Reviewerly/exHarmony huggingface dataset:
News text Analysis using Text Summarization and Sentiment Analysis based on NLP

Every day, at least 2.5 quintillion bytes of data are generated worldwide. This results in information explosion. Excessive information about a subject makes it difficult to focus on the most important concepts and findings. As a result, it becomes challenging for data analysts to determine which data is correct and which data is unnecessary for a given task. Natural Language Processing (NLP) based text summarization is an effective solution to this problem. Text summarization helps to reduce the size of a data or text while retaining the information. At the same time, it is highly difficult to manually summarize lengthy text documents. The primary goal of the proposed text summarization model is to highlight and present consumers with the most pertinent information from the provided text data. Using text summarization and NLTK, this study attempts to propose a text sentiment analysis on news material.
A Decentralized Flight Insurance Smart Contract Application using Blockchain

Flight insurance has always been an element of air travel because the aviation sector has always been vulnerable to risks and uncertainties. There is a chance to upgrade aviation insurance processes and make them more secure, transparent, and effective with the development of blockchain technology. In this paper, potential of blockchain technology in flight industry is explored, including its advantages and challenges, and how it can be applied to create a decentralized and secure system for flight insurance. The application will allow users to purchase Insurance on the go and will make claim procedures hassle-free with less documentation. The service can be accessible to users by visiting the website.
Rate reduction of blind quantum data compression with local approximations based on the unstable structure of quantum states

In this paper we propose a protocol for a data compression task, blind quantum data compression, with finite local approximations. The rate of blind data compression is susceptible to approximations even when the approximations are diminutive. This instability originates from the sensitivity of a structure of quantum states against approximations, which makes the analysis of blind compression in the presence of approximations intractable. In this paper we construct a protocol that takes advantage of the instability to reduce the compression rate substantially. Our protocol shows a significant reduction in rate for specific examples we examine. Moreover, we apply our methods to diagonal states and propose two types of approximation methods in this special case. We perform numerical experiments and observe that one of these two approximation methods performs significantly better than the other. Thus, our analysis makes a step toward general investigation of blind quantum data compression with the allowance of approximations towards further investigation of approximation-rate trade-off of blind quantum data compression.
Analogical Study of Activation Concept in Neural Networks with Neat- Python Module

Determining real-time machine simulation and functionalities of complex AI Engines is difficult to comprehend and is rarely discussed.We present a technique to analyze the workflow of one such engine, the NEAT engine, one of the fundamental and robust training engines in the current machine learning scenario.Computer Vision also presents a great approach towards working in real-time speedy functioning virtual simulators and visual platforms, whereas NEAT is not specialized in the same, but can perform to the best of the extent in the present day Machine Learning aspect.Technologies like Python, PyGame, and CsV were used to facilitate the research.So far, we have tested both the frameworks on real time cases, and it is safe to say that the NEAT module has presented an accurate trajectory, besides greater time complexity.Thus, we not only evaluate the accuracy but the other key factors as well.This study demonstrates that NEAT has the ability to address other difficult issues in the future and can produce excellent outcomes with a relatively small population.Robotics, artificial intelligence for video games, natural language processing, and healthcare are some of the potential future applications for NEAT.
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation

The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected to a filtering operation on the graph spectral domain, the theoretical rationale for why this leads to higher performance on the collaborative filtering problem remains unknown. The presented work makes two contributions. First, we investigate the effect of using graph convolution throughout the user and item representation learning processes, demonstrating how the latent features learned are pushed from the filtering operation into the subspace spanned by the eigenvectors associated with the highest eigenvalues of the normalised adjacency matrix, and how vectors lying on this subspace are the optimal solutions for an objective function related to the sum of the prediction function over the training data. Then, we present an approach that directly leverages the eigenvectors to emulate the solution obtained through graph convolution, eliminating the requirement for a time-consuming gradient descent training procedure while also delivering higher performance on three real-world datasets.
Partilha de Informação em Contexto Empresarial na Ótica da Ciência da Informação.

