Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Minority Oversampling and Random Forest Technique

In an era of digitalization, the usage of credit card is rapidly increasing and as a result, the cases associated with credit card fraud are on raise. So, fraud detection has become an important instrument and, in many cases, the best strategy to prevent fraud. Among the existing techniques, Machine Learning (ML) is crucial in detecting fraud. In this paper, ML algorithms like Decision Tree (DT), k Nearest Neighbors (KNN), Logistic Regression (LR) and Random Forest (RF) were analyzed and it is inferred that the RF algorithm works best on performance measures like precision, accuracy, recall, Matthews Correlation Coefficient, F-1 score. This paper also proposes to detect 100% fraud transactions while reducing false negatives by using SMOTE (oversampling technique) in combination with Random Forest algorithm.
A UAV Mission Planning With Limited Fuel Capacity

Unmanned Aerial Vehicles (UAVs), also referred to as drones, are now being used intensively in many applications including surveillance missions, traffic monitoring, agriculture or disaster and crisis management, since they avoid risking human lives while their manageability permits reaching areas that are hard to access. These vehicles are usually controlled by a number of operators inside one or more Ground Control Stations (GCSs), depending on the size of the mission. This paper presents two new formulations for a single UAV mission planning with limited fuel capacity in a given environment. The first is to obtain the optimal minimum fuel consumption trajectories to accomplish a mission. The second gives the minimum fuel tank capacity to achieve the same mission. A mission starts from a starting point, which is assigned by the ground control station (GCS), from and to which a UAV starts and ends its mission. The mission then continues by visiting a set of tactically located way-points. Due to limited fuel capacity, the UAV has to reconfigure its mission routes among the given set of way-points and an assigned aerial fuel station to successfully achieve the mission. The proposed formulations optimal results are simulated and compared together and also compared with the results of the case where the fuel capacity is not limited to elaborate the difference between these scenarios and clarify the importance of the limited fuel capacity problem and its effect on mission planning.
Multiple Multicast Architecture for XMPP based Applications

Extensible Messaging Presence Protocol (XMPP) is a real-time information transmission specification protocol that is based on IP technology and Extensible Markup Language supporting publish/subscribe messaging systems. Multicast is the operation that is carried out by a publisher in a publisher-subscriber scenario such that it simultaneously sends data to multiple subscribers. There are many XMPP Extension Protocols (XEPs) available in the literature, that enable the use of XMPP for multicast. This network is made up of publishers who publish messages to multiple subscribers, some of whom may overlap. This is a case of Multiple Multicast (MMC). In this paper, a novel architecture to extend XMPP for multiple multicast has been proposed to cater multiple multicast in a publish subscribe network. In the literature, it was found that multiple multicast was not extensively considered in a mobile, publish subscribe network, hence the architecture proposed to cater to the multiple multicast nature for data exchange between multiple publishers to multiple groups of subscribers. In the considered network of subscribers in publish subscribe scenario, cluster heads help in forwarding the data to nodes connected to it and a cloud storage is used to ensure that message is successfully delivered to all the subscribers from publishers by creating a hierarchical structure to the network. Multilevel cache ensures that overloading does not happen at any cache memory. This paper attempts to reduce the number of replications and other overheads incurred by these replications. Performing multicast dramatically reduces network traffic by delivering a single stream to multiple receivers.
POS-based Classification and Derivation of Kannada Stop-words using English Parallel Corpus

For the retrieval of information from different sources and formats, pre-processing of the collected information is the most important task. The process of Stop-Word elimination is one such part of the pre-processing phase. This paper presents, for the first time, the list of stop words, stop stems and stop lemmas for Malayalam language of India. Initially, a corpus of Malayalam languages was created. The total count of words in the corpus was more than 21 million out of which approximately 0.33 million were unique words. This was processed to yield a total of 153 Stop-words. Stemming was possible for 20 words and lemmatization could be done for 25 words only. The final refined stop word list consists of 123 Stop-words. Malayalam is a widely spoken language by people living in India and many other parts of the world. The results presented here are bound to be used by any NLP activity for this language.
Blockchain-based Intellectual Property Management Using Smart Contracts

