Reviewerly/exHarmony huggingface dataset:
Unraveling Campus Placement Success Integrating Exploratory Insights with Predictive Machine Learning Models

The dynamics of campus placements have garnered considerable attention in recent years, with educational institutions, students, and employers all keenly invested in understanding the factors that drive successful recruitment. This surge in interest stems from the potential implications for academic curricula, student preparation, and hiring strategies. In this study, we aimed to unravel the myriad factors that influence a student's placement success, drawing from a comprehensive dataset detailing a range of academic and demographic attributes. Our methodology combined thorough exploratory data analysis with advanced predictive modeling. The exploratory phase unveiled notable patterns, particularly highlighting the roles of gender, academic performance analysis, Degree and MBA specialization in placement outcomes. In the predictive modeling phase, the spotlight was on state-of-the-art machine learning models, with a particular emphasis on their capacity to forecast placement success. Notably, algorithms like Logistic Regression and Support Vector Machines not only confirmed the insights from our exploratory analysis but also showcased remarkable predictive prowess, with accuracy scores nearing perfection. These findings not only demonstrate the capabilities of machine learning in the academic and recruitment spheres but also emphasize the enduring importance of core academic achievements in influencing placement outcomes. As a prospective direction, future research might benefit from examining how placement trends evolve over time and integrating qualitative insights to provide a holistic view of the campus recruitment process.
Satellite Image Segmentation Using Federated Learning: A Privacy Preserving Approach

Satellite images are images captured by satellites orbiting the Earth. They provide valuable information about the Earth's surface, including land cover, vegetation, urban areas, and more. Image segmentation is particularly useful when analysing satellite images because it allows researchers and analysts to extract meaningful information from the images. By segmenting the image into different regions, it becomes easier to identify and classify various features such as buildings, roads, water bodies, forests, and agricultural land. Satellite images can raise privacy concerns due to their ability to capture detailed information about the Earth's surface, including private properties, sensitive locations, and personal activities. In this paper, a federated learning approach is incorporated to perform semantic image segmentation tasks on satellite images. This would attempt in overcoming the privacy issue and reducing the communication load without compromising the overall performance of the model. The results obtained show that the federated learning approach is performing better than the conventional deep learning approach with a loss value of 0.0503 and 0.255 on two different datasets.
An Expert System for the Diagnosis of Livestock Poisoning

In recent years, artificial intelligence systems have become increasingly attractive for use in agriculture. Diagnostic expert systems are designed to determine the nature of various deviations from the usual course of biological or technical processes based on a set of symptoms. Although the first diagnostic expert systems appeared in the late 60s and early 70s of the 20th century, diagnostics remains an interesting domain for artificial intelligence. In this paper, we present an expert system that aims to assist veterinarians and farmers in the diagnosis of livestock poisoning. Our preliminary studies show that due to the wide variety of poisonings and their symptoms, the diagnosis of poisonings is a particularly difficult task, and providing such a system would be useful. Furthermore, the system architecture is presented and its use is demonstrated by an example in this paper. The system is implemented by Flex and VisiRule development tools.
Tiny Entropy Based Image Compression

This paper introduces the OSCAR2023 model, designed for constrained applications where computational efficiency is paramount. OSCAR2023 considers compression ratio, image quality, and computational cost. It aims to achieve a delicate balance between compression performance and computational requirements, much like the simplicity and high compression rates that made JPEG dominant at the time.
Deepfake as an Artificial Intelligence tool for VFX Films

Deepfake refers to the technique of replacing human bodies in movie scenes through the utilization of a method called Deepfake, which uses AI's (Artificial Intelligence) most recent advancements. Since we cannot see the reality of these shots, filmmakers use this as an opportunity to express their creativity. In order to advance VFX production, Deepfake is combined with AI. We have discussed the potential impact of Deepfake in future in this study. Students at a university were exposed to Deepfake footage and engaged in a questionnaire-based poll to learn more about the software's value for VFX. Additionally, the Deepfake approach, like Facial Landmarks, Voice Features, Body Movements, Background and Lighting that was employed in two films, has been elaborated. Results have shown that using AI's Deepfake technology improves the calibre of VFX film production and increases box office success.
Automated Resume Parsing: A Natural Language Processing Approach

