Reviewerly/exHarmony huggingface dataset:
Machine Learning Techniques for The Detection of Imposters in Credit Card Fraudulence

Identification of credit card fraud is the most common problem in the current world. We attempted to identify credit card fraud using algorithms that applied machine learning techniques. We use the algorithms of Decision Tree, Random Forest, and Extreme Gradient Boosting. The effectiveness of the model can be evaluated using a selection of some intimately available data. The analysis of a financial institution’s real-world credit card data group follows. Moreover, new noise is injected into the data samples to further evaluate the systems’ continuity. The study’s first method is crucial since it creates a tree against user behavior that may be used to identify fraud. As a backup plan, a user-effort-supported forest will be built, and it will be attempted to use this forest to identify the suspect. The analysis’s results conclusively show that the common optional system reliably and accurately detects instances of credit card theft.
Facial Disease Progonstication using Deep Learning Technique

Due to debates on the correlation between illness and the face that go back thousands of years, facial diagnosis has become widespread. The objective of this study is to see whether deep learning techniques can identify diseases from unrestricted 2D face pictures. Introducing deep learning for facialskin related diseases have completed successfully computer-aided facial diagnosis for five distinct illnesses with relatively little available dataset. In reality, Getting pictures of face-skin illnesses is challenging, costly, and tedious, and it comes with ethical considerations because it entails managing personal data. As a result, the datasets used in face diagnostic research are frequently small and private in comparison to those used in other machine learning application sectors. A non-invasive, low-cost solution for disease screening may be made available by the success of deep-learning applications in face diagnostics on a small dataset. Comparing three deep learning technique to execute on the dataset to obtain better result from better technique on face-skin disease dataset. In the test dataset of images the best model achieves accuracy of 0.9752 with a loss of 0.19.
Virtual Science is a New Scientific Paradigm

Researchers speculate on the long-term effects of wearable virtual reality and augmented reality headsets on the global information society. The evolution of virtuality through its various stages is discussed. Today's views of social life, i.e. virtualized society, virtual realities, virtual images, and their impact on today's social processes are widely explained. The obstacles faced by today's youth when faced with virtual realities are discussed. The issue of the effect of the development of science and technology on the process of turning virtual images into reality on people's worldview was studied. It was deeply analyzed that virtualistics is a new philosophical direction, a new scientific paradigm. The article philosophically analyses the concept of the combination of virtuality and real life to create a healthy social environment
Generation of Spatially Entangled States of Light in Nonlinear Waveguide Arrays

High-dimensional degrees of photons – such as orbital angular momentum, spatial modes of frequency – provide a powerful mean to increase the density and security of quantum communication, and to enhance flexibility in quantum computing. In particular, the spatial degree of freedom is particularly suited to on-chip integration. Rapid progress has been made in recent years to develop integrated circuits achieving on-chip quantum interference, entanglement and gate operations on spatially encoded states, culminating in the demonstration of the Boson Sampling task on-chip [1]. While many of these demonstrations have relied on external sources to generate quantum states of light, which were then fed into a passive circuitry, a next step has been taken recently with the demonstration of active chips combining the generation and manipulation of spatially encoded states [2].
Software Reliability Analysis with Various Metrics using Ensembling Machine Learning Approach

In recent years, there has been an increasing need for remote surveillance and monitoring of our boundary. Nowadays, it is a difficult duty for military to monitor international border regions. Our goal is to incorporate robots in military security systems in order to provide the highest level of protection, since robotics are being adopted by all fields in order to enhance current systems. In this study, we propose a surveillance robot that is capable of monitoring and identifying intrusions near international boundaries. As a result, the surveillance robot is programmed to automatically identify border intruders and notify surrounding security staff through Internet of Things video streaming. When motion is detected, the Raspberry Pi 3 Model B, which is in charge of the bot’s computer, is programmed to turn on the camera so that it may transmit live footage. Moreover, an infrared sensor is used to detect obstructions and avoid collisions. The raspberry pi camera is connected to a microcontroller that keeps watch over the premises and sounds an alarm if an intruder is spotted. Enabling WiFi enables the Internet of Things to be used for the surveillance robot’s transmission function. The camera footage may be analysed from any Internet-connected place due to Raspberry Pi’s live streaming capability. As the IoT module serves as both the transmitter and receiver, the node’s footprint, price tag, and level of complexity are all reduced. To that purpose, this project proposes using a surveillance robot running the Raspbian operating system.
Review of Melanoma Detection by using Blending Ensemble Method