Smart contracts are an attractive aspect of blockchain technology. A smart contract is a piece of executable code that runs on top of the blockchain and is used to facilitate, execute, and enforce agreements between untrustworthy parties without the need for a third party. This paper offers a review of the literature on smart contract applications in intellectual property management. The goal is to look at technology advancements and smart contract deployment in this area. The theoretical foundation of many papers published in recent years is used as a source of theoretical and implementation research for this purpose. According to the literature review we conducted, smart contracts function automatically, control, or document legally significant events and activities in line with the contract agreement's terms. This is a relatively new technology that is projected to deliver solutions for trust, security, and transparency across a variety of areas. An exploratory strategy was used to perform this literature review.
An Expert System for Classification and Detection of Improper Posture

Posture is something that is often neglected when a person is focused on said activity and hence there are several Artificial Intelligence based systems that have been developed to intelligently detect a poor posture and immediately alert the user. This paper presents an exhaustive comparative analysis of posture detection and recognition models such as accelerometer sensor based, Kinect sensor based and computer vision-based models that are currently available. The powers of AI to detect the posture are also reviewed. Further, the paper proposes three different posture recognition systems that performed better than the models that currently exist. They also overcome several limitations faced by the current systems. The proposed systems used four classification algorithms which were SVM, KNN, Random Forest and Decision Tree. Of these, SVM was found to be the best performing algorithm for the first system which yielded an accuracy of 99.87%. The second gave an accuracy of 100% for all the four classification algorithms. Third system gave an accuracy of 100% for SVM, KNN and Random Forest. It performed with an accuracy of 99.3% for decision tree classifier. This paper also highlights the use of the Grid Search tuning method to obtain the optimum hyperparameters for each classification algorithm.
An Overview of Text Representation Techniques in Text Classification using Deep Learning Models

Organizations handling huge amount of data needs to preserve privacy of the documents. Every customer has the rights to ask for privacy of their documents. These documents can be classified into different categories like private, public, confidential, etc. Using suitable text classification methods, documents can be classified into different categories. Various approaches like machine learning (ML), deep learning (DL), natural language processing (NLP) is available. Machine learning algorithms are showing acceptable performance but doesn’t work well when data grows in size. In this paper, convolutional neural network(CNN) which is a deep learning model, is used to classify documents into different categories. Deep learning models are beneficial over machine learning models in terms of performance and volume of data to be classified. The performance of the model is evaluated and obtained acceptable performance.
Comparative analysis of MOSFET and FINFET DRAM N*N Array

Memory is a means of storing information in the form of ones and zeros. Every digital device, today, has a significant amount of memory embedded inside. Memories, it is believed, take up lot of the chip area. DRAM is a specific type of RAM (random access memory) that stands for "dynamic random-access memory". All types of modern desktops, laptops and mobile workstations require some kind of RAM to function, and DRAM is one kind of RAM that is generally seen in those. These are called "volatile" because of the fact that transistors need electricity to work and anything stored will disappear if the PC is turned off. The 4*4 DRAM memory has been designed, implemented and analysed using gpdk090 technology library using the cadence tool.
Human Fall Detection in Surveillance Videos

The development of healthy aging and the assistance of older people in their everyday activities is one of the most pressing concerns in modern cultures. Human fall is such an irregular action that occurs when a person gets startled and loses their equilibrium, resulting in sliding. These accidents among old people can result in long-term impairments and even death. Automated detection of falls has gotten a lot of interest in computer vision and pattern recognition areas because of its social and economic implications. Although wearable sensor-based techniques have achieved excellent detection rates, some potential users are hesitant to wear them, and so their usage has not yet become standard. As a result, new ways have arisen, such as vision-based solutions. Fall detection that is accurate for the help of older persons is critical in reducing the number of fatalities and injuries caused by falls. The influence of deep learning on vision-based systems, such as action recognition, has transformed the landscape. The emergence of Smart Environments and Internet of Things concepts, together with the growing number of cameras in our everyday lives, creates an ideal setting for vision-based solutions. As a result, this paper aims to provide a vision-based method that uses Convolutional Neural Networks to determine if a series of frames involves a fall event or not. The proposed approach is using Histogram of Oriented Gradient (HOG) features extracted from series of frame to determine the fall event.
Performance Analysis of Visual Cryptography and Watermarking Algorithms

