Reviewerly/exHarmony huggingface dataset:
Bayesian modeling of the temporal evolution of seismicity using the ETAS.inlabru package

The epidemic type aftershock sequence (ETAS) model is widely used to model seismic sequences and underpins operational earthquake forecasting (OEF). However, it remains challenging to assess the reliability of inverted ETAS parameters for numerous reasons. For example, the most common algorithms just return point estimates with little quantification of uncertainty. At the same time, Bayesian Markov chain Monte Carlo implementations remain slow to run and do not scale well, and few have been extended to include spatial structure. This makes it difficult to explore the effects of stochastic uncertainty. Here, we present a new approach to ETAS modeling using an alternative Bayesian method, the integrated nested Laplace approximation (INLA). We have implemented this model in a new R-Package called ETAS.inlabru , which is built on the R packages R-INLA and inlabru . Our study has included extending these packages, which provided tools for modeling log-Gaussian Cox processes, to include the self-exciting Hawkes process that ETAS is a special case of. While we just present the temporal component here, the model scales to a spatio-temporal model and may include a variety of spatial covariates. This is a fast method that returns joint posteriors on the ETAS background and triggering parameters. Using a series of synthetic case studies, we explore the robustness of ETAS inversions using this method of inversion. We also included runnable notebooks to reproduce the figures in this article as part of the package's GitHub repository. We demonstrate that reliable estimates of the model parameters require that the catalog data contain periods of relative quiescence, as well as triggered sequences. We explore the robustness of the method under stochastic uncertainty in the training data and show that the method is robust to a wide range of starting conditions. We show how the inclusion of historic earthquakes prior to the modeled time window affects the quality of the inversion. Finally, we show that rate-dependent incompleteness of earthquake catalogs after large earthquakes have a significant and detrimental effect on the ETAS posteriors. We believe that the speed of the inlabru inversion, which includes a rigorous estimation of uncertainty, will enable a deeper exploration of how to use ETAS robustly for seismicity modeling and operational earthquake forecasting.
An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy

Abstract This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the algorithm’s search speed and accuracy. Experimental results on 18 benchmark functions revealed that the proposed algorithm yields superior performance in terms of convergence speed, optimization accuracy, and ability to jump out of the local optimal solution compared with the standard LSO, gray wolf optimizer, and particle swarm optimization algorithms. Furthermore, the improved LSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the effectiveness of the proposed algorithm was further verified by studying the house price prediction problem using two real-world datasets.
Segment a cam contour with generalized cross product

Cam contour fitting is greatly affected by data segmentation errors. Yet traditional segmentation methods are susceptible to data noise and not able to cope with contour of multiple curve type. Therefore, a new method is proposed based on Generalized Cross Product (GCP), and the segmentation of arc cam contour is taken as examples. Regarding the contour as a series of arc fragments, circular features of each fragment are transformed to a GCP norm by vector mapping. Based on the robustness of GCP, the disturbances of data noises on the GCP norms of a segmented arc are suppressed while those on its adjacent arc are amplified in the mapping. Then there is a sharp contrast in between the GCP norms, i.e. the differences of the features of 2 adjacent arcs are exhibited distinctly. Thus the dividing point between 2 segments can be easily identified. By this way, the cam contour is segmented accurately. Most importantly, the accuracy can be greatly improved through comparing the vector mappings of different mapping parameters. It is validated by experiments that the method can apply to cam contour of various curve type, including spline curves, and has strong robustness against data noise.
Trustworthy Blockchain Based Certificate Distribution for the Education System

Due to increased mobility and growth in global students, as well as the current issue of diploma forgery and the sale of forged certificates, the storage and sharing of student educational records continues to present numerous privacy challenges. This paper proposes block chain-based decentralized certificate management systems in higher education. In this scheme, diploma management tasks like preserving, issuing, and verifying are implemented by the Proof of Work consensus algorithm. Encrypted data, keywords, and digital signatures are stored using cryptographic algorithms like (secure hash Algorithm) SHA-256 and (Elliptic Curve Digital Signature Algorithm) ECDSA. This system allows any individual student to be able to store and access educational certificates. College students can send their certificate hash value to verify their certificates so that organizations can verify the students' certificates using blockchain. Security and performance evaluations reveal that security and privacy preservation are possible with this proposed system. The proposed method is executed using Ethereum and the Solidity programming language. The proposed methodology can be employed by any university to tailor its education to the unique requirements of professional fields recognized by the industry's employers. Additionally, this method offers companies a platform where the employees' academic histories may be checked. This makes it simpler for students, organizations, and other sectors to get consent for student data.
Post Quantum Steganography for Cloud Privacy Preservation using Multivariable Quadratic Polynomial

