Reviewerly/exHarmony huggingface dataset:
Recommendation System for Journals based on ELMo and Deep Learning

Choosing the right journal to publish research studies is critical for researchers. Despite its importance, the task of determining the suitable and high-ranking journal for publishing can be challenging due to several factors such as the growing number of the available journals and the fact that every journal has its specific area of expertise. In this paper we investigate content-based journal recommendation systems that rely on using NLP to analyze features of existing journals and use those to pre-select a particular number of suitable journals for a new paper. Our experiments are based on the ELMo feature engineering mechanism and use different deep learning neural network architectures (CNN, RNN). We used datasets from the disciplines physics, chemistry and biology, with each containing data of more than 750000 publications. The data source consists of the abstracts of the papers. The experiments show promising results with the accuracy of our models outperforming existing models. Specffically, our RNN model can achieve 83% accuracy when using data from physics by considering top-20 journals.
Extraction of a computer-certified SMT solver for nonlinear theories

We present an effective procedure for solving of the so-called $\delta-$satisfiability modulo theory (SMT) problem over the reals. Our methods are supported by the special approach to the exact real arithmetic: constructive analysis. First, we present an alternative proof of the decidability of the bounded $\delta-$SMT problem within our framework and an efficient branch-and-bound algorithm, which is based on the rational interval arithmetic. We provide some initial experiments to demonstrate the applicability of the derived solver. Finally, we discuss potential extensions and possible optimizations of the suggested algorithm.
A Centralized Blockchain Architecture with Optimum Sharing

The monolithic network architectures are used in Bitcoin and other big cryptos. Different hierarchical ways to protect the decentralized blockchain have been discussed to improve its usability. However, no debate in the literature on how an appropriate shard size can be calculated to optimize the output and how deceptive or unreliable nodes can affect the optimum shard size range. This study includes an exchange and authentication scheme for hierarchical blockchain architecture in order to fix these issues. The hierarchy splits the network nodes into many disjoint fragments, with most transfers unregulated between these shards. Taking into account the performance and precision of the transaction validation in the case of malicious or defective nodes, the optimum shard size is calculated. It ensures that most of the good shards are available with the right scale, according to the highest acceptable threshold of defective nodes. It also offers a method to distinguish damaged shards by overlapping a tiny proportion of purchases across all shields and discarding all transactions. The theoretical efficiency analysis and the influence of network sharing on performance were based on experimental findings conducted on Amazon EC-2 nodes.
Distributed Architecture of Data Retrieval Algorithm based on Hadoop Considering Abnormal Information

As digital media continues to grow, how to retrieve and efficiently store multi-modal data is becoming a major issue. Most of the existing retrieval algorithms focus on the analysis of a single modality, which often fail to achieve the established accuracy target, and usually consume a lot of time. To deal with these tough challenges, this paper studies the distributed architecture of the data retrieval algorithm based on the Hadoop considering abnormal information. In our method, 3 aspects are considered: 1) background is studied to review the state-of-the-art appraoches for the efficient data analysis model. 2) novel data retrieval algorithm is proposed, it then improves the traditional methods. 3) novel data retrieval algorithm is proposed, it improves the traditional methods. Through the testing undert the Wikipedia dataset, the performance is validated.
Human Cognition and Artificial Intelligence: A Survey

Cognitive science is a rich field of study that can provide useful information to the developers of Artificial Intelligence (AI) systems. This study investigates the relationship between AI and cognitive science. This study significantly advances the theoretical understanding of how artificial autonomy influences humans perceive, which in turn influences people behavior. This research focuses on secondary studies that established a link between cognitive science and artificial intelligence (AI) for the development of AI technologies.
Efficient Collision Modelling for Numerical Optimal Control

Efficiently solving optimal control problems (OCP) is a prerequisite for using methods such as model predictive control (MPC) for real-time control of complex robotic systems. For many applications, it is mandatory to guarantee that the OCP solutions correspond to collision-free motions. However, establishing appropriate constraints for OCP is not straightforward. A main reason is that the function determining the Euclidean distance between two arbitrary convex shapes is often non-differentiable and non-convex. Moreover, this function can generally only be defined implicitly, for example, by an optimization problem. Recently, it was proposed to derive collision constraints by utilizing duality theory of convex optimization as the resulting constraints are differentiable and allow to check the exact distance between arbitrary compact convex sets. These constraints naturally depend on the chosen representation of the sets for which collision avoidance should be established. In this paper, we show that the computational effort of solving OCP including the proposed constraints can be reduced by explicitly considering the type of sets involved and choosing their representation properly. To this end, we consider the two prominent cases where collision avoidance has to be established between two polytopic sets or between a polytopic and an ellipsoidal set. The claim that our proposed constraints allow improved computational performance is supported by numerical experiments with a car model.
Sign Language to Text Conversion using Hand Gesture Recognition