The extraction of critical information from resumes, such as contact information, skills, education, and job experience, requires the use of resume parsing. In this work, we propose a resume parser that integrates two methodologies: a Named Entity Recognition (NER) model approach and a Keyword and Pattern Matching model approach using Regular Expressions (Regex), utilizing certain NLP libraries and methods. The NER model makes use of NLP libraries to precisely recognize and categorize named entities-such as names, phone numbers, and email addresses-in the resume text. In order to provide a thorough profile overview, it also organizes the parts on talents, education, and job experience. In addition, the Keyword and Pattern Matching model makes use of Regex and pre-established rules to extract certain information, such job titles, firm names, and years of experience. Our resume parser uses NLP-based approaches to increase accuracy and performance, allowing it to handle various resume formats and deliver trustworthy results. Performance assessments show how well the parser extracts crucial information, even from resumes with various layouts and formats. Our resume parser seems to be a useful tool for processing huge numbers of resumes in real-world applications due to its use of NLP libraries and methodologies, together with excellent accuracy and processing speed.
To Detect and Mitigate the Risk in Continuous Integration and Continues Deployments (CI/CD) Pipelines in Supply Chain Using Snyk tool

Continuous Integration/Continuous Deployment (CI/CD) is an approach of software development that enables teams to automate the build, testing, and deployment of applications, resulting in faster and more efficient software delivery. However, the CI/CD pipeline can become a potential target for vulnerabilities and attacks, leading to severe consequences for the software development process and the overall security of the system. In addition to introducing CI/CD, this study also looks at potential pipeline vulnerabilities. It describes many attack vectors that can jeopardise the software's availability, confidentiality, and integrity, such as malicious code injection, unauthorised access, and exploitation of misconfigurations. In the CI/CD pipeline, cutting-edge there are few security techniques have been used to mitigate these vulnerabilities. Tools that are mainly used in this study include CodeQL, SLSA, and Snyk.
Virtual Assistant Using Deep Learning Techniques

An application programme known as a virtual assistant, is as an AI based digital assistant, that can recognize natural language voice commands and carry out the user's requests. The goal of this work is to use deep learning to build a thorough virtual assistant for the Core Windows platform. The purpose is to solve the lack of an appropriate virtual assistant for Windows users, especially in scenarios with slow internet or server problems. The suggested virtual assistant seeks to provide offline capability, improve natural language interpretation, and optimize performance on Windows devices by utilizing recurrent neural networks, transformer topologies, and pre-trained language models. This study helps the Windows ecosystem's virtual assistants reach more audiences and enhance user experience. solution for the lack of an appropriate virtual assistant for Windows users. By utilizing recurrent neural networks, transformer topologies, and pre-trained language models, the proposed virtual assistant offers offline capability, enhanced natural language interpretation, and optimized performance on Windows devices. These advancements aim to reach a broader audience and improve the user experience within the Windows ecosystem.
Integrating IoT and Blockchain-Enabled Device-to-Device Communication to Improve Privacy in Intelligent Quarantine Environments

The exponential progress of Internet of Things (IoT) technology has resulted in the emergence of groundbreaking solutions across diverse industries, such as healthcare and quarantine management. In the realm of global health crises, such as the ongoing COVID-19 pandemic, the successful execution of efficient quarantine protocols assumes paramount importance. The objective of this research paper is to examine the potential integration of Internet of Things (IoT) devices, wireless sensors, and blockchain technology in order to establish a Smart Quarantine Environment (SQE). The SQE aims to improve privacy, security, and efficiency in the management of quarantine protocols. By analyzing the existing literature and exploring relevant case studies, this paper seeks to provide insights into the feasibility and benefits of implementing such a system. The utilization of IoT devices and wireless sensors enables the facilitation of real-time monitoring and data collection. This approach is supported by the integration of blockchain technology, which ensures data integrity, transparency, and user control. By addressing concerns related to privacy and security, this combination of technologies enhances the overall effectiveness of the system.
NLP Based Hate Speech Detection And Moderation

As Social media penetration increases in day-to-day life so does the growth of hate speech. After 2016 due to affordability of internet many user on boarded the internet which not only increased social media interactions but also growth of hate speech. be it religion phobia, homophobia, gender, toxicity etc. To control hate speech, NLP based machine learning model has been proposed. The proposed model uses TFIDF feature generation method to which binarized naive bayes is applied to calculate maximum likelihood feature for each labels. Model has been trained with logistic regression on the maximum likelihood feature. which helps to classify hate speech and moderate accordingly. The proposed model produces an overall accuracy of 83 percent and able to achieve 5 percent improvement compared to multinomial naive bayes' production in identifying hate speech and classifying it under various labels.
Some Applications of Quantum Information Theory

