Reviewerly/exHarmony huggingface dataset:
Hybridised Intelligent Dynamic Model of 3-Satisfiability Fuzzy Logic Hopfield Neural Network

This study presents a new way of increasing 3SAT logic programming’s efficiency in the Hopfield network. A new model of merging fuzzy logic with 3SAT in the Hopfield network is presented called HNN-3SATFuzzy. The hybridised dynamic model can avoid locally minimal solutions and lessen the computing burden by utilising fuzzification and defuzzification techniques in fuzzy logic. In addressing the 3SAT issue, the proposed hybrid approach can select neuron states between zero and one. Aside from that, unsatisfied neuron clauses will be changed using the alpha-cut method as a defuzzifier step until the correct neuron state is determined. The defuzzification process is a mapping stage that converts a fuzzy value into a crisp output. The corrected neuron state using alpha-cut in the defuzzification stage is either sharpening up to one or sharpening down to zero. A simulated data collection was utilised to evaluate the hybrid techniques’ performance. In the training phase, the network for HNN-3SATFuzzy was weighed using RMSE, SSE, MAE and MAPE metrics. The energy analysis also considers the ratio of global minima and processing period to assess its robustness. The findings are significant because this model considerably impacts Hopfield networks’ capacity to handle 3SAT problems with less complexity and speed. The new information and ideas will aid in developing innovative ways to gather knowledge for future research in logic programming. Furthermore, the breakthrough in dynamic learning is considered a significant step forward in neuro-symbolic integration.
Detection of Animals for Road Safety Using Deep Learning Method

Wild animals must be monitored effectively and consistently in their natural environments. This project creates an algorithm to find animals in outdoors. The effort of manually recognizing all the various species might be challenging due to their diversity. This method categorizes animals based on their photos so that we can more effectively watch them. Animal tracking, animal accidents, and theft can all be avoided with the use of animal identification and classification. Deep learning algorithms can be used effectively to identify wild people. This process helps to save wild animals and also to prevent vehicles from accidents while crossing the road. The deep learning method alerts the users traveling in vehicles by locating the animals nearby to the vehicles passing the road.
An Approach For Cervical and Breast Cancer Classification Using Deep Learning: A Comprehensive Survey

The most common malignancies in women worldwide are breast & cervical, but very few researchers have examined how gender expectations affect diagnostic adherence provided sexual implications and physical contact. Considering these perceptions is essential to enhancing diagnostic accuracy and services since they may be a major factor in decision-making. As cervical and breast treatments for cancer to be effective, accurate early detection is essential. Machine learning and deep learning are being used by an expanding population and businesses to analyze vast volumes of data and provide useful insights. It has become quite frequent in clinical practices to use ML-based techniques to predict the initial stages of major illnesses like cancer, renal failure, and cardiovascular diseases. Several of the most prevalent diseases in women include cervical cancer, and early detection could help reduce mortality and morbidity. The paper provides a comprehensive analysis of the techniques and research issues widely employed now in the area. The causes, as well as mortality statistics of cancer, are also covered in this. The goal of the paper is to present a deep learning-centric system for earlier and more accurate breast and cervical cancer prediction. The Convolutional neural networks (CNN) were utilized in the progress of deep learning models. This research analyzes the effectiveness of the CNN model employing transfer learning with a model that has already been trained (VGG16). Considering the results, deep learning algorithms have the capacity to anticipate disease somewhere at the earliest stage at which fatality rates can be reduced.
Measurement Optimization Techniques for Excited Electronic States in Near-Term Quantum Computing Algorithms

The variational quantum eigensolver (VQE) remains one of the most popular near-term quantum algorithms for solving the electronic structure problem. Yet, for its practicality, the main challenge to overcome is improving the quantum measurement efficiency. Numerous quantum measurement techniques have been developed recently, but it is unclear how these state-of-the-art measurement techniques will perform in extensions of VQE for obtaining excited electronic states. Assessing the measurement techniques' performance in the excited state VQE is crucial because the measurement requirements in these extensions are typically much greater than in the ground state VQE, as one must measure the expectation value of multiple observables in addition to that of the electronic Hamiltonian. Here, we adapt various measurement techniques to two widely used excited state VQE algorithms: multistate contraction and quantum subspace expansion. Then, the measurement requirements of each measurement technique are numerically compared. We find that the best methods for multistate contraction are ones utilizing Hamiltonian data and wave function information to minimize the number of measurements. In contrast, randomized measurement techniques are more appropriate for quantum subspace expansion, with many more observables of vastly different energy scales to measure. Nevertheless, when the best possible measurement technique for each excited state VQE algorithm is considered, significantly fewer measurements are required in multistate contraction than in quantum subspace expansion.
A new method for constructing discrete hyperchaotic systems with a controllable range of Lyapunov exponents and its application in information security