The cryptography and steganography can be employed as an appropriate tool for improving the confidentiality of transmissions over the cloud environment. Several of the prevailing steganographic systems are based on the public key steganographic systems such as RSA and ECC where the privacy is based on the complexities in addressing the numerical factorization issues and distinct log-based issues. Moreover, the schemes for addressing the numerical factorization issues and distinct log-based issues enhances statistically. Hence the presence of quantum computing within the extent of 1000 bits can be real-world risks to the system with these issues. The goal is to propose a fresh Quadratic Polynomial Based Post Quantum steganography system that aggregates the post-quantum cryptography with steganography for assuring safety during cloud communication that will be preserved both for traditional computing and post-quantum computing age.
Two Stage Image Restoration based on Histogram Equalization and Edge Computation for Image

Image restoration is the basic technology in image processing to increase the contrast and brightness of the image. Also, this technique should help to characterize the content of an image with high efficiency. It is widely used in many applications. This paper introduces the potential of the two stages functional in an image for image restoration. In the first stage, pair wise pixel histogram equalization is introduced to enhance the image content. For the betterment in the visual quality of foreground region present in the image, the image are analysed by finding edges of the object in the second stage. Also, markov chain transition is used for efficient edge detection.
Fight Detection in surveillance video dataset versus real time surveillance video using 3DCNN and CNN-LSTM

The abundant presence of surveillance cameras result in huge volumes of video data, which need to be monitored constantly. Real time fight detection from surveillance videos will help in preventing or stopping the fight. Fights in parking lots, bars, restaurants and public places can be avoided if there is a system that does real time detection. The proposed system compares the fight detection accuracy in surveillance video dataset by applying two approaches namely 3DCNN – Three Dimensional Convolutional Neural Network and CNN-LSTM- Long Short Term Memory network. The Surveillance Camera Fight dataset is used in this work for fight detection. 3DCNN and CNN-LSTM provide 86% and 87% accuracy respectively in classifying fight actions with the test dataset. The proposed work also includes survey and analysis of fight detection using the real time streaming surveillance video. The proposed model is tested on webcam streaming device which captures the video in real time, preprocessed and analyzed using both 3DCNN and CNN-LSTM prebuilt trained models. The real time fight detection ended up with significantly fairer accuracy with lots of challenges in implementation.
Implementation of PV based solar water cooler with enhanced chiller performance using Peltier module

In the last decade and recently they have developed various cooling methods but fewer things are available in the compact manner. In general, the water coolers are available in a large manner and some tiny ones use huge power which is from an external source. Available in the compact manners are Conduction plates and heat pipes, Immersive cooling system and Chilled water-cooling system. Where the above functions of Heat are locked at the connecting place between the copper tubing and the components, heat is then sent to the connecting place between the copper tubing and a solid conduction plate placed on the side of the server. The computers are placed in a non-conductive liquid allowing the central processing unit, graphics processing unit, and the heat is dissipated directly to the liquid. The compacted refrigerant is worked with a condenser that rejects the power from the refrigerant to the environment or a liquid coolant or something to that effect. To make a solar water cooler in a compact size and to run on solar energy so that it can be portable to any place and runs on its own.
Determining the Optimizer Performance of CNN for Face Recognition

Face recognition is the most widely used method of identifying and verifying an individual. Face recognition systems can detect and identify people in photos, videos, and in real time. Face recognition is widely used for biometric and security purposes because it can capture a wide range of information and does not require physical contact for verification. Deep learning is used to improve the efficiency of face recognition. A convolutional neural network (CNN) is a deep learning algorithm that has found widespread success in a variety of real-time applications. This algorithm overcomes the limitations of the machine learning algorithm. The CNN's performance is primarily determined by the network's architecture, optimizer, and parameters. There are several optimizers available to assist CNN obtain an optimal solution through quick convergence. The performance of several optimizers for different face datasets is tested in this work in order to discover the best optimizer for face recognition. This study gives a thorough grasp of several optimizers.
Speech Emotion Recognition: Node.js and Web Speech API

