a viable option as it is statistically shown that 25% of people would only wait for a max of 2 mins, 59% would wait no longer than 4 mins and 73% would abandon their purchase or work if its more than 5 mins. So, to tackle this common issue, the proposed system incorporates an innovative solution by virtualizing the entire queuing system and creating a scheduling process with the help of advanced scheduling algorithms such as FCFS Scheduling and Priority Scheduling. This is a web application that will be developed using Full stack (Laravel, PHP, CSS, HTML, phpMyAdmin). With the help of this an ecosystem will be created where the work process can be monitored, altered, and improved that makes it efficient for both the parties, i.e., the workers and the people waiting in a queue, and allowing for further improvement by collecting workers statistics which can be used by the admin to further optimize the workflow. This system enables ‘Virtual Queue Creation’ by adapting functionalities such as ‘Virtual Room Creation’ used by famous organizations such as Google and Microsoft. The work statistics get stored in the backend with the help of SQL-based technology i.e., phpMyAdmin and a user-friendly UI will be developed for ease of use for all the end users of this proposed virtual system. The web application as mentioned above uses scheduling algorithms for the automation of queue management and will also have the functionality for manual management making it more flexible for the end users. This web application will incorporate several APIs for data collection and storage for statistics handling. The APIs and frameworks that will be used are Laravel, Excel JS API, and user-created APIs. This new system will be able to reduce the waiting times to a great extent and will be able to facilitate the needs that are necessary for this fast-paced modern world.
Metaheuristic Evolutionary Algorithms: Types, Applications, Future Directions, and Challenges

Metaheuristic optimization methods are widely used to solve complex optimization problems in various fields such as engineering, finance, and logistics. Evolutionary Algorithms (EAs) are a family of metaheuristic optimization algorithms that are inspired by biological evolution and natural selection. EAs mimic the process of natural selection by maintaining a population of candidate solutions and applying genetic operators such as mutation, crossover, and selection to generate new solutions over multiple generations. In this review, we will focus on six popular types of EAs: Genetic Algorithms (GA), Evolution Strategies (ES), Genetic Programming (GP), Differential Evolution (DE), Estimation of Distribution Algorithms (EDA), and Cultural Algorithms (CA). The study also provides insights into the selection of appropriate metaheuristic optimization methods for solving specific optimization problems.
Text Comparison Based on Semantic Similarity

Main objective of the research is to develop a method for computing text similarity incorporating both statistical techniques and semantics. The method uses algorithms, libraries, and semantic models to analyze the word usage patterns and frequency of the texts, and can model human common sense knowledge through the use of multiple algorithms. One strength of the method is its adaptability to different domains, because of the incorporation of similar semantics. Users can choose from a range of methods within the implementation, giving them flexibility in their text analysis. The research is about developing a promising method for text similarity analysis that combines the strengths of statistical and semantic approaches. The stated technique is demonstrated to get the mapping of Course Objective sentences and Programme Outcome sentences based on similar semantics as required by National Board of Accreditation.
OpenMarshall: Open-Set Recognition of Aircraft Marshalling Signals for Safe Docking

Aircraft marshallers provide visual docking guidance to the pilots to safely maneuver large aircraft while avoiding unseen obstacles. Although the marshallers are well-trained, there exists a huge gesture variance for the aircraft marshallers worldwide in performing a specific marshalling signal. It creates great difficulty for a pilot from a different geographical location to interpret the desired hand signal. Besides, poor visibility in adverse weather coupled with the high pilot sitting position in big aircraft increases the signal deciphering complexity. To tackle this, we propose a novel high-fidelity gesture-tracking model, OpenMarshall, which extracts salient body joints for marshalling and performs real-time classification of hand signals into a set of known marshalling signals and anomalous gestures with the help of a sequential learning unit. On one hand, our approach can help the Aircraft Marshallers to rectify gestures in their training phase, and on the other hand, vision-based automatic deciphered signals in the cockpit can reduce the workload for a pilot in the docking phase. Additionally, we augment substantial occlusion-simulating scenarios during OpenMarshall training to deal with heavy occlusions in airports. OpenMarshall is a lightweight and generic detector-tracker pipeline that makes it suitable to deploy in platform-agnostic configurations, such as autonomous UAVs, passenger jets, commercial aircraft, etc.
Certificate Authenticity Verification Using Hashgraph