The most severe kind of skin cancer in humans is melanoma. Due to melanoma detection delays, most humans die for years. Effective treatment of melanoma skin cancer depends on early detection. The automatic system will decrease the number of fatal instances brought on by melanoma. In the pathology lab, there are some manual methods for finding melanoma. However, this method may take more time because of the complex dataset. Deep learning models are used to solve this issue. There are numerous melanoma datasets on Kaggle. This experiment will use the ISIC dataset downloaded from Kaggle. The primary goals of the proposed approach are to decrease overfitting, improve model accuracy, minimize error, shorten training periods, and address issues with class imbalance. In a different research paper study, we observed that the ensemble approach in deep learning can give better accuracy than when individual models are used. For the experiment, use blending ensemble learning. This project will use a base model such as CNN, Vgg-16, GoogleNet, and ResNet-50 as a meta-model for the final prediction. The model evaluation will use the confusion matrix, accuracy, and F1 score.
Deep-ABSA: A Multichannel Deep Learning Framework For Aspect-Based Bangla Sentiment Analysis

Nowadays people express their opinions on social media. Also provides product reviews on eCommerce websites and responds to various news as comments. It is necessary to know the polarity and aspect of various posts and comments for business, education and security. Aspect-based sentiment analysis (ABSA) predicts text category and polarity. In this paper, we proposed a deep learning framework Deep-ABSA for aspect-based sentiment analysis from Bangla texts. Our proposed framework is a multi-channel architecture. We implemented the word embedding, Bi-LSTM with the attention mechanism for one channel. And for another channel, we adopted the character-embedded convolutional neural network. Finally, we concatenated both channels for adjoining the features of each channel. We obtained an adequate performance from our proposed framework for aspect term analysis from Bangla sentences.
Yoga Pose Estimation Using Rule-based Approach

Due to high computing requirements and a lack of available datasets, precise pose detection in yoga is a challenging task. Since even small modifications can have negative effects, suggestions should be made precisely[1–2]. Pradhan is a rule-based technique which is used to guide people who practice yoga at the convenience of their homes. Pose estimation’s primary goal is to foretell human poses by identifying key points like elbows, knees, wrists, and so on. In this paper, we have proposed a system which uses rule-based techniques on every frame that was processed by the Mediapipe Framework. In this technique, the user can either upload images or perform a yoga posture in front of a camera which is then used to classify the posture from a set of 10 pretrained postures. The yoga poses used are Dolphin Pose, Half-Moon Pose, High Lunge Pose, Mountain Pose, Side Plank Pose, T-Pose, Tree Pose, Upward Salute Pose, Warrior-II Pose and Warrior-III Pose. When the work is put into practice, a real-time video feed from the user’s computer’s webcam is collected, and the yoga pose’s estimation is done. This research work used angle heuristics to categorize various yoga postures for pose detection, and we were able to achieve a combined accuracy of 93%.
DeepFake Detection using Transfer Learning

A deepfake is a computer-generated fake image or video that combines images to create a new image or video that depicts an event, comment, or activity that did not actually occur. This has become a real problem nowadays to decide the originality of a video. For the same reason we are trying to create a machine learning model using transfer learning which will help us to distinguish between different videos to decide which video is real and which one is fake. For that we are using four different models and comparing their results. The overall best model is InceptionResNetV2 considering its training time and accuracy. In our results the InceptionResNetV2 performs best and gives an accuracy of 97.1 percent.
PAMPER - Patients Medical Records Protection using EncRyption (with Blockchain)

This paper analyzes the current trends in the protection of patient’s medical records. With the growing population, the need for data storage has substantially risen which also increases our requirement to store this data safely and efficiently. As we move towards a more green and clean environment it is essential to make it easy for people to store and access data without harming the environment. Thinking along the same lines, authors work on creating a secure platform to store medical records of patients which will not only help them access it globally but also reduce the burden of having thick files filled with pages, often carryinga risk of damage or loss. Authors propose blockchain methodology along with patients’ id encoded in ciphers so that only the parties responsible in contempt of court can access these records. Blockchain technology’s immutable nature helps make the platform much safer with the help of hash chain and private key.
Cloud Security Enhancement Using Modified Enhanced Homomorphic Cryptosystem