The need for security has been gradually increasing in the era of technology. Secure and solid trade of data between gadgets is vital for any organization in the ongoing computerized world. Cryptographic strategies are utilized to guarantee the protected transmission of information, guaranteeing the client's security by putting away and communicating information in a specific organization. Utilizing encryption, just the expected client having the key can get to the data. During information or on the other hand fundamental transmission, the channel ought to be gotten by utilizing vigorous encryption methods. This paper helps us to understand the mechanism of Shor's algorithm in a simple manner. The initial part of the paper gives us a quick introduction about the unitary gates used. The paper further explains about the importance of the estimation of quantum phase estimation along with quantum Fourier transform. It is also utilized to reduce factorization problem to order finding problem. This paper contains detailed explanation about factoring a number using shors algorithm.
TicketTrace: Intelligent High Parity Ticket Detection Through Deep Learning Techniques

Bug reports play a crucial role in software development by facilitating communication between users and developers and aiding in the resolution of software defects. However, the presence of duplicate bug reports can lead to inefficiencies in the bug tracking process, resulting in wasted resources and delayed issue resolution. This paper propose a novel approach for detecting high parity bug reports using deep learning techniques. This paper's approach leverages the power of deep neural networks to automatically identify similarities and patterns within bug report data, the bug report that have used to train is taken from multiple software projects. Model is trained on a carefully curated dataset of bug reports, consisting of textual information such as titles and descriptions. The heart of this papers solution lies in the architecture of Siamese deep learning model. The trained siamese network is then used to take new inputs tickets and compare them with existing tickets to detect high parity ones. All the implementation uses open source python packages such as tensorflow, keras and transformers to build the neural network. In the evaluation phase, the performance of proposed model is measured using established metrics, including precision, recall and F1-score. The empirical findings illustrate that the utilization of deep learning techniques led to an accuracy of 79 and 81 percent for two distinct testsets. Additionally elaborate on the constraints of the paper's methodology and discuss potential enhancements for the model implemented.
A Blockchain Based Sustainable Digital Insurance Business Parametric Solution Architecture to Protect Realtime QSR Business Interruption Losses

An insurance policy will reimburse the company if something bad happens to something that is covered under the policy to bring sustainability to the business. The process of settling a dispute can take a long time because there are many people involved. Parametric insurance is different from traditional insurance. Traditional insurance is a type of insurance in which a company pays out a certain amount of money, no matter what happens. Parametric insurance is designed to protect policyholders from certain events, by paying out a set amount depending on the severity of the event. In the perishable food and quick service restaurant sector, disruptions caused by delays are one of the types of events that parametric insurance may be able to cover. Supply chain delays can happen for several reasons. Events like adverse weather, heavy traffic, and poor road conditions can all slow down the supply chain of perishable food items and groceries. On the other side for smaller QSRs, footfall may be reduced drastically on specific conditions. Parametric-based insurance starts processing claims when a certain event takes place. Insurance claims may be processed automatically by the terms and conditions provided in the policy definitions, and delays that result in undesired scenarios may be insured. Building parametric insurance policies using the conventional centralized database approach is possible, but there are trust difficulties because the data is vulnerable to manipulation. This study explores the approach to the issue and the software architecture of the dependable blockchain-based digital technologies supporting parametric perishable food and QSR supply chain insurance. We will go into detail about the user experience, technological solutions, deployment issues, and insurance for parametric services. The proof of concept for parametric insurance was completed, and the resulting blueprint may be applied to more use cases in adjacent industries.
Quantum Network for Quantum Communication

Quantum networks represent a paradigm shift in communication because they harness the principles of quantum physics to make data transfer efficient and safe. This study presents a thorough examination of quantum networks, including their fundamental concepts, potential applications, and challenges. The fundamental concepts of quantum physics are first introduced, with a focus on superposition and entanglement as the two key elements that underlie quantum networking. Quantum nodes, channels, and repeaters—the structure and components of a quantum network—are discussed, with an emphasis on how they support long-distance quantum communication. By outlining the advantages and disadvantages of quantum networks, this paper aims to contribute to the growth of this revolutionary subject and to encourage additional research and innovation.
Exploring Multi-Model Sentiment Analysis: A Conditional-Value System