Images are a common source of data transfer in numerous domains. They can be a source for transferring confidential information across various mediums. As this practice has been adopted on a large scale, securing the images become a major important aspect. Our paper here is an attempt to aid the security of images and also to reduce the illegal tampering of images while transferring them. We aim to provide algorithms which are robust and imperceptible that help in enhancing the security of images and also provide an analysis of those algorithms. A technique for digital image watermarking and kn sharing is presented in this paper. Digital Image Watermarking is accomplished using functions such as the wavelet transform and cosine transform. Given an image as the input signal, the watermarks would be used to verify the image's validity. The scheme is resistant against certain threats and normal image processing actions. In the case of KN Secret Sharing a user-supplied key is used to encrypt the input image. Using the K out of N technique, the given image is further divided into N separate shares. Although these N shares can be distributed, the end user only requires K of them to produce the original image. The original image is still encrypted after it is recreated. The recreated encrypted image can be decrypted and the required image obtained using the authenticated key. Watermarking finds its applications in forensics and numerous such domains. Etc. KN Sharing can be used in Distributed systems and also in Data transfer systems.
BoundNet: Pixel Level Boundary Marking and Tracking of Instance Video Objects

Object segmentation is the inevitable result of object recognition and semantic segmentation when moving from coarse to fine inference. Despite many efforts in segmenting instances, the quality of masks remains unsatisfactory. Because of the low pixel-to-edge ratio and the restricted spatial resolution of the feature map, the boundaries of the predicted version masks are typically inaccurate. Defining the object boundaries at the detailed pixel level is essential for instance segmentation. This paper aims a simple, yet conceptually effective, mask-based RCNN framework to generate accurate segmentation masks to overcome these challenges. Additionally, it can extract the foreground objects from the background and run at high inference speeds to meet the growing demand for accurate, real-time computer vision applications. This study introduces a new database called RSBFRS that includes unconstrained images and videos. Thus instance segmentation using Mask RCNN was implemented to mark/extract precise boundaries of moving person in a video and the maximum prediction score value of 0.9983 obtained. This efficient technique can serve as a basic support for future studies on instance-level detection and recognition.
Comparative Analysis Of Optimization Techniques On Multi-class SVM

Support Vector Machine is the most efficient algorithm in machine learning for classification problems. The data generated these days requires Multi-classification and SVM’s are used for doing the same. To improve the performance and reduce the time complexity of the multi-class SVM, different optimization techniques have been used. This project aims to compare these optimization techniques on SVM on different parameters like accuracy and time. The comparative analysis has been carried out on optimization techniques which are the current state-of-art; Sequential Minimum Optimization and Dual Decomposition.
Multi Speaker Recognition and User-specific Answering system

In our everyday life, we associate and run over voice aides that react to queries that are either speaker-autonomous or connected with the individual who is signed into the system. The most common way of approving the personality of the individual to give admittance to the system by utilizing discourse/voice features is called Speaker Recognition. A significant amount of computation in speaker recognition arises from the likelihood and distance computations of the feature vectors of known and unknown speakers; their dimensionality and the complexity of the speaker models help in determining the identification time. We plan to create a text-independent multi-speaker recognition system to remember each speaker and perform specific tasks for them. This will be performed in a noisy environment when it could be hard to identify the speaker. The proposed methods include Gaussian mixture models (GMM), vector quantization, frequency estimation, MFCC, template matching, neural networks, Support Vector Machine (SVM), etc.
A Compression Framework for Reducing Cost of Communication in a Federated Learning setup on a Non-IID Dataset

Federated Learning is a novel technology that aims to facilitate the training of ML models using the collaborative power of all its users by allowing them to download the models and train them locally. This enables the clients to maintain privacy while contributing to the global training of the model. Due to the increasingly complex nature of modern ML models, a lot of data is generated that leads to heavy loads on the network hosting the system. In this paper, we discuss a method to significantly reduce communication costs by reconstructing the generated updates using sparsification to eliminate the lower bound values, k means clustering to reduce the entire dataset to a discrete set of values and then encoding them. The proposed compression methodologies will be integrated with LEAF which is a pre-existing framework used to simulate a real-world federated setting with multiple clients and an aggregating server. The data resulting from the compression framework when compared to its parent data indicates a reduction in size by 98%
An Optimal Travel Planning Solution Using The Amalgamation of Traveling Salesman Problem and Multiple Linear Regression

With access to various modes of transportation and transport becoming more affordable, travel and tourism have seen a tremendous spike in the years before 2020. We estimate that travel and tourism will surpass their momentum anytime soon in the near future. This project aims towards a planning solution that keeps in mind time and fare. Our project will calculate the shortest distance between each of the user’s chosen locations, as well as the return route, and then recommend the best and most efficient order in which to visit them. This information would also decrease the time it would otherwise take. We will find the most efficient travel and return route in our project through a backtracking solution to Enhanced Travelling Salesman Algorithm. We will further use Multiple Linear Regression to find the cost estimate for this shortest distance. Hence, through our project, we provide users with a complete travel planning solution that considers time, distance, and fare.
An Automated System for Identification of Tweets Requiring Customer Service Concern