Abstract Since people use chaos extensively for a wide range of applications in data encryption and secure communications, a new method for designing practical high-dimensional discrete hyperchaotic systems is proposed for the first time in this paper. This method controls the range of the Lyapunov exponents in reverse by adding control variables so that the range of the values of the Lyapunov exponents is controlled within a specified interval, which is more suitable for engineering applications. Then, it is mathematically proved that the method ensures that the orbits of chaotic systems are globally finite and their Lyapunov exponents are bounded. In addition, as a practical demonstration of the selective image encryption scheme based on target template matching introduced in this paper, a 6D discrete hyperchaotic system was created, and the analysis of the simulation results verifies the applicability of the 6D hyperchaotic system designed by the method presented in this paper in the field of image encryption.
Automated Recognition of Underwater Objects using Deep Learning

The underwater object detection is a difficult task due to the unclear visibility and lack of objects underwater in kovalam, it was expected to have visibility up to three to five meters but it is very complicated to detect deeper/underwater objects like shoals of anchovy and silver moony, Bluefin trevally, puffer fish, box fish, etc., The existing method focuses on identifying a single object underwater and have low accuracy (78%) to detect multiple objects in a single instance. The proposed solution intends to improve the accuracy of identifying objects underwater. The proposed work can be used in submarines, military purposes, and archeology to identify objects effectively. The proposed system employs novel techniques for precise object detection and techniques like YOLO and SSD along with precise object detection methods and algorithms like Convolutional Neural Networks (CNN). These artificial intelligence algorithms allow to detect every object in an image by the area it takes up in a highlighted rectangular box, identify every object, and tag every object.
A Convolutional Neural Network Based Classification Approach for Breast Cancer Detection

Thousands of women worldwide are diagnosed with breast cancer yearly, which may be fatal if not treated. The diagnosis of the condition may take years, by which time the patient has little choice except to have the affected breast removed. Early diagnosis and treatments are the best ways to stop this disease's spread. In this study, the authors presented a Computer Aided Diagnosis (CAD) system to assist in breast cancer diagnosis. The study uses the Wisconsin breast cancer dataset to classify benign and malignant data. For the classification, three pre-trained Deep learning algorithms: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), were used. A novel CNN model that exceeds the performance efficiency of three pre-trained models and requires minimal compilation time is proposed. A number of evaluation matrices are used to analyze the models' classification abilities. Upon closer inspection, it has been established that the proposed CNN model outperforms CNN, LSTM, and MLP models with validation accuracy of 97.85%. CNN and LSTM performed with accuracies of 94.12% with the Adagrad optimizer and 93.5% with the Adam optimizer, respectively. Furthermore, MLP performance with 92.44% accuracy using the Adam optimizer. The proposed CNN model achieves the lowest Loss value and compilation time. In addition, the models' recall value, precision, and f1-score are computed to pick out the most effective model for diagnosing breast cancer on numeric data.
A Comparison Study of Fraud Detection in Usage of Credit Cards using Machine Learning

Credit Cards are widely used in online transactions due to its usefulness and effectiveness. Despite the hype, the credit card fraudulent activities have inconsistent patterns of behaviour and frequently change them. Fraudsters are aware of new technologies to hack the online transactions. Credit Card Fraud is becoming more common due to the increasing use of credit cards. Designing and Creating a cutting-edge Machine Learning based Fraud Detection solution is the major goal of this research work. Machine learning forms the basis of the algorithms like XGBoost, Support Vector Machine, Decision Tree, Random Forest, and Logistic Regression. A CNN (Convolutional Neural Network) is then used to enhance the efficiency of fraud detection. Layers of a Convolutional Neural Network assists in obtaining accurate detection. An extensive empirical analysis was conducted using the most recent CNN model's hidden layer counts, epochs, and applications. The F1 score, accuracy, precision, and recall all affect the results of the algorithms. Area Under Curves (AUC) has been altered to leverage values of 99.9%, 85.71%, 93% and 98%. A ROC curve is produced using the confusion matrix as a framework. The proposed method overcomes the problem of Credit Card identification by combining Deep Learning with Machine Learning techniques. Furthermore, to reduce the number of false negatives, this study has performed data-matching trials with the implementation of Deep Learning Techniques. This study has employed Deep Learning techniques and perform trials to match the data with the model. Utilizing the suggested strategy, it is possible to locate Credit Card Fraud (CCF) remotely from any location.
High-Throughput Hardware Implementation for Haraka in SPHINCS+