In a business environment, meeting deadlines in projects can lead to the absence of information management, namely deficiencies in the process of creation and document organization that make it difficult to recover. The implemented project aims to define improvements in the processes of creation, organization, and sharing of info documentation, leading to the development of instruments in this area. This is an applied investigation, based on the review of narrative literature and the collection of evidence using observation grids and their analysis. It is also exploratory-descriptive research, allowing one to know and deepen themes. It was necessary to map the documentation produced throughout the developed projects and to an evaluation of the documentation stored in the institutional repositories. Improvements were proposed in the creation, organization, and sharing of information documentation that used the technologies to facilitate the creation, access, and dissemination of info documentation, thus fulfilling the proposed objective.
Survival Prediction of Cervical Cancer Patients using Genetic Algorithm-Based Data Value Metric and Recurrent Neural Network

Survival analysis and machine learning has been shown to be an indispensable aspect of disease management as it enables practitioners to understand and prioritize treatment mostly in terminal diseases. Cervical cancer is the most common malignant tumor of the female reproductive organ worldwide. Survival analysis which is a time –to –event analysis for survival prediction is therefore needed for cervical cancer patients. Data Value Metric (DVM) is an information theoretic measure which uses the concept of mutual information and has shown to be a good metric for quantifying the quality and utility of data as well as feature selection. This study proposed the hybrid of Genetic Algorithm and Data Value Metric for feature selection while Recurrent Neural Network and Cox Proportionality Hazard ratio was used to build the survival prediction model in managing cervical cancer patients. Dataset of 107 patients of cervical cancer patients were collected from University of Benin Teaching Hospital, Benin, Edo State and was used in building the proposed model (RNN+GA-DVM). The proposed system outperform the existing system as the existing system had accuracy of 70% and ROC score of 0.6041 while the proposed model gave an accuracy of 75.16% and ROC score of 0.7120 respectively. From this study, It was observed using the GA_DVM features selection that the variables highly associated with cervical cancer mortality are age_at_diagnosis, Chemotherapy, Chemoradiation, Histology, Comorbidity, Menopause, and MENO_Post. Thus, with early diagnosis and proper health management of cervical cancer, the age of survival of cervical cancer patients can be prolonged.
Smart System for Attendance Based on Face Recognition

Face recognition systems are essential in almost every industry in this digital age.One of the most common biometrics is face recognition.It can be used for security, authentication, and identification, among other things.Despite its low accuracy in comparison to iris recognition and fingerprint recognition, it is widely used due to its contactless and non-invasive process.Furthermore, face recognition systems can be used to track attendance in schools, colleges, and offices, among other places.As the existing manual attendance system is time consuming and difficult to maintain, this system aims to build a class attendance system that uses the concept of face recognition.There may also be opportunities for proxy attendance.As a result, the growth for this system grows.The images of the students in class are used to create the database.The Haar-Cascade classifier and the Local Binary Pattern Histogram algorithm are used for face detection and recognition, respectively.Faces are detected and recognised in the classroom's live streaming video.At the end of the session, attendance will be mailed to the appropriate faculty.
Comparative Study of Noises Over Quantum Key Distribution Protocol

The security of modern cryptographic systems mostly depends on mathematical hardness to solve a particular problem manually. The most popular and widely used classical cryptosystem, RSA (Rivest, Shamir, Alderman) is based on the mathematical hardness of prime factorization of the product of two large prime by any of the present classical processors. But the advancement in technology particularly in quantum information provides a threat to the present communication protocols. It motivates the researchers to move to new technologies that are fundamentally more secure through the principle of quantum mechanics. Such protocols rely upon quantum bits or qubits for data transmissions securely. The application of qubits instead of bits in cryptography using quantum key distribution protocols makes encryption robust. But the quantum communication protocols are prone to noise due to environmental factors, fragile and traverse’ a long fiber optical path. Hence, the states of qubits are easily tampered with by external factors. Noise alters the information; eventually, the message may not be meaningful to receivers. Except for bit flip and phase flip noises, depolarization, amplitude damping, decoherence, thermal relaxation, and phase damping are obstacles to achieving long-distance communication. The objective of this paper is to present a comparative study of quantum communication protocols in a noisy environment with different classes of noise.
Multi-GPU-Enabled Quantum Circuit Simulations on HPC-AI System

The quantum circuit simulator on HPC system plays a key role in algorithm implementation, algorithm design, quantum education, and associated other research areas. In this work, we have implemented cuQuantum for accelerating quantum computing workflows on 64-bit floating point and TensorFloat-32-based accelerated system. The commonly used quantum algorithms like Shor’s, quantum Fourier transformation (QFT), and Sycamore circuit are implemented on HPC-AI system. These algorithms are further accelerated using cuQuantum. The observed performance for GPU-enabled circuits increases linearly on 2, 4, and 8 A100 GPUs for the given qubit size. GPU-enabled performance obtained on PARAM Siddhi AI system is compared with those observed from CPU only run as well as observed from the previous generation volta architecture (V100) GPUs. For Shor, QFT, Sycamore circuits, the relative speedup (qubits) between eight A100 GPU enabled run and CPU only run and is observed as ~143x(30), ~115x(32), ~104x(32) respectively. Similarly, the relative speedup (qubits), between 4 V100 GPU enabled run and CPU run observed as ~43x(30), ~29x(32), and 24x(32) respectively. In view of the better compute capability and memory, the relative performance between four A100 and four V100 GPUs varies from 1.5x to 2.2x for all the three algorithms.
The Mid-Second Millennium A.D. Submerged Iron Production Village of Pontada in Lake Matano, South Sulawesi, Indonesia