Digitization has been an important driver of change and has influenced the mode of operation of universities. However, the certificates of graduates are still paper-based and do not fit into the employer’s digitized recruitment process. The digitization of certificates has long been postponed and has not adopted to the digitized process of employers and universities. Further research on this topic shows that multiple industries will benefit from a digitized process that prevents forged certificates from degrading the value of legitimate recipients and issuing institutions. This paper aims to present a systematic literature review of current distributed ledger technology-based solutions and propose a new type of digital certificate that is hashgraph based to prevent fraud and counterfeiting.
Detecting Anomalies in Industrial Control Systems with LSTM Neural Networks and UEBA

The increasing adoption of the Industrial Internet of Things and integration of operational technology with information technology networks have made industrial control systems (ICS) more vulnerable to cyber-attacks, which can cause severe consequences such as disruption of critical infrastructure, loss of data, and significant financial losses. To enhance the security and resilience of these systems, anomaly detection in ICS has gained significant attention in recent years. This paper introduces ongoing research focused on using Long Short-Term Memory (LSTM) neural networks for forecasting and subsequent anomaly detection over device logs. This approach involves User and Entity Behaviour Analytics (UEBA) to analyze and define entities of interest from a real industrial plant and extract a baseline behaviour model through features that are fed into the LSTM model for predicting future events and detecting anomalies. The proposed solution has the potential to provide real-time detection of cyber and physical threats, thereby enhancing the security and resilience of industrial control systems.
Adapting a Captive Portal for Phishing-Resistant Network Authentication Using Security Keys

As in other scenarios, most network authentication systems are based on passwords, which have been widely used during the last decades despite their security vulnerabilities. The WebAuthn standard published in 2019 has described a new authentication paradigm based on public-key authenticators, which are usually hardware devices known as security keys. Although WebAuthn is being used in many web authentication services, security keys have not yet been integrated with network authentication mechanisms. This work successfully developed and integrated a WebAuthn authentication server with a captive portal system, so users can be authenticated using security keys within a web-based captive portal network authentication system. This makes the results presented here are a first approach for adapting network authentication to this new authentication paradigm relying on security keys.
Blockchain In Healthcare Data

As a result of their centralization, most data management systems used in healthcare today have more points of failure and are therefore more susceptible to collapse in the event of major catastrophes. Privacy concerns, security hazards, a lack of patient control, and a lack of system openness are the key problems with traditional Health Information Exchange (HIE) systems. Blockchain technology may transform how information is currently exchanged in the healthcare sector. Blockchain is the most recent, destructive, decentralised automation that radically changes how data is handled in the healthcare industry and other connected sectors.By the use of smart contracts to offer access to approved clinicians, the proposed application enables patients to preserve control over their medical data. This guarantees that only those with permission can access the private medical information, thereby enhancing privacy and security. Our system provides an efficient and effective way to store and manage medical records while ensuring data integrity and confidentiality. By leveraging blockchain technology, our application enables patients to take control of their medical data and give access to authorized parties with ease, making it a valuable tool for both patients and healthcare providers.
Quantum technology infrastructures for cybersecurity research

This article describes the new disruptive panorama that quantum technologies bring, the quantum technology infrastructure that CESGA makes available to the research and academic community, and the relationship of these technologies with the field of cybersecurity. This is one of the fields that will change substantially because of the benefits and threats that these new advanced technologies bring.
Discovery of Unknown Classes in Constrained Semi-Supervised Learning

Supervised classification models have proved their significance in research and are utilized in many key areas. However, there are some limitations for these methods. In supervised learning, adequate labelled training data are required for each class. The learned classifier can classify only the data belonging to classes for which training data is available and lacks the ability to handle the data of unseen classes. However, in real time scenarios, there may not be adequate training data for all the classes. There could also be scenarios where the classes not available in the training data appearing in the testing data. The learner is expected to have the capability to uncover the class labels for the data belonging to unseen classes. To handle this situation, methods under different learning paradigms are proposed. In this work, various semi supervised learning techniques that can handle hidden classes(clusters) are evolved. Variant of Semi Supervised EM Based-K means and Naïve Bayes are discussed.
Web Bot Detection Using Mouse Movement