SPHINCS+, a hash-based signature scheme, has stood out as one of the four winners in the post-quantum cryptography (PQC) competition hosted by the U.S. National Institute of Standards and Technology (NIST). However, the slow signing speed forms a bottleneck for applications. Therefore, a kind of short-input hash function named Haraka is recommended as the third instantiation in SPHINCS+ due to its advantage in processing speed. In this work, we propose four hardware architecture schemes for Haraka in SPHINCS+, denoted as Case I to Case IV. Several optimization methods are combined and applied in different cases to perform the trade-off between area and throughput for different application scenarios. We code our designs in System Verilog language and synthesize them under the TSMC 28-nm CMOS technology. The experiment results show that Case IV achieves the best throughput and the most efficient performance, about 81.92 Gbps and 1.26 Mbps/GE, respectively, which also significantly outperforms the state-of-the-art implementation of Haraka and the advanced hardware implementation of the SHA-3 hash function.
The Role of ERNIE Model in Analyzing Hotel Reviews using Chinese Sentiment Analysis

This research aims to find the best deep learning model to do Chinese sentimental analysis. BERT’s model may work well in the English language but not work in the Chinese language. English is easier to encode in embedding space, and words are separated by space characters, unlike Chinese words, which do not exist separate by the area. BERT Chinese model work on character level encoding is not enough to make the BERT model adapt to Chinese NLP tasks. The BERT Model and ERNIE models are the current state of art pre-training language models. Word level embeddings and input is needed to improve the BERT model to perform in Chinese Sentiment Analysis. With the motivation to improve the Chinese Sentiment Analysis, this study will explore the role of different models to propose a better version of the ERNIE model. This study will limit the scope to improve Chinese sentiment analysis among different NLP tasks.
Research Challenges and Future Directions for Data Storage in Cloud Computing Environment

The flexibility and reliability with which cloud computing can deliver a wide variety of computation, memory, communication, and resource management services through the internet have contributed to its rise in popularity in recent years. Increasing interest in cloud computing is due to the growing need for reliable, affordable infrastructure. This essay largely focuses on an overview of problems and unresolved difficulties with cloud databases as storage for cloud computing. This survey’s objective is to give a researcher a broad perspective on the problems, unresolved problems, and emerging trends in cloud storage, with a particular focus on cloud databases. This article compares academic approaches and methodologies to address the difficulties related to data availability, including replication, managing, and securing data in cloud enabled technologies.
Modified Encryption Standard for Reversible Data Hiding using AES and LSB Steganography

The development in the cloud computing technologies and the information security policy, the data recovery from the hidden technologies is improved and also it is most implemented methodology. The recovered data from the embedded can also be error free and the redundancy is very low even though it was encrypted using different available encryption algorithms. The traditional algorithms embed the data and recover using the dame approach. We proposed encryption algorithm using Least Significant Bit (LSB) and Auxiliary syndrome Encryption Standard (AES). In this approach, we first create an auxiliary syndrome for the data using AES algorithm and then embed the data in the least bit extracted using the wavelets of the cover image. The message is inserted into the first sub-image of the cipher picture after image encoding and encryption, and each pixel can load a bit of secret message. The embedded data can be effectively retrieved and the original picture can be correctly restored on the receiver side utilizing the encryption key and extracted auxiliary syndrome. The proposed method gave the best analysis in terms of Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER).
Text Detection and Language Identification in Natural Scene Images using YOLOv5

Deep learning has immensely evolved ever since digital era. Deep learning also includes feature extraction as a facet. Text snipping from a picture is a difficult task since the image comprises text in a variety of sizes, styles, orientations, alignments, low contrast, noise, and with a complicated backdrop structure. Transformation of an image into different perspective for feature identification is the first step towards text recognition. Scene texts provide rich contextual information that can be applied to several types of vision-based applications, hence over the last few years we have witnessed an increase in interest in the detection and recognition of scene texts. In order to address the issue of language detection from multilingual scene text photos, a deep learning-based solution is suggested in this paper. In this study, the underlying model of a Convolutional neural network is employed to detect objects in real-time with high accuracy. This study employs a single neural network “you only look once" known as YOLO, since it offers predictions with just a single forward propagation trip through the neural network to evaluate the full image. We used COCO ‘Common Objects in Context' dataset which is a large-scale object detection, segmentation, and captioning dataset. To evaluate the image YOLO divides the image into smaller parts and forecasts boundary areas and probabilities for every part. The predicted probability weighs these region proposals. It then provides identified objects after non-max linear suppression. We used F1-score which combines accuracy and recall into a single metric by computing their harmonic means.
Proportionate Adaptive Graph Signal Recovery