Most people are unaware of sign language which creates a language barrier amongst people and it's very challenging to find interpreters for American Sign Language (ASL). Therefore, this research is based in ASL fingerprint-based real-time method utilizing Convolutional Neural Networks (CNN). The approach discussed in this work involves passing the hand through a filter first, and then a classifier that determines the type of hand gesture once the filter has been applied. It is implemented using CNN as it is very effective at addressing computer vision issues. This proposed model achieved 99% accuracy on the first (MNIST) dataset and 96% on the second (ASL) dataset.
Building an Intelligent Aerial Surveillance System through Deep-learning based Object Detection

The article discusses the challenges involved with detection of vehicles with aerial pictures and methods used to overcome these challenges. Aerial images present unique challenges, such as smaller size of vehicles & intricate backgrounds. Traditional approaches such as sliding window and feature extraction have limitations in accurately detecting vehicles in aerial images. Deep learning models like R-CNN, Faster R-CNN, & Mask R-CNN were proposed & demonstrated exceptional performance. However, there remain challenges in their direct application to vehicle detection in aerial images. Modifications were proposed to overcome these challenges, including improvements to region projected network & classifier and the use of instance segmentation in addition to object detection using bounding boxes. The article highlights the need for large aerial picture real-time detection and the importance of accurate vehicle recognition from aerial photographs in various applications, including urban planning and traffic management.
Improve Secured Digital Image Watermarking with Discrete Cosine Transform and ABCT

Nowadays Because of the internet's popularity and accessibility, digital watermarking is critical for preventing illegal redistribution of digital data. As a method of protecting sensitive information stored in digital images, "digital watermarking" has gained popularity. The goal of this proposed work is to suggest a secure watermarking scheme with applications in the medical field. As well as being undetectable, this scheme can also withstand numerous forms of attack. In order to maintain the security and authenticity of patient data, the proposed work requires the concealment of sensitive information within the host image. Domain concepts, like the spatial domain, transform domain, and wavelets, can be used to categories image watermarking methods. DCT algorithms increase computation time while also degrading image data hiding performance due to low PSNR values. The proposed technique ABCT increases PSNR values or makes digital image data more robust and invisible. Taking into account the following issues in the existing system (DCT) and the proposed technique, the goal is to provide excellent security to image data, with the message embedded within the image. Watermarking is used in conjunction with content encryption, with encryption providing the secure proposed technique from digital watermarking.
An Exploratory Analysis of Metaverse Software Development Kits

Metaverse gains significant impact in the realm of innovation, business, and finance. This is the hypothesis of a tenacious, an online 3D universe that integrates different virtual spaces. This can be seen as a future iteration of the web. It is perceived as a graphically rich virtual space with some level of likeliness which is the appearance or nature of appearing to be valid or real. Metaverse can be considered as the duplicate of genuine universe, in which all current advancements will be consolidated to develop a superior innovation. On the other side in present reality, people can hear, smell, contact, taste, and feel the things, which will become believable in the virtual universe also. Metaverse creates a new reality, where people can take part in this reality with their avatars and interact with other.Most viable things being trendy in metaverse is the different types of Software Development Kits (SDK’s) available which are discussed in this paper. Metaverse has impacted the video game industry with its ability to connect to other players. As a result, users can interact and communicate with others in formats like real-time that can trick human mind in perceiving them as in the real world.
A Computer Vision based Facial Denoising Alignment using Convolution Neural Network Model