By the mid-second millennium A.D., Indonesians were already familiar with iron tools. One of Indonesia’s main centers of iron production is the Lake Matano area in the hinterland of East Luwu, South Sulawesi, eastern Indonesia, renowned for its nickeliferous iron ore. Research in Lake Matano conducted during 2016-2022 succeeded in documenting a submerged village with remnants of an iron processing industry dating to the late first millennium A.D. In this article, we describe a second submerged site associated with processing iron, which included forged iron implements. Occupation at this site, called Pontada, is dated to between the fourteenth and fifteenth centuries A.D., before the site sank beneath the surface of Lake Matano. Written and archaeological evidence suggests that its antiquity corresponds to when the empire of Majapahit in eastern Java imported nickeliferous iron from Lake Matano and traded it throughout coastal Luwu.
A Study on Envy: Psycho-Social Review on the Jews’ Envy toward Paul in Acts

이 연구의 목적은 시기심의 특징과 요인들을 이해하고, 시기심이 발로하는 과정을 밝히며, 이러한 시기심이 사도행전에 나타난 유대인의 바울에 대한 시기에서 어떻게 작동하는지 심리·사회적으로 분석, 고찰하여, 현재 우리 삶에 던져주는 시사점을 밝히는데 있다. 이를 위해 시기심에 관한 이론과 사도행전의 배경이 된 초기 기독교 시대의 유대인과 바울의 모습을 문헌연구를 통해 파악하였다. 시기심은 사회적 상향비교를 통해 누군가에게 느끼는 고통스러운 감정으로, 이는 곧 자기평가와 자기인식으로 이어진다. 다메섹 사건 이후 바울은 유대인만을 위해 존재하는 유일하신 하나님이 이제는 유대인과 이방인 모두를 구원하신다는 신학을 펼쳤다. 이는 유대주의자들의 정체성, 자기를 파괴하는 일이었기에 매우 위협적이었으며 시기심의 원인이 되었다. 시기심을 극복하기 위해 바울이 대응했던 방식처럼, 현대 사회를 살아가는 우리 또한 시기하는 상대방의 핵심욕구를 파악하여 겸손한 태도를 유지하는 것, 가십에 참여하지 않는 것, 지지하는 공동체를 찾는 등 개인적이고 공동체적인 노력을 적용할 수 있다.The purpose of this study is to understand the characteristics of envy, examine Jews’ envy toward Paul in Acts, and reveal the implications to us in this time. Theory of envy and the background of Acts, Jews and Paul in the early Christian era were identified through literature research. Envy is a painful feeling towards someone through social upward comparison, which leads to self-evaluation and self-awareness. After the Damascus incident, Paul developed the theology that the only God who exists only for the Jews now saves both Jews and Gentiles. This was very threatening and a cause of envy for the Jews because it was the destruction of the identity and self of the Judaisers. Just as Paul responded to overcome envy, we, who live in modern society, must understand the core needs of the envious partner and maintain a humble attitude, avoid participating in gossip, and find a community that supports us.
The Use of Digital Twins for Elaboration of Strategic Guidelines to Ensure Sustainable Development of Industrial Enterprises

Many scientists associate the prospects for sustainable development of industrial enterprises with the development of strategic partnerships. Although there are a number of studies that address different issues related to strategic management of sustainable development in industrial enterprises, little attention is paid to the problems of modeling resource flows within the framework of strategic interactions. This is largely due to the complexity of collecting information about the production processes at the enterprises. To solve this problem, we suggest applying digital twins, the existence of which is based on simulation models. The purpose of this article is to develop a simulation model for the resource flows of an industrial enterprise within the framework of a strategic partnership. This approach will allow making informed managerial decisions to achieve the goals of sustainable development. A simulation model discussed in this article will be developed on the example of a sugar factory. The difference between this model and the existing ones is that the resource flow parameters are determined taking into account the existing production conditions and the interests of the participants involved in strategic interactions, i.e. by the volume of demand and the expected sales price. The simulation model is based on the principles of system dynamics. The indicators that set the sustainable development vector are determined by economic, social and environmental components. The decision-making process on sales volumes is described using Java code. The model experimental verification showed that it makes possible not only to analyze the parameters describing the enterprise’s sustainable development taking into consideration various cooperation alternatives, but also to determine the best interaction option that meets the goals of sustainable development. The study results may be used to form digital twins as a practical method to improve efficiency of industrial enterprises.
Key Based Steganography Using Convolutions