Non-Legitime traffic in terms of automated internet bot traffic is a long-standing problem causing a huge economic impact and lack of trust in companies and administrations worldwide. For years, Artificial Intelligence and especially Machine Learning have been key players fighting and helping the stakeholder to analyze and detect fraud instances automatically. However, it does not exist a reliable ground truth public dataset to evaluate and compare the proposed methodologies in the literature. In this ongoing study, a public dataset consisting of human and bad-bot web mouse movements extracted from real bot engines is being developed. When finished, it will be uploaded publicly. In addition, this dataset is evaluated using two Machine Learning models. The first is gradient boosting algorithm based on tree classifiers and the second a Recurrent Neural Network. Both models obtain excellent preliminary results.
Hybrid Student Performance Assessment System

The abundance of data in educational databases makes it increasingly difficult to assess students’ performance. After the COVID 19 epidemic, lessons were switched to blend mode, which combines traditional classroom instruction with online learning. There must be a system in place to evaluate students in the current scenario. Therefore, a redesign of the assessment system is necessary. The assessment system needs to considers the blend mode. This research paper proposed HSPAS (Hybrid Student Performance Assessment System) to assess the student performance and make recommendation to development of students learning. This approach contains three phases of assessment. The First one is the Foundational Assessment; second one is comprehensive assessment and third one is ID3 decision tree-based classification algorithm. The three-phase assessment improve the classification efficiency. The proposed approach is compared with the existing approaches. The experimental results show the efficiency of the proposed system.
OpenVuln Scanner with OpenVuln Security Framework

The research paper presents OpenVuln Scanner, a cutting-edge OSINT security tool that aims to streamline the process of performing security assessments for organizations. By combining the power of OSINT research and analysis with a carefully crafted vulnerability assessment framework, the tool provides a comprehensive solution for identifying and mitigating potential security threats. The proposed framework takes into consideration various security domains and provides a systematic approach to evaluating an organization's security posture. The tool is designed to be user-friendly and can be easily integrated into an organization's existing security infrastructure. With the aim of enhancing the overall security posture of organizations, the tool provides a valuable contribution to the field of cybersecurity.
Data Access Control in Cloud Computing

This paper presents a research project focusing on enhancing security features in cloud access through the utilization of AES (Advanced Encryption Standard) algorithms. The primary objective of this project is to ensure the easy accessibility of data and applications exclusively for authorized users. To address the prevalent privacy concerns in cloud access systems, we propose a novel distributed data access control mechanism that aims to safeguard user privacy. The first component of our approach introduces a groundbreaking method for achieving user anonymity, specifically targeting collusion scenarios involving untrustworthy authorities. The second component features a state-of-the-art policy obfuscation mechanism that effectively protects the confidentiality of decision attributes (metadata) from conspiring parties. Furthermore, we introduce a third-party authority system as the third component of our privacy enhancements, allowing authorities to log in and out of the system without overriding each other's permissions. Additionally, we propose a new activation feature and conduct a comparative analysis against popular activation features such as VM Orchestrator, VMware Sphere, and Ovirt, with the objective of enhancing system performance and security. Through this project, we aim to contribute to the ongoing efforts in strengthening the security framework of cloud access systems while ensuring user privacy.
Sentimental Analysis Of CBDC Tweets Using Machine Learning and Deep Learning Techniques

India is in digital spree, and it is forerunner in many streams. Central bank digital currency has been in many countries and it is under the process of testing in many nations. India had move one step ahead and have gone to pilot testing in recent time. Understanding public sentiments towards Central Bank Digital Currency by conducting sentiment analysis using tweets is the major objective of this research. The researchers have used the Twitter data to understand the public perception and sentiment towards digital currency using machine learning and deep learning techniques. The intention is to trace patterns and trends in the data to gain insights into the digital currency and to find the people reaction towards digital currency. The authors have utilized the natural language processing techniques and machine learning algorithms in Python to analyse the data and generate results.
Fusion of Two View Mammographic Texture Information Through Binary BAT Algorithm for Enhanced Breast Cancer Detection