Cloud computing provides organizations with cost-effective, scalable, and flexible computing resources, enabling them to store, process and manage large amounts of data more efficiently. having instant access to computational resources without having to purchase pricey hardware and software, makes cloud computing an attractive option for businesses of all sizes. The increased use of cloud computing has also led to an increased need for security measures to protect sensitive data stored and processed in the cloud. Without proper security measures in place, cloud computing can be a risky and vulnerable environment, making cloud security a top priority for organizations utilizing cloud services. Homomorphic encryption is among the recent cryptographic technique which is proposed in our work that enables computations that can be done on encrypted data without having to first decrypt it. This makes it possible to securely process sensitive data in the cloud, preserving the privacy and confidentiality of the data. In this research, fully homomorphic encryption is used to implement cloud security for data analytics. We use homomorphic encryption, which enables the processing of the data requiring decryption. The result analysis shows improvement in key generation time, encryption time complexity, and decryption time complexity.
Scaling Agile P3O–VMO for Banking – Global Capability Centre

Implementing an Agile P3O-VMO (Portfolio, Programme, and Project Offices-Value Management Office) in the banking industry involves a combination of processes, methodologies, and tools to align the bank's projects and portfolios with its strategic goals and objectives while delivering value to the business. The goal of implementing Agile P3O-VMO is to help the bank to become more flexible, responsive to change, and able to adapt quickly to the needs of Agile teams [1]. The process involves assessing the bank's current processes, methodologies, and tools to identify gaps and areas for improvement.
Machine Learning Optimal Control Pulses in an Optical Quantum Memory

The use of machine learning (ML) methods is an attractive approach to abstract the control of experimental variables away from researchers, to an optimizable, automated process. ML has been used to optimize experimental processes, ranging from laser cooling for BEC creation [1], to super-resolution optical microscopy [2] and optical memory optimization [3]. Optical quantum memories have been identified as an essential component for the realisation of a quantum internet, when used for quantum repeater nodes. A key factor is the efficiency of the memory in the node, indeed, it has been shown with an efficiency of 0.8 one can achieve kHz key rates [4]. Current experimental realisations have an efficiency of 0.33 [5]; thus improvement of the internal memory efficiency remains an important research goal.
Time-Frequency Quantum Metrology

Hong-Ou-Mandel interferometry takes advantage of the quantum nature of two-photon interference to increase the resolution of precision measurements of time delays. Relying on few-photon probe states, this approach is applicable also in cases of extremely sensible samples and it achieves attosecond-scale resolution, which is relevant to cell biology and two-dimensional materials. Here, we theoretically analyze how the precision of Hong-Ou-Mandel interferometers can be significantly improved by engineering the spectral distribution of two-photon probe states. In particular, we assess the metrological power of different classes of biphoton states with non-Gaussian time-frequency spectral distributions, considering the estimation of both time and frequency shifts. We find that grid states, characterized by a periodic structure of peaks in the chronocyclic Wigner function, can outperform standard biphoton states in sensing applications [1].
Assistive Technology: An Alternative for Locomotion Disability and Resultant Social Participation

Having effective mobility is crucial to carry out daily activities and participate in social roles. This paper presents a robot that can detect hand gestures and provide assistance to people with mobility impairments, including the elderly and those with disabilities. The proposed technology utilizes image processing with the Raspberry Pi Arm – 11 board (700 MHz) to control the dc motors, which are connected to motor driver circuits (L298). The robot’s actions are based on the data received from a webcam interfaced by USB ports. The processors used are compatible with Linux and GUI operating systems, making the technology both high-performance and economical.
An Ultimate Benchmark for the Optimal Performance of Quantum Communication Network Using Multi Path Routing Protocol

In recent years, quantum communication networks have emerged as a promising alternative to classical communication networks for the transmission of quantum information. One of the challenges for achieving optimal performance in quantum communication networks is the efficient routing of quantum signals across multiple paths. To address this, a variety of routing protocols have been proposed, including multi-path routing protocols. Multi-path routing involves sending a quantum signal through multiple distinct paths simultaneously to reduce delays in the transmission of quantum information. By distributing the quantum information over multiple paths, this approach can provide significant performance improvements, including reduced latency and higher throughput. In addition, multi-path routing can enable new possibilities for the security of quantum communication networks, such as more secure entanglement swapping protocols and improved protection against eavesdropping. In this paper, we investigate the performance of multi-path routing protocols in multi-node quantum communication networks. We consider two approaches to multi-path routing, a basic routing approach based on shortest path optimization, and a more advanced approach based on graph partitions. We evaluate the performance of these protocols in a range of multi-node networks, with focus on the trade-off between latency and throughput. The results show that multi-path routing yields significant performance improvements in such networks and provides an efficient way for routing quantum signals across multiple paths.
A Multilevel Center Embedding approach for Sentence Similarity having Complex structures