Speech Emotion Recognition (SER) is a field of study that seeks to extract the emotion from speech signals. It is one of the most challenging tasks in the speech signal analysis domain. In this paper, we are going to present a web application using Web speech API, AFINN, Node.js. In this model the Web speech API available in Google which makes the web apps to handle voice data and it also helps us to convert the speech to text and make a request to the Node.js server with the text, Node.js is a server which helps us to evaluate the text using AFINN's list to return the score, where AFINN is a word list based approach for sentiment analysis. It evaluates the part where emotion is present and after the evaluation of the text the browser displays a different emoji depending on the score. We are building this model so that the user/call center employee can recognize customer's emotions from speech and can improve their service and converse with more people.
Influence of DLT in the E-Governance Strategy

The Distributed Ledger Technology (DLT) technology is one of the future drivers of distributed form of data handling especially with blockchain dominating with immutable and secured e-transactions. With its influence and pre-dominant implementation in various sectors like banking, DLT has different use-cases applied in the E-Governance too. Countries like Estonia have come up with various blockchain based e-services for more transparency and privacy. Smart contracts / chain code in the permissioned blockchain could provide the basic Smart Governance needs such as simplified beneficiary authentication, transparency, privacy, security, automation, cost control, faster processing time and so on. This shows the DLT as a catalyst for automation in the future of Smart Governance along with other technologies like IoT, digital twin and so on. This paper analyses the benefits and challenges of blockchain based E-Governance implementation as well as the different DLT types available such as DAG, hashgraph, etc. apart from the blockchain and their potential for an effective citizen centric E-Governance.
Deep Learning assisted Cervical Cancer Classification with Residual Skip Convolution Neural Network (Res _ Skip _ CNN)- based Nuclei segmentation on Histopathological Images

Cervical cancer (CC) remains the second most typical cancer in women internationally having a death too of sixty percent. CC starts with nil obvious symptoms and possesses a lengthy latency turning initial diagnosis via periodical health checks extremely significant. The problem of automatic technique to identify CC is proffered for enhancing the detection's precision. The CC histology source images will be required for employing image preprocessing to lessen the effect resulting from the images noise alongside the effect upon succeeding accurate feature extraction resulting in impertinent background. The images will be segmented employing the Residual Skip Convolution Neural Network (Res_Skip_CNN) centered upon 4 renowned discriminative attributes: i) nuclei's proportion to the cytoplasm, ii) nuclei's diameter, iii) shape factor, iv) nuclei's roundness. The random forest classification paradigm would be considered as an input to the cervix's segmented image having the epithelium layer for aiding the diagnostician in CC detection. We assess the execution of the proffered Res_Skip_CNN with RF (Res_Skip_CNN-RF) classifier opposing the Herley datasets employing a variable classes quantity while doing image classification. Consequently, it is observed that the proffered Res_Skip_CNN-RF attains 95% of Accuracy, 93% of Precision, 89% of Recall, and 85% of Fl-Score.
VSCODE- Code With Voice Using Natural Language Processing(NLP)

Physically Challenged people who are interested in coding, in the current scenario cannot code and exhibit their knowledge in programming skills. Our project enables them to code without using a keyboard. They can code entirely by their voice and execute their code. We are converting the user's speech to text by Natural Language Processing (NLP). With the use of text obtained from NLP model we generate code snippets using our custom code generator API. The code snippets are generated in the Visual Studio Code text editor using our own VSCode extension.
Machine Learning Based Smart Social Media Mining for Disorder Communication Detection

Social Media are gaining momentum in this digital era. Data is being shared on social media on random intervals across the world. The Population of the persons in social media has overcome the population of certain countries of the world, which implicates the growth of the social media at this juncture. Any news or posts in the social media can be so trending that the post get viral within fraction of time, thus attracting the attention of the other persons. Whilst this can be taken in the positive account, precautions have to be taken to avoid the social media being misused. Any news or posts related to hatred, spread of misappropriate content relating to religion, sex, countries or borders, or any other news that can harm or spread hatred towards any community or people has to be stopped, else it may create havoc or damages of property or a brand that can affect the lives and the reputation of many. This paper puts out a novel approach to identify those mental disorders communication from the social media accounts by using machine learning approach.
Cryptocurrency: the good, the bad and the ugly