Female breast cancer is a mortal disease. Every year its incidences and mortality rate increase globally. According to statistics studies, number of breast cancers estimated during 2010, 2015 and 2020 are 0.09, 1.06 and 0.123 million, respectively. Symptoms are not obvious in the beginning stage. Therefore, the early detection is difficult. Mammography is a gold standard for more than forty years and Cranio-Caudal and Medio-Lateral Oblique views are normally used in diagnosing breast diseases. These two views are results of scanning the breast in different angle. Thus, they provide some unique information in addition to the information common to both views. Hence combining the information of these two views for diagnosis may produce better results. To improve the detection performance, fusion of texture information from the images of these two views through Binary BAT algorithm is suggested in this paper. An improved performance metric was obtained using the proposed method.
The Conceptualization and Construction of an Automatic Tank Cleaning Robot

This study's objective is to find remedies for the issues that the current technique for removing sludge from tank bases has. It's a prototype robot that can autonomously clean tanks rather than requiring human labour. The prototype design includes the cleaning robot, water jet, suction pump, sensors, linear movement ladder, and rotating arrangement. This device is used to clean the tank's inside and the building's floor. This system is based on the linear slider technique, which is used to drop the robot into the tank, and the rotating arrangement, which is used to scrub the interior of the tank. Both of these methods were used to clean the tank's inside. The cleaning procedure is facilitated with an integrated cleaning brush and water jet. Finally, the sludge deposits and the dirty water are collected by the suction pump. The prototype's initial test will be cleaning household water tanks.
A Machine Learning Approach to Cryptosystem Security Detection

A platform for interactivity called content- based image recovery searches and manages massive picture collections using PC vision algorithms. More efficiently. There is an increasing need for gadgets and PC frameworks that support fast reading, searching, and recovery for image collections as a result of the growth of enormous, computerized image collections driven by rapid advancements in electronic capacity limit and registering power. Electronic data security has grown to be a major concern with a focus on sound advancement and ongoing forms of growth. Researchers will focus their efforts on daily modifications to current shows to overcome the shortcomings of energy security displays. However, a tiny number of recommended encryption calculations have recently been found to be questionable, posing serious hazards to sensitive data. A key tactic for thwarting such attacks is to use the most reliable encryption computation; however, which estimation is normally acceptable in a given situation depends on the type of data received. Regardless, comparing many cryptosystems on your own to determine which is the finest might be time- consuming. for a prompt and precise judgement on the most suitable encryption costs. To fulfil the requirements of picture content security, we suggest an Triple-DES block-based change computation for RDH. Furthermore, the proposed image content assurance method allows for direct image recovery and convolution on the substance assured images. As a result, not only is secure image storage and correspondence established, but calculating endeavors can be completely dispersed, making it a perfect analogy for today's well-known distributed computing innovation. According to security research, the proposed picture encryption scheme provides a certain level of safety from both a factual and computational standpoint. The test results also show that the proposed encryption area image recovery and convolution have a good presentation with a reasonable capacity overhead. Overall, this assessment shows a straightforward and helpful technique for looking at disconnected images on computers, and it acts as a foundation for future frameworks for recovering connected images that are substance-based and employed for similar objectives.
A Survey on Bitcoin Deanonymization

Blockchain has become a technology and business disruptor with a significant potential for both use and misuse. The distributed architecture combined with the integrity assurance given by the hash as well as the anonymity provided because of the use of identity derived from the public key has made this a revolutionary combination. The positive popularity is because of the anonymity it provides to the transactions and the same is also the reason that regulators, governments, and law enforcement authorities the world over are genuinely concerned about it. Hence it is essential for anyone who wants to track this technology to understand the depth of anonymity it provides. In this paper, we survey, analyse and present the methods by which it is possible to deanonymize and reveal the identity of interesting entities of bitcoin transactions.