The volume of text data, including internet reviews, media posts, and academic articles, has significantly increased in recent years. Text similarity measurements are essential for many applications, including various language processing tasks and IR based systems. However, when dealing with complicated structures like lengthy sentences, having main and subordinate clauses in terms of compound/complex sentences as well, these similarity measurements become more difficult. In this article, we provide a multilevel center embedding method for determining similarity in such text. The suggested method makes use of several embedding levels, such as word, pos, clause, and sentence levels, to capture the intricate structure of text. By constructing the center embedding of a sentence and then iteratively computes the difference between an original center embedding and modified versions of the sentence by applying the center embedding in a leveled manner that introduces a new level of abstraction. Our findings show that, the multilevel center embedding strategy outperforms in category of complicated structured based phrases/sentences.
Distributed Execution of Dask on HPC: A Case Study

Performing computations on a large dataset using a system with limited memory can present difficulties, as the entire dataset may not be accommodated in memory. Dask is a framework that facilitates parallel and out-of-core computation, thereby demonstrating its efficacy in enhancing the utility of modern hardware for processing larger datasets. High Performance Computing (HPC) is a distributed computing architecture that aims to enhance computational performance by distributing the workload across a network of interconnected computing nodes. Each node in an HPC system is typically equipped with its own set of processors, memory, and storage resources, and is responsible for executing a subset of the overall workload. By utilizing this parallel processing approach, HPC systems can handle large-scale computational tasks that are beyond the capabilities of individual computing nodes. This work presents an approach for managing and overseeing the distributed execution of Dask on HPC using a workload manager that is frequently utilized in HPC environments. We conduct a performance evaluation of Dask’s distributed execution capabilities on HPC. Our evaluation focuses on two critical performance metrics, namely, execution time and memory management. Specifically, we aim to investigate the efficiency of Dask’s distributed execution approach in the context of HPC, and identify potential challenges and opportunities associated with its deployment in this environment.
YOLOv5 Model-based Ship Detection in High Resolution SAR Images

Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models.
FastNet for Symbol Detection in Massive MIMO Systems

In massive multiple-input-multiple-output (MIMO) systems, a major limiting factor for symbol detection is the amount of computational complexity required. Symbol detection in unquantized massive MIMO systems have been studied in the context of both traditional and machine learning methods. In this paper, we propose a hybrid framework that replaces some neural network layers with simple gradient descent layers to reduce complexity. Simulations showed that a judicious choice of the number of such layers can lead to significant reduction in the range of 35-50%, in computational complexity, with marginal change in performance.
Moiré Vision: A Signal Processing Technology Beyond Pixels Using the Moiré Coordinate

Nowadays, many computer vision (CV) techniques require high-precision feature point detection in calibration targets. Checkerboards or circle grids are common patterns used to infer the locations of the points directly from the image. However, such approaches can lead to degradation in sub-pixel accuracy for low-resolution or sub-sampled sensor data (e.g., Bayer pattern). This paper proposes a target-based calibration technique applicable to various conventional CV methods, leveraging the moiré phenomena. We use a moiré pattern that is created between the digital camera’s pixels and the high-frequency texture on the calibration target. Even a tiny movement of the target or camera leads to a significant motion of the moiré, acting as a magnifier of the target texture for estimating accurate location of targets or camera. In contrast to previous moiré approaches that employ frequency domain analyses, we introduce the moiré coordinates and derive them from the difference between the sensor and target texture position. In the moiré coordinate, each moiré pattern is undistorted. Unlike moiré in standard images, each moiré pattern has almost the same scale and shape in these coordinates. Utilizing this property, we propose a straightforward algorithm to detect and handle correspondence in the moiré peaks in image and texture location, treating them as feature points of calibration target. In addition, the moiré vision can magnify pixels, which means the pattern in the moiré coordinate visualizes the pixel sensitivity with high spatial resolution. To validate the proposed approach, we present an empirical evaluation using simulated and real Bayer sensor patterns. We show the advantages of the proposed method in terms of accuracy and identify promising future directions.