Cryptocurrency is the technological development of currency into the digital form which is considered for sustainable development. There are large numbers of investors, who have started investing on cryptocurrency and it is the emerging financial market across the globe. But the cryptocurrency poses various threats to the investor safety and security and it is hazardous to the environmental condition. There is still no consensus on cryptocurrency whether it is a better option to invest or not. Hence this studies to shed light on to the path of investment opinion about cryptocurrency. This paper will give the perspective of the current trends in the crypto market. This concept will be discussed through three steps: the good cryptocurrency and how the financial market has evolved into the digital technology and secondly now it could emerge as a sustainable development in the field of finance. Hence the technology encourages the development of investor and green cryptocurrency. There are various individual issues, related to bad cryptocurrency like money laundering, scam, crypto whale and viruses. Finally, the ugly cryptocurrency in the financial sector affects the environmental condition and how it could affect the ecosystem of the planet due to crypto mining. This study suggested the token offering organization to adopt the proof-of-authority process than the proof-of-work, to avoid carbon emission and more volume of energy consumption. This study also suggests the investor to invest on the green cryptocurrency than the hazardous cryptocurrency (bitcoin), which affects the environmental condition.
Mixture of Decision Trees for Interpretable Machine Learning

This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a single decision tree, but which can alternatively be divided into subproblems. Each subproblem can then be learned well from a single decision tree. Therefore, MoDT can be considered as a method that improves performance while maintaining interpretability by making each of its decisions understandable and traceable to humans.Our work is accompanied by a Python implementation, which uses an interpretable gating function, a fast learning algorithm, and a direct interface to fine-tuned interpretable visualization methods. The experiments confirm that the implementation works and, more importantly, show the superiority of our approach compared to single decision trees and random forests of similar complexity.
Hyperparameter Tuning in Offline Reinforcement Learning

In this work, we propose a reliable hyperparameter tuning scheme for offline reinforcement learning. We demonstrate our proposed scheme using the simplest antmaze environment from the standard benchmark offline dataset, D4RL. The usual approach for policy evaluation in offline reinforcement learning involves online evaluation, i.e., cherry-picking best performance on the test environment. To mitigate this cherry-picking, we propose an ad-hoc online evaluation metric, which we name "median-median-return". This metric enables more reliable reporting of results because it represents the expected performance of the learned policy by taking the median online evaluation performance across both epochs and training runs. To demonstrate our scheme, we employ the recently state-of-the-art algorithm, IQL, and perform a thorough hyperparameter search based on our proposed metric. The tuned architectures enjoy notably stronger cherry-picked performance, and the best models are able to surpass the reported state-of-the-art performance on average.
Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders

To ensure the stability and reliability of service quality, large Internet companies need to closely monitor various KPIs (Key Performance Indicators, such as network throughput, CPU usages) and trigger timely troubleshooting or mitigation when any anomaly occurs. However, the diversity and complexity of anomalies bring great challenges to this work, especially when there is no manual label and low delay is required. In this paper, we propose HS-VAE (Highly Sensitive Variational Auto-Encoders), an unsupervised, robust algorithm based on CVAE (Conditional Variational Auto-Encoders) with high sensitivity to anomalies for KPIs, which contains mainly 3 parts: a simple but important data filter before training, improved conditional VAE with two dropout layers and adjusted anomaly detection method based on reconstruction probability. Our experiments using real-world data show that, HS-VAE's best F1-score ranges from 0.91 to 0.98. In addition, HS- VAE is excellent in sensitivity to anomaly and works well even with low latency requirements.
Data-Efficient Automatic Model Selection in Unsupervised Anomaly Detection

Anomaly Detection is a widely used technique in machine learning that identifies context-specific outliers. Most real-world anomaly detection applications are unsupervised, owing to the bottleneck of obtaining labeled data for a given context. In this paper, we solve two important problems pertaining to unsupervised anomaly detection. First, we identify only the most informative subsets of data points and obtain ground truths from the domain expert (oracle); second, we perform efficient model selection using a Bayesian Inference framework and recommend the top-k models to be fine-tuned prior to deployment. To this end, we exploit multiple existing and novel acquisition functions, and successfully demonstrate the effectiveness of the proposed framework using a weighted Ranking Score (η) to accurately rank the top-k models. Our empirical results show a significant reduction in data points acquired (with at least 60% reduction) while not compromising on the efficiency of the top-k models chosen, with both uniform and non-uniform priors over models.