For most higher-level face evaluation applications, including liveliness, human activity identification, and personal contact, facial organization is a significant task. The real-world usefulness of such models is constrained since the utilization of present approaches can significantly deteriorate when handling images under particularly uncontrolled circumstances. This is true even though the best-in-class accuracy has significantly improved thanks to new access to large datasets and potent deep learning algorithms. In this study, a composite recurrent tracker that can simultaneously find single image facial arrangement and deformable facial following in nature, has been suggested. The multi-facet LSTMs are combined to illustrate real-world scenarios with varied length, and an internal denoiser that focuses on enhancing the information images to increase the resiliency of the general model, is offered. Face positioning is important in the majority of face examination frameworks. It emphasizes locating a few prominent features of human faces in images or recordings. The planning strategies and implementations described in this research, are based on information expansion and programming enhancement techniques, and they allow for working on a wide range of models with a place for specific continuous computations for face arrangement. A sophisticated set of evaluation metrics that enables new assessments to lessen the frequent issues seen in actual opportunity-following contexts, is proposed. The exploratory results show that the models created utilizing the approaches are more accurate, faster and more robust in defined testing environments, and more flexible in global positioning frameworks is the proposed challenge.
User Flexible Approach for Autonomous Restaurant Menu Ordering System

Automation systems are everywhere in the workplace, the house, or any sizable sector. Recent automation innovations are also being implemented by hotels and restaurants, which have robots that deliver meals and iPads for taking orders. Customers may easily choose the items using electronic menu cards, which resemble tablets. The presentation of this information will be sent to the restaurant's kitchen. The touch screen or display is primarily kept at the customer's end for different uses, including the menu display. This is coupled with an Arduino Uno to process and communicate the data via an R.F. transmitter to the consumer. The Arduino Uno is interfaced with an R.F. receiver and other parts to process the data received at the kitchen end. The monitor or any comparable display device shows the data. In this way, the old menu and order system is effectively replaced with a computerized one at the kitchen end.
Hybrid Multiple Cryptography for Data Encryption

Since the information technology revolution, cryptography has been essential for protecting sensitive information and communication. A plethora of algorithms have been developed to safeguard data and protect data integrity, with the goal of improving data security and protection. Modern cryptography is based on a variety of encryption techniques, namely Hybrid cryptography and multiple cryptography. Hybrid cryptography combines asymmetric key and symmetric key encryption to combine the strengths of both. Multiple cryptography is the process of encrypting a previously encrypted message one or more times, each time using the same or a different symmetric or asymmetric algorithm. The study proposes an approach that seeks to incorporate both hybrid and multiple cryptography as a way to combine all of their benefits while also minimizing the drawbacks that each technique has on its own to provide an additional layer of security. Although these techniques have all contributed to data protection, not all of them have been successful in providing a high level of security as they have shown a propensity for being targeted and exploited over time. This research project implements a cryptosystem based on Hybrid Multiple Cryptography (HMC) which is a novel scheme for data encryption that offers a greater level of security for sending data in an Android messaging application, in order to showcase the system's security capabilities in real time.
Automatic Trash Monitoring System

The research over the past years concludes that nearly "62 million Tones "of waste is being produced in "INDIA" yearly. This is because it is becoming increasingly difficult for national and local governments to assure the effective sustainable management of trash due to the fast rise in the volume of solid and hazardous garbage caused by ongoing economic expansion, urbanization, and industrialization. Though, the municipalities have implemented a system called "Swachh Bharat Mission (Grameen) Phase-II "to segregate liquid and solid waste for the improvement of cleanliness in rural areas throughout the country. But this process fails to ensure the efficient management of waste segregation. Manually no one in the world separates the waste Since this issue has been in the spotlight for recent years, few people have designed some systems for waste segregation using conveyor belts but that requires lots of space and is time-consuming. Now, keeping that in mind, this study has proposed and designed a smart automation system called "Automatic Trash Monitoring Bin (ATM BIN)", which aims to improve effective waste segregation. It comprises several sensors and servo motors that work together and produces effective outcomes. This system offers a long-term solution for determining the volume of waste and timely garbage collection and increases operating effectiveness, while also decreasing management expenses, resource consumption, and roadside emissions. This system also prevents the problem of trash being thrown in landfills.
Artificial Intelligence based Healthcare Chat Bot System

Machines are becoming capable of performing tasks like humans in this age of Artificial Intelligence. The most vital as pect of living a healthy life is health care, and the most difficult aspect is finding a doctor's consultant. Everyone cannot afford to visit a doctor for every health issue. The objective of the proposed research work is to design or build a Healthcare chat bot in AI to assist in determining the patient's health and providing basic information before contacting a doctor, but only for minor issues. Using a medical chat bot will save healthcare costs while also increasing medical knowledge. Chat bots are computer programs that employ AI and ML to connect with people. The chat bot system retrieves the query from the database that the user has requested and makes a judgement based on it before presenting the responses.
Automated Extraction of IoT Critical Objects from IoT Storylines, Requirements and User Stories via NLP

