Reviewerly/exHarmony huggingface dataset:
Geochemical mapping by unmixing alluvial sediments: An example from northern Australia

Alluvial sediments have long been used in geochemical surveys as their compositions are assumed to be representative of areas upstream. Overbank and floodplain sediments, in particular, are increasingly used for regional to continental-scale geochemical mapping. However, during downstream transport, sediments from heterogeneous source regions are carried away from their source regions and mixed. Consequently, using alluvial sedimentary geochemical data to generate continuous geochemical maps remains challenging. In this study we demonstrate a technique that numerically unmixes alluvial sediments to make a geochemical map of their upstream catchments. The unmixing approach uses a model that predicts the concentration of elements in downstream sediments, given a map of the drainage network and element concentrations in the source region. To unmix sedimentary chemistry, we seek the upstream geochemical map that, when mixed downstream, best fits geochemical observations downstream. To prevent overfitting we penalise the roughness of the geochemical model. To demonstrate our approach we apply it to alluvial samples gathered as part of the Northern Australia Geochemical Survey. This survey gathered samples collected over a ∼500,000 km2 area in northern Australia. We first validate our approach for this sample distribution with synthetic tests, which indicate that we can resolve geochemical variability at scales greater than 0.5–1° in size. We proceed to invert real geochemical data from the total digestion of fine-grained fraction of alluvial sediments. The resulting geochemical maps for two elements of potential economic interest, Cu and Nd, are evaluated in detail. We find that in both cases, our predicted downstream concentrations match well against a held-out, previously ‘unseen’ subset of the data, as well as against data from an independent geochemical survey. By performing principal component analysis on maps generated for all 46 available elements we produce a synthesis map to identify the significant geochemical domains in this part of northern Australia. This map shows strong spatial similarities to the underlying lithological map of the area. Finally, we compare the results from our approach to geochemical maps produced by interpolating alluvial geochemical data using two standard methods: kriging and inverse distance weighting. We find that, unlike the method presented here, both these methods can generate maps that are dampened relative to expected magnitude, as well as being spatially distorted. We argue that the unmixing approach is the most appropriate method for generating geochemical maps from regional-scale alluvial surveys.
History information-based Hill-Valley technique for multimodal optimization problems

The key of multimodal optimization algorithms is to divide the population into multiple species, and each species searches for a global optimum. This article proposes a new differential evolution based on the Hill-Valley technique for multimodal optimization. This technique uses history information to classify individuals on the same peak as one species. Compared to other niching techniques, this technique is insensitive to parameters and does not waste additional computing resources. For each species, the evolutionary state recognition method is developed to judge the evolutionary state and the search strategy is designed according to the evolutionary state. In addition, the prediction mechanism is designed to save computing resources. The experimental results show that the performance of the proposed algorithm is better than other algorithms on the CEC2013 test set and the NESs test set.
Analysis of cyber threats as a risk factor in the banking sector

The banking industry faces increased risks based on cyber threats, mostly through mobile applications and web portals, and to a lesser extent through other communication channels. Cyberattacks continue to increase, and the banking sector can be particularly vulnerable, especially in the context of the Covid-19 pandemic. The subject of the paper is to analyze the views of respondents of users of banking services in the Republic of Serbia on the problem of cyber threats as a risk factor in the banking sector. The main conclusion is that the largest number of respondents, as much as 70% of them, are not concerned or not very concerned about security in the banking services' sector. This indicates the existence of a high degree of trust in the security of banking services and the activities that banks undertake to protect data and deposited financial assets. In addition, banks should still emphasize preventive activities to reduce the risks based on cyber threats to an acceptably low level. Some of the preventive activities can be engaging in the education of internal auditors and increasing the effectiveness of information security audits.
Block Chain Integration with AI for Agriculture Product Supply Chain

Customers spend for agro - based items made by farmers in agro - based supply chain operations (ASCs). People stress the significance of agri-food quality throughout this operation, while producers anticipate higher revenues. Effective tracing and governance for agri-food commodities encounter enormous hurdles as a result of the size and dynamism of ASCs. Nevertheless, the majority of the currently available solutions are unable to adequately address the accountability and administration needs of ASCs. First, in order to enable product tracing and provide organizational unit for the agri-food tracking data in ASCs, we develop a blockchain-based ASC architecture. The manufacturing and preservation of agri-food goods are then effectively decided in order to maximize profit using a Learning Based training-based Supply Chain Administration technology. To show the efficiency of the suggested cryptocurrency system and the DR-SCM approach in various ASC contexts, detailed simulation tests are conducted. The findings indicate that the proposed ledger ASC architecture provides a strong assurance of trustworthy product traceability. Moreover, compared to intuitive and Q-learning approaches, the DR-SCM may provide larger product profitability.
Classification of Cyber Hate Speech from Social Networks using Machine Learning

Social networking sites like Twitter, Facebook, and others play a big part in social network analysis in the present Web 2.0 environment. Hate speech, which is abusive communication that singles out certain group traits like gender, religion, or race in order to incite violence, is growing in importance as social media platforms gain popularity. Hate speech on the internet is a relatively new problem in our society that is steadily growing by exploiting the flaws in the platforms that distinguish most social networking sites. The main source of this occurrence is offensive remarks, whether delivered during user contact or in the form of an uploaded multimedia context. Hateful and toxic content created by a portion of social media users is a growing phenomenon that has prompted researchers to devote significant resources to the difficult task of identifying hateful content. Some of the popular methods are the Support Vector Machine, the Logistic Regression Model, and Decision Trees. These strategies, however, frequently come under the umbrella of discriminative learning, which seeks to distinguish one class from others while taking into consideration the real world. First, this study has reviewed social networks and social network analysis. Second, the necessity of detecting hate speech and the distinction between it and abusive content are covered. Thirdly, several machine learning algorithms are being contrasted. The experimental findings demonstrate that the suggested strategy consistently outperforms the alternatives.
A Comparative Study of Implementing Agile Methodology and Scrum Framework for Software Development

Agile is a software development process method that is quickly becoming the industry's de facto standard. Every organisation seeks agility because it wants quick delivery and the ability to respond to rapidly changing client needs. Agile is a project management methodology that has aided many businesses in responding swiftly to changing market conditions. While agile methodology may be used to almost every situation, it was developed in India by a software development business. Customer happiness and overall productivity can both benefit from agile methods. Agile and scrum can be used separately, but their combined benefits make the methodology popular.
Securing Data in Cloud: Major Threats