This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence than least mean squares (LMS) algorithm. In this paper, the gain matrix is obtained in a closed-form by minimizing the gradient of the mean-square deviation (GMSD). The first algorithm is the proportionate-type graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm. The second algorithm is the proportionate-type graph extended LMS (Pt-GELMS) algorithm, which uses the previous signal vectors alongside the signal of the current iteration. The Pt-GELMS algorithm utilizes two gain matrices to control the effect of the signal of the previous iterations. The stability analyses of the algorithms are also provided. Simulation results demonstrate the efficacy of the two proposed proportionate-type LMS algorithms.
Latency-Aware 360-Degree Video Analytics Framework for First Responders Situational Awareness

First responders operate in hazardous working conditions with unpredictable risks. To better prepare for demands of the job, first responder trainees conduct training exercises that are being recorded and reviewed by the instructors, who check for objects indicating risks within the video recordings (e.g., firefighter with an unfastened gas mask). However, the traditional reviewing process is inefficient due to unanalyzed video recordings and limited situational awareness. For better reviewing experience, a latency-aware Viewing and Query Service (VQS) should be provided. The VQS should support object searching, which can be achieved using the video object detection algorithms. Meanwhile, the application of 360-degree cameras facilitates an unlimited field of view of the training environment. Yet, this medium represents a major challenge because low-latency high-accuracy 360-degree object detection is difficult due to higher resolution and geometric distortion. In this paper, we present the Responders-360 system architecture designed for 360-degree object detection. We propose a Dynamic Selection algorithm that optimizes computation resources while yielding accurate 360-degree object inference. The results, using a unique dataset collected from a firefighting training institute, show that the Responders-360 framework achieves 4x speedup and 25% memory usage reduction compared with the state-of-the-art methods.
Curating and extending data for language comparison in Concepticon and NoRaRe

Language comparison requires user-friendly tools that facilitate the standardization of linguistic data. We present two resources built on the basis of a standardized cross-linguistic format and show how the data is curated and extended. The first resource, the Concepticon, is a reference catalog for standardized concepts from linguistic research. While curating the Concepticon, we found that a variety of studies in distinct research fields collected information on word properties. However, until recently, no resource existed that contained these data to enable the comparison of the different word properties across languages. This gap was filled by the Database of Norms, Ratings, and Relations (NoRaRe), which is an extension of the Concepticon. Here, we present the major release of both resources - Concepticon Version 3.0 and NoRaRe Version 1.0 - which represents an important step in our data development. We show that extending and adapting the data curation workflow in Concepticon to NoRaRe is useful for the standardization of cross-linguistic datasets. In addition, combining datasets from different research fields enables studies grounded in language comparison. Concepticon and NoRaRe include lexical data for various languages, tools for test-driven data curation, and the possibility for data reuse. The first major release of NoRaRe is also accompanied by a new web application that allows convenient access to the data.
Semi-supervised classifier ensemble model for high-dimensional data

To complete the challenging task of high-dimensional data classification with limited labeled samples, we propose two semi-supervised learning models, namely the random subspace classifier ensemble model (SSRS) and its adaptive version (ASSRS). Considering the unique characteristics of high-dimensional data, SSRS selects subspaces of sample and feature dimensions and then reduces the dimensions of each subspace. To improve SSRS performance further, we designed a sample-labeling auxiliary algorithm, adaptive sample subspace algorithm, and adaptive weight voting rule for ASSRS to increase the proportion of labeled samples, obtain a suitable sample subspace for each feature subspace, and acquire a relative optimal weight for each base classifier. Experiments revealed that the performances of SSRS and ASSRS were better than those of other competitive algorithms and that the performance of ASSRS was stronger than that of SSRS. Additionally, we can accurately label samples in datasets where the proportion of labeled samples is relatively low by using SSRS and ASSRS. Because analysts are facing large numbers of high-dimensional datasets with limited labels, it is important to make accurate predictions based on a limited proportion of labeled data.