The first step to designing a resilient Internet of Things (IoT) application is to identify IoT critical objects (services, devices and resources) in the design phase. However, this step is a time-intensive task, because they are manually identified from storylines, requirements and user stories and have other challenges. In this work, we assessed the usefulness of Named Entity Recognition (NER) models to automatically identify IoT critical objects as a way to make a modelling process faster and less prone to errors. This was performed with the development of five NER models based on five different architectures (Spacy, BERT, Transformers, LSTM-CRF and ELMo) that were trained and tested with a large dataset with 7396 annotated sentences. Our results indicate that all NER models had satisfactory performance, but BERT had the best one and can be useful to support the time-intensive step of the early stages of the development of resilient IoT systems. Furthermore, these NER models have a high potential to be extended to a framework to automatically extract IoT critical objects from documents (storyline and requirements) and list all possible IoT threats and resilient countermeasures that can be used in the design of a resilient IoT application.
Decentralized Blockchain based Online Voting System with Biometric Authentication

Online voting system using blockchain with integration of biometric authentication have emerged as a promising solution to address the challenges of online voting such as preventing fraud and maintaining the integrity of the voting results. Blockchain technology provides a high level of security and transparency, making it difficult for hackers to manipulate or alter the voting results. Biometric authentication, on the other hand, ensures that only eligible voters can cast their votes, preventing double voting and other forms of fraud. Despite the potential benefits of using blockchain technology and biometric authentication for online voting, there are still challenges that need to be addressed. These challenges include ensuring that the system is accessible and user-friendly to all voters, protecting against cyberattacks, ensuring compliance with legal and regulatory requirements, and maintaining voter privacy. The objective of this proposed online voting system using blockchain with biometric authentication is to create a secure and reliable voting system that is accessible and user-friendly to all voters, while maintaining the privacy and integrity of the voting process. The system will use blockchain technology to create a tamper-proof and auditable record of the votes, and biometric authentication to ensure that only eligible voters can cast their votes. The system will also incorporate additional security measures such as encryption and multifactor authentication to protect against cyberattacks and ensure compliance with legal and regulatory requirements. By addressing these challenges, this proposed online voting system using blockchain with biometric authentication has the potential to increase voter confidence and trust in the election process.
Design and Development of Smart Security Key for Knowledge based Authentication

Ensuring the safety of passwords has always been a paramount concern for online users, however, numerous individuals still become a victim of identity theft due to the presence of multiple attack types such as brute-force, phishing, and dictionary attacks. These attacks can compromise the users' accounts, and dictionary attacks seem to be the most prevalent. To improve security and safeguard the privacy of users, a new methodology is proposed that involves a sophisticated digital key incorporating cutting-edge biometric and fingerprint sensor technologies along with embedded cryptography. This key generates passwords that are distinct and sturdy, which are impervious to cracking, and utilizes the Advanced Encryption Standard (AES) algorithm to encrypt and decrypt them. This mechanism offers a new means of securely managing passwords with less user intervention and a heightened level of security compared to traditional authentication techniques.
Banded Controllers for Scalable POMDP Decision-Making

This paper introduces a novel and computationally efficient policy representation, termed a banded controller, for Partially Observable Markov Decision Processes (POMDPs). The structure of a banded controller is obtained by restricting the number of successor nodes for each node in a finite state controller (FSC) policy representation; this is formally defined as the restriction of the controller’s node transition matrices to the space of banded matrices. A gradient ascent based algorithm which leverages banded matrices is presented and we show that the policy structure results in a computational structure that can be exploited when performing policy evaluation. We then show that policy evaluation is asymptotically superior to a general FSC and that the degrees of freedom can be reduced while maintaining a large amount of expressivity in the policy. Specifically, we show that banded controller policy representations are equivalent to any FSC policy which is permutation similar to a banded controller. Meaning that banded controllers are computationally efficient policy representations for a class of FSC policies. Lastly, experiments are conducted which show that banded controllers outperform state-of-the-art FSC algorithms on many of the standard benchmark problems.