Social Network Communication has become an important factor for businesses for their successful operation. It has become important to connect with customers and offer support to them on these platforms. Several customers prefer to post comments, suggestions or complaints about a company’s products and services to online media such as Facebook, Twitter etc. because it is convenient for them and it increases pressure on product owners/service providers to respond. Twitter is a prominent social media platform with 206 million daily active users. Research from Twitter and Sprinklr suggest that 2 out of 3 people prefer Twitter for customer service over other social media platforms. It pays off for businesses when they attend to customer concerns or social media since studies show that people who receive customer service on Twitter from telecom brands preferred those brands 3 times more than those that had not received support. Yet many businesses aren’t connecting with consumers on Twitter. In this paper two unsupervised classification models are proposed that performs clustering on customer support dataset on twitter in such a way that companies can segregate those messages/tweets in which customers are needing genuine help. This will help the companies to actively respond to these customers and this can help increase their customer satisfaction.
Anonymity-Enabled Communication Channels: Attacks and Defense Methods

Within information security, anonymous communication has gained momentum and helps to achieve a quantum leap in privacy due to its unique properties like anonymity, un-traceability, un-observability, and pseudonymity. Although end-to-end encryption guarantees the security of the data, it is not able to secure the metadata within the communication channel thereby putting the privacy of the users to be vulnerable. An overview of anonymous communication channels has been delineated in this research work, starting from Chaum’s mix network, DC-nets, onion routing, and other anonymous routing protocols. Although anonymous communication channels are becoming a hot topic to preserve security, the attacks on these channels are endless. To break down this anonymity, eavesdropper uses different types of attacking methods from time to time. This research provides the overall survey of the attacks on anonymous communication channels. These different types of attacks have been analyzed. To overcome these attacks, various measures have been provided over time. We have analyzed these attacks along with their different defense countermeasures. The classical description of the attacks together with their defense methods are given.
Analyzing Major Indian Protests using Nature Inspired Algorithms

The rates at which strikes, riots and protests are increasing these days is alarming and a matter of grave concern. In order to understand the intensity of a protest and eventually take steps in order to minimize massive possible damage, it is critical to analyze the sentiments towards that particular event. Previous works in this field have majorly used traditional machine learning approaches. However, we believe that incorporating nature-inspired algorithms along with BERT for feature selection can greatly improve the accuracy. In this work, we have proposed a novel approach wherein we use an amalgamation of BERT and nature-inspired algorithms to perform sentiment analysis of major protests in India. For this, we combine the feature extraction capabilities of BERT with both swarm intelligence as well as evolutionary algorithms. For comparative study, we have taken a model which only uses BERT along with KNN and compared it with models which use nature inspired algorithms as an additional step to perform feature selection. We have achieved remarkable improvement in accuracy by incorporating nature inspired algorithms with the highest improvement of 17.78% in classifying sentiments by using the Grey-Wolf optimizer. Significant improvements in accuracy were also observed by using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO) and Genetic Algorithm (GA). Snscrape has been used to extract tweets of major protests in India. Our results accurately correspond to the actual intensity of devastation that was observed during those protests.
A Method Executing Optical Real-Valued Matrix Multiplication

Photonic integrated circuits have potential to advance artificial neural networks by providing ultra-fast computing capabilities with high power efficiency. In present conventional optical neural network architectures, information is encoded in light amplitude, meaning that the light intensity and phase represent the absolute value and the sign of the data, respectively. Since photodetectors are only sensitive to light intensities, the signs of the real-valued results are lost and fail to be recovered in present solutions. In this work, we propose a novel decomposition approach to overcome the issue of sign-loss, requiring no reference signal. The proposed designs can easily scale to multi-layer neural networks by cascading linear and nonlinear units. Our design needs no electronic digital signal processing, avoiding repeated bidirectional optical-electronic signal conversion between layers. We simulate the fidelities in real-valued matrix vector multiplication tasks with fabrication errors. The numerical results show that decomposed approach effectively tackled the calculation mistake issue caused by sign-loss. Our work presents a promising method towards anti-sign-loss and ensure computational accuracy.