Reviewerly/exHarmony huggingface dataset:
ATM Interface Using Culture Empowered Low Literate Users

The utilization of banking robotized automated teller machine (ATM) mechanical advancements has huge significance and advantages in Pakistan, however unskilled and semi-literate Pakistanis addressing about 40.33%, don’t see them as valuable or simple to utilize. The motivation behind this contextual analysis was to distinguish procedures utilized by interface developers of ATM frameworks to make simple-to-utilize ATM interfaces for Pakistani people. One association in Pakistan was utilized for this current investigation’s populace. Information was gathered through semi-organized, inside and out interviews with nine financial ATM framework interface engineers and the investigation of 11 archives. Discoveries from the members were approved through part-checking and hierarchical records. Through methodological triangulation, one significant topic arose: the client-focused we need to incorporate five important parts: (i) client-focused plan measures, (ii) significance, comprehension of the social foundations and education levels of clients, (iii) plan dependent on information and clients’ requirements, (iv) understanding the objective of clients. (v) straightforward, easy to use, and simple to utilize the plan. These discoveries in this investigation give guidance for the future advancement of procedures to make simple-to-utilize ATM framework interfaces for individuals with shifting capacities, proficiency levels and other data innovation frameworks that are UI innovation subordinate.
Adopting Centroid and Bandwidth to Shape Security Line

The voice patterns of gender and age have been used as speaker's identity and implied into sectors such as smart cards, healthcare, banking, and other security access controls. However, age, illness, and ambient noise factors exist challenging issues of voice changes in speech recognition systems to be resolved. This paper presents experiment results of observing those issues, and its goal was obtaining the elements of voice features to secure the speaker's identity. Mel-frequency Cepstral Coefficient (MFCC) was used to conduct observations on spectral centroid and bandwidth, it has effectively performed capturing features of individual speech signal. The experiment was conducted with data split into three groups: under 13, range 13 to 19, and above 19 years old for both male and female. The results showed that both gender under 13 have centroid voices reached to 98%, age of 13 to 19 remain with centroid reached 98% of bandwidth for male and passing over 100% for female, then above 19 for both genders speak lower with centroid 50% of bandwidth, and male above age of 19 remains to keep centroid about 50% and female changes to 95%. MFCC fits to expose the speech spectrum for the purpose of an analysis frame on specific local spectral properties of voices, and the finding is to form a security line or an individual profiling adoption.
Academic Certificate Validation Using Blockchain Technology

Academic certificates are essential for an individual's career and hence they are more prone to being tampered. This paper proposes an idea of sharing certificates and verifying their authenticity using blockchain technology. Blockchain paves the way for secure storage and sharing of information. Its main focus is to maintain trust among users. This proposal focuses on designing and implementing a system that will prove to be a solution for addressing the issue of fake certificates using Hyperledger Fabric. The technology here is tamper-proof and maintains transparency. This system will have a database of academic certificates awarded by the University, which is recorded as a transaction using the Hyperledger Fabric, which further can be referred by other organizations present in the network to verify the authenticity of the certificates using the information provided by the students to the database. This system provides end to end encryption.
Handling sparsity and seasonality problems simultaneously in session-based recommender systems using graph collaborative filtering

Session-based recommender systems have evolved as a new paradigm in recent years, intending to capture short-term yet dynamic user preferences to give more timely and accurate suggestions that are responsive to the change in their session contexts. However, sparse data for user-item interaction has been one of the significant essential issues as we need a colossal amount of memory to store those sparse data. Seasonality is another major issue in recommendation systems as there are many variations in the pattern of customers’ interests at different time intervals. In our study, we resolve the above mentioned issues by using graph collaborative filtering and creating feature bins. As a case study, we used sequential data from YooChoose customers to validate the efficacy of our proposed methodology. Further, we use five state-of-the-art graph neural network models to get the best recommendation. The performance of those models is evaluated using the NDCG (Normalized Discounted Cumulative Gain) and ROC-AUC (Area under the Receiver operating characteristic curve) metrics. In our study, we find out that Residual Gated Convolutional Neural Network with four layers and Adam optimizer gave the best recommendations.
Building an Industry Standard Novel Language Model Using Named Entities

In every Industry, there is a significant amount of text used in their specific domains. As these are less prevalent in the testing set, anticipating entity names in a language model is a problem faced by the entire industry. In this research a unique and very effective strategy for creating exclusionary classification models that could map entity names based on entity type information is provided. A group of benchmark datasets based on Mortgage is presented, which we used to test the below-presented model. According to experimental findings, our model achieves a perplexity level that is 64% higher than that of the most advanced language models.
Benchmarking Speed of Post-Quantum Lattice Based PKE/KEM Schemes Using Liboqs