This paper presents a detailed comparative study conducted on five distinctive language models with the objective to classify sentiments embedded in a dataset of 10,000 reviews. Each model allocated sentiment scores of −1, 0, or 1, representing negative, neutral, or positive sentiments respectively. The intricacies of the study became evident when varying models assigned divergent classifications. In response to these discrepancies, we introduced a rule-based methodology, assigning a ‘Conditional-Value’ to each data entry, with an average score of 0.3326 across the dataset. Our study's results underscored the dependability of the ‘Cardiffnlp-label’ model, particularly in instances of non-consensus among models. The rule-based process, employed to ascertain ‘Conditional-Value’, offers a robust groundwork for subsequent studies in the realm of multi-model sentiment analysis. Moreover, the findings of this study underscore the potential advantages of developing a composite model, leveraging the unique strengths of varied models to augment precision in sentiment categorization. Despite the inherent intricacies entwined in sentiment analysis, this scholarly inquiry highlights the encouraging avenues for subsequent investigations and progress in this continually shifting and advancing discipline.
Smart Document Classifier and Analyzer Using NLP

Circulars are the primary mode of communication in an established hierarchy. The Vishweshvaraya Technological University releases around 35–40 circulars every month which averages to around 450 annually. RVCE administration themselves generate up to 15–20 circulars on a monthly basis. It is tedious for an individual to keep track of the large flow of crucial information. A machine learning model thus becomes necessary to meticulously scan the documents, classify the information and present the essence in a brief manner. This paper presents a machine learning model developed in Python that is capable of classifying announcements given by the college into four categories: fees, exams, holidays, and events. The classification of the documents are carried out using the SVM algorithm. Using computer vision algorithms, crucial data such as the circular id, date of issue, authority signatures, and tabular data are retrieved. All of this information is placed into a template to produce a brief, easy-to-read notification. To automate the transmission of these templates to their appropriate stakeholders, a mail bot is created. The model achieved an accuracy of 98.2%. The results clearly reflect that the model can accurately classify announcements into the four categories with a high degree of reliability.
Digital Assistant Robot for Academic Fraternity Using Deep Learning

Robots, autonomous machines capable of executing a multitude of tasks with little to no human intervention, have found applications across diverse fields such as manufacturing, healthcare, transportation, and notably, education. The current era, characterized by rapid technological advancements, has seen robots become increasingly instrumental in education, yet there remains substantial potential for further enhancement. As technology continues to evolve, there is a growing interest in integrating robots into educational settings. This paper presents a transformative innovation in educational technology through the introduction of the Digital Assistant Robot (ERO). Engineered specifically for the academic fraternity, ERO seamlessly integrates human touch with artificial intelligence to enhance the classroom experience while recognizing the invaluable role of educators. This pioneering prototype leverages cutting-edge technologies to provide personalized support and guidance to both teachers and students. By automating administrative tasks and serving as an interactive learning companion, ERO aims to optimize instructional time, foster student engagement, and promote a positive learning environment. The innovative capabilities of ERO hold immense promise for transforming the way education is delivered and experienced. ERO's key feature is its advanced face recognition system, powered by OpenAI's CLIP model, enabling automated attendance management. By combining innovative hardware design, advanced software modules, and user-friendly interfaces, The proposed robot represents a significant step towards enhancing engagement, efficiency, and inter activity in the classroom. The findings from this study contribute to the growing body of knowledge on the application of robotics in education and provide insights into the successful integration of digital assistants in academic settings.
A Proactive Approach to Advanced Cyber Threat Hunting

Organizations strive to secure their valuable data and minimise potential damages, recognising that critical operations are susceptible to attacks. This research paper seeks to elucidate the concept of proactive cyber threat hunting. The proposed framework is to help organisations check their preparedness against upcoming threats and their probable mitigation plan. While traditional threat detection methods have been implemented, they often need to address the evolving landscape of advanced cyber threats. Organisations must adopt proactive threat-hunting strategies to safeguard business operations and identify and mitigate unknown or undetected network threats. This research proposes a conceptual model based on a review of the literature. The proposed framework will help the organisation recover from the attack. As the recovery time is less, the financial loss for the company will also be reduced. Also, the attacker might need more time to gather data, so there will be less stealing of confidential information. Cybersecurity companies use proactive cyber defence strategies to reduce an attacker's time on the network. The different frameworks used are SANS, MITRE, Hunting ELK, Logstash, Digital Kill Chain, Model in Diamonds, and NIST Framework for Cybersecurity, which proposes a proactive approach. It is beneficial for the defensive security team to assess their capabilities to defend against Advanced Threats Persistent (ATP) and a wide range of attack vectors.