In information security, one of the most predominant tasks is to safeguard crucial information from potential intruders. This information is supposed to be safe in password-guided vaults or secure cloud storage. However, with increasing cyberattacks, additional security layers to this vulnerable sector are never redundant. Focusing on image data, a confidential image can be embedded in some other random image to hide the asset but many naïve approaches to achieve this has been broken down. The approach presented in this report is focused to increase the complexity of the architecture of the existing models. The proposed model encrypts the confidential image with a key and hides it in another random image in such a way that is safe to send. With this, even if the manipulation is somehow identified, the processed image is expected to be robust enough to withstand reverse engineering methods. The model thus ensures utmost confidentiality. The retention rate is calculated in terms of SSIM value as 0.828. The average PSNR value is 70.495. The mean square error was 0.017. The reconstructed, noisy images can also be digitally premastered to refine the quality.
A Novel Image Encryption Algorithm Using Logistic and Henon Map

Image encryption is a method for efficiently hiding the image’s valuable characteristics/contents. Chaotic maps are commonly employed in the encryption of systems that are dynamic. The main qualities of Chaotic Maps that make them effective for Image Encryption are that they are extremely sensitive to the initial values of their parameters; even a minor change in these parameters will result in a big change in the end output. Using chaotic/Logistic Maps, we research and develop new/improved ways to encrypt images. The suggested approach tries to provide information security by encrypting images with chaotic maps. The inclusion of chaotic methods in the encryption makes decrypting the image more challenging for attackers.
Confidence-Aware Paced-Curriculum Learning by Label Smoothing for Surgical Scene Understanding

Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic vision. Most recent works focus on designing curricula based on difficulty levels in input samples or smoothing the feature maps. However, smoothing labels to control the learning utility in a curriculum manner is still unexplored. In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a heavy smoothing penalty in the true label and limits learning less information. Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger smoothing value at the beginning of training and gradually decreased it to zero to control the model learning utility from lower to higher. We also designed a confidence-aware pacing function and combined it with our CBLS to investigate the benefits of various curricula. The proposed techniques are validated on four robotic surgery datasets of multi-class, multi-label classification, captioning, and segmentation tasks. We also investigate the robustness of our method by corrupting validation data into different severity levels. Our extensive analysis shows that the proposed method improves prediction accuracy and robustness. The code is publicly available at https://github.com/XuMengyaAmy/P-CBLS. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this article is to improve the performance and robustness of deep neural networks in safety-critical applications such as robotic surgery by controlling the learning ability of the model in a curriculum learning manner and allowing the model to imitate the cognitive process of humans and animals. The designed approaches do not add parameters that require additional computational resources.
PICKD: In-Situ Prompt Tuning for Knowledge-Grounded Dialogue Generation

Generating informative, coherent and fluent responses to user queries is challenging yet critical for a rich user experience and the eventual success of dialogue systems. Knowledge-grounded dialogue systems leverage external knowledge to induce relevant facts in a dialogue. These systems need to understand the semantic relatedness between the dialogue context and the available knowledge, thereby utilising this information for response generation. Although various innovative models have been proposed, they neither utilise the semantic entailment between the dialogue history and the knowledge nor effectively process knowledge from both structured and unstructured sources. In this work, we propose PICKD, a two-stage framework for knowledgeable dialogue. The first stage involves the Knowledge Selector choosing knowledge pertinent to the dialogue context from both structured and unstructured knowledge sources. PICKD leverages novel In-Situ prompt tuning for knowledge selection, wherein prompt tokens are injected into the dialogue-knowledge text tokens during knowledge retrieval. The second stage employs the Response Generator for generating fluent and factual responses by utilising the retrieved knowledge and the dialogue context. Extensive experiments on three domain-specific datasets exhibit the effectiveness of PICKD over other baseline methodologies for knowledge-grounded dialogue. The source is available at https://github.com/rajbsk/pickd .
Role of Angular Framework in Web Development

A well-liked open-source front-end web application framework called Angular is used to create scalable and reliable web apps. Because of its robust capabilities, Angular makes web development simpler by offering a methodical and effective way to create dynamic and interactive apps. This study attempts to investigate the function of the Angular framework in contemporary web development and its advantages for web developers. The paper explores the features of Angular that make it a top choice for web development and looks at the benefits of choosing it over competing front-end frameworks like React and Vue. A few actual examples of Angular applications and how they were created utilising the framework are also highlighted in the paper. The results of this study indicate that Angular is a highly efficient framework.