Recent advances in quantum computing have high-lighted the vulnerabilities in contemporary RSA encryption. Shor's approach for factoring numbers is becoming more tractable as quantum computing advances. This jeopardizes the security of any cryptographic system that is based on the complexity of factorisation. Many other crypto-systems based on theories like Elliptic Curve Cryptography are also vulnerable. To keep a cryptographic system safe against a quantum adversary, we must develop approaches based on a hard mathematical problem that is not vulnerable to quantum computer attacks, and we must develop Post Quantum Cryptography (PQC). One potential option is the use of lattices in a system called ring Learning with Errors (rLWE). Several techniques for postquantum encryption have been submitted to NIST. This paper studies the different speeds of different lattice-based protocols.
Benchmarking Quantum Algorithms on Xanadu, IBM, and Google Quantum Computers

NISQ-era Quantum computers are slowly getting deployed to real-world use cases. Quantum Computation facilitates speedy solutions to a wide range of complex problems. To monitor progress towards more advanced devices, we can characterize their dynamics and analyze their performance using certain benchmarks for determining their computational capabilities. In our research, we simulated and analyzed Bose Hubbard Hamiltonian in Google, IBM, and Xanadu Quantum Computers and calculated the accuracy and speed. Then we solved one of the NP-hard problems called the Maximum clique and computed its execution speed.
Cybersecurity as a Tic-Tac-Toe Game Using Autonomous Forwards (Attacking) And Backwards (Defending) Penetration Testing in a Cyber Adversarial Artificial Intelligence System

In this paper, we investigate the potential behavior of Artificial Intelligence (AI) black hat hackers and the ability of ethical human hackers to defend against them. To this end, we develop an adversarial AI testbed, which marries game board play to the cyber realm by employing a statistical AI algorithm that maps every move made on the tic-tac-toe game board to an attack or defensive move made in the cyber realm. Our work demonstrates that this approach is an effective means of constraining the possible cybersecurity state space for autonomous forwards (i.e., attacking) and backwards (i.e., defending) penetration testing. Our results suggest that welltrained AI hackers may be nearly impossible to beat by human defenders unless prior knowledge of their possible attack strategies are known; however, for humans, this approach quickly becomes intractable. Also, we have observed that different AI algorithms used to search (play) the game state space (and thus the cyber state space) behave as different AI hacker personalities, which illustrates how forwards and backwards penetration testers with varying skills and knowledge would exploit or defend network vulnerabilities.
Detection of Face with Occlusion Using Deep Learning Techniques

The objective of this is to find various researches being done for detection of hidden face while there is any occlusion and another objective is to find best deep learning technique used till date for finding the face of a person who might be involved in certain criminal activities. Due to the impact of coronavirus world has shattered so wearing a face mask is mandatory action as a prevention from the disease but people are covering their face to do certain offences. It is found that various deep learning techniques has got success in recognizing the face while wearing a mask but still there remains an area of improvising.
Quantum Powered Employee Transport and Agri-Logistics Optimization

Logistics involves managing how resources are acquired, stored, and transported to their final destinations along the supply chain in a cost-effectively manner. Logistics problems are NP-hard combinational optimization problems involving searching for the best solution in a large solution space. Recent studies have demonstrated the capability of quantum methods to solve such combinatorial optimization problems. Consequently, this paper presents the application of quantum computing methods for two real-world logistics problems, namely, a) Employee transport route optimization and b) Agri-tech logistics optimization. The main goal of this paper is to present the evaluation of the results of the experiments executed on quantum gate-based variational algorithms and quantum annealers. Given the current limitation of quantum hardware, we had to simplify the problem and use case specific decomposition techniques such as distance based clustering to reduce the search space and size of the problem before executing them on quantum computers. The method has been elaborated in this paper. The results were benchmarked with classical exact solvers.
An Improved DeepFake Detection Approach with NASNetLarge CNN

Deep fake images are a new technology that has emerged with the development of computer vision and deep learning technologies in recent years. The development of these deep fake technologies has led to the production of many fake or manipulated products. Thus, the problem of detecting the deep fake has emerged and many methods have been developed to solve this problem. In this study, feature extraction and classification method on the dataset with NASNetLarge CNN deep learning model is proposed and a successful result is produced. In the proposed method, training and test datasets were created by removing facial regions from the video frames in the Celeb-DFv2 dataset. The architecture of the NASNetLarge model is explained and the success of the model is tested. According to the test results, an ACC value of 96.7% was obtained and compared with other methods. As a result, the study offers an easier model training with a smaller dataset than other methods and produces a competitive and successful result.
Variational Quantum Algorithms for Chemical Simulation and Drug Discovery

Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein folds distinctively, and the difficulty of finding its stable shape rapidly increases with an increase in the number of amino acids in the chain. A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous. At some point, the number of these combinations will be so vast that classical computers cannot even attempt to solve them. In this paper, we examine how this problem can be solved with the help of quantum computing using two different algorithms, Variational Quantum Eigensolver (VQE) and Quantum Approximate optimization Algorithm (QAOA), using Qiskit Nature. We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance. Further, we make comparisons with SoTA algorithms and evaluate the reliability of the method.
A Decomposition-based Constrained Multi-objective Evolutionary Algorithm with An Infeasibility Utilization Mechanism for UAV Path Planning

Evolutionary algorithm has been used to solve unmanned aerial vehicle (UAV) path planning problems. Usually, a UAV path planning problem is modelled as a constrained optimization problem, and the goal for evolutionary algorithms is to find a collision-free trajectory with respect to the constraints, which requires the algorithms equipping not only a powerful search engine but an effective constraint-handling technique. In the community of constrained multi-objective evolutionary algorithms, an essential idea is how to make good use of the informative infeasible solutions during the evolution process, which can significantly improve performance of the algorithms. To the best of our knowledge, this has seldom been explored in UAV path planning problems. To address this issue, this paper proposes a decomposition-based constrained multi-objective evolutionary algorithm with an infeasibility utilization mechanism for solving UAV path planning problem. UAV path planning represented by the B-Spline curve is first formulated as a bi-objective optimization problem, i.e., minimizing the travelling distance and the risk of a UAV, with three constraints including the minimum flight height, the maximum flight height and minimum flight angle. Then a decomposition-based constrained multi-objective evolutionary algorithm that can utilize the information containing in infeasible solutions is adopted to solve the constrained optimization problem. To further make good use of the infeasible solutions found by the algorithm, an infeasible utilization mechanism is proposed to guide the search to the optimal regions. The experimental results have indicated that the proposed algorithm is superior over the compared algorithm in terms of finding a set of well-distributed and well-converged non-dominated solutions.
The Network of Attention-Aware Multimodal fusion for RGB-D Indoor Semantic Segmentation Method

In recent years, convolutional neural networks have effectively improved the accuracy of semantic segmentation tasks. However, semantic segmentation of indoor scenes is still a challenging problem due to the complexity of indoor environments. With the advent of depth sensors, the use of depth information to improve semantic segmentation has been considered. Most of the previous studies simply use equal-weight stitching or summation operations to fuse RGB features and depth features, failing to make full use of the complementary information between RGB features and depth features. In this paper, we propose the network of attention-aware multimodal fusion for RGB-D indoor semantic segmentation method. By designing an attention-aware multimodal fusion module, the multilevel RGB features and depth features are effectively fused. The cross-modal attention mechanism is also introduced in the multimodal fusion module, and the features are fused at different scales so that RGB features and depth features can guide and optimize each other using complementary information, thus extracting feature representations rich in spatial location information. The method can effectively promote the synergistic interaction of multimodal features and further improve the effect of semantic segmentation. Experimental results on publicly available datasets SUN RGB-D, NYU Depth v2 and ScanNet show that our designed algorithm outperforms other RGB-D image semantic segmentation algorithms.
Quantum Image Teleportation Protocol (QITP) and Quantum Audio Teleportation Protocol (QATP) by using Quantum Teleportation and Huffman Coding

In order to transmit images and audio securely, the authors present the Quantum Image Teleportation Protocol (QITP) and Quantum Audio Teleportation Protocol (QATP), which utilizes the Quantum Teleportation (QT) technique combined with Huffman Coding. The QITP secures the teleportation of quantum states of an image while simultaneously encrypting and decrypting them using Huffman Coding since it is only possible to recover or decode data if the prefix codes are known. To test their approach, the authors transformed pixels or RGB values from digital images into text, which was then fed into the Huffman Coding Technique. It has the advantage of compressing the entire text, which makes it faster to transmit vast amounts of information. This work also demonstrates the Quantum Audio Teleportation Protocol (QATP) with and without Huffman coding. For proof of concept, experimental evaluations were performed for both suggested QITPs and QATPs (Standard QITP, QITP with Huffman Coding, Standard QATP, QATP with Huffman Coding), using IBM Quantum Assembly Language (IBM QASM) Simulator and real quantum hardware using the Quantum Information Science Kit (Qiskit), a quantum computing platform.
Convergence Analysis of Interval Feedforward Neural Networks

In practical application of neural networks, we often try to change some factors to make the algorithm perform better, but the convergence analysis of the algorithm cannot keep up with it in time. This paper studies the convergence of interval feedforward neural network (IFNN). First the convergence of the IFNN is derived theoretically based on gradient descent algorithm, where the IFNN possesses the interval input and output but the point weights; Then, by means of numerical analysis, the relationship between the convergence of general IFNN and interval weights is discussed. In the numerical analysis, the coverage of interval output is taken as the objective function, and the particle swarm optimization (PSO) algorithm is used for optimizing to ensure the interval network output approaching the interval width range of the weight and threshold of the point output. Studying the convergence of interval neural networks (INNs) can effectively promote the application range of INNs, making INNs better used in uncertain systems, reducing the impact of fluctuations, noise and measurement errors, and improving the objectivity and robustness of industrial process modeling.
Early Prediction of Breast Cancer through Deep RNN Approach

Cancer is the foremost cause behind the most death pace of people around the world. Breast cancer among females is the foremost cause of deaths worldwide. There have been various investigation or experimentation aimed at the discovery and interpretation of facts taken on initial expectation and discovery of chest cancer disease to initiate action & increase the opportunity of fortitude. Utmost research targets x-ray pictures of the breasts. Although, photographs of the breasts made by X-rays occasionally produces a threat of fake recognition which can compromise the medical status of infectious person. It’s crucial and important to discover disease detection systems which is simpler to invest into impact and effort with exceptional datasets, modest and more secure, which could deliver an additional reliable prognosis. The proposed manuscript recommends an associated model of diverse Deep Learning Algorithms (DLA)comprising Artificial Neural Network (ANN) and Recurrent Neural Networks(RNN) for efficiently detecting and predicting breast cancer disease. The study exploration utilizes the x-rays facsimile database (as base research datasets) for prediction, detection, and diagnosis of breast cancer. This anticipated research prototype may be associated with several clinical examination data i.e. text, audio, image, video, blood, urine and many more.
Research on Manipulator Control Based on Improved Proximal Policy Optimization Algorithm

In the scene of random patching in the industrial scene, an algorithm based on a distributed frame of proximal policy optimization (PPO) with Generalized Advantage Estimation (GAE) is proposed in this paper. The visual part is taken from camera, which is considered as state input. A distributed approach (actor-critic) is established to improve the efficiency of sampling. The sampling data are stored in the experience pool. Both punishment and reward strategies are considered in the raised method. The improved PPO algorithm can be verified on Pybullet. We found that it greatly improves effect in terms of convergence steps and actual reward performance.
Multilingual Sentiment Analysis of YouTube Live Stream using Machine Translation and Transformer in NLP

YouTube has become one of the all-inclusive video streaming sources on the internet. Today, the news is streamed on YouTube, marketing of a product is done live on YouTube and it has become a platform for one of the biggest PR producers for companies. Various companies have proposed an optimized way of understanding and getting the opinions of the viewers from YouTube live chat and find the best possible way to provide relevant and informative content to boost the business strategy. This study uses Natural Language Processing (NLP) based approach along with NLP transformers to classify and analyses the sentiment.
Quantum Text Teleportation Protocol for Secure Text Transfer by using Quantum Teleportation and Huffman Coding

In this work, the authors present Quantum Text Teleportation Protocol (QTTP) that uses Quantum Teleportation (QT) technique and Huffman Coding for secure text transfers. The QTTP enables the teleportation of quantum states of text (for example, email) in a secure manner, while simultaneously encrypting and decrypting them using Huffman Coding since data can only be retrieved or decoded if the prefix codes are known. The Huffman Coding approach offers the benefit of compressing the entire text, resulting in faster transmission of large amounts of information. For proof of concept, the authors experimentally evaluated both of the proposed QTTPs (Standard QTTP and QTTP with Huffman Coding) using Quantum Information Science Kit (Qiskit), a quantum computing platform and simulated on IBM QASM Simulator and on IBM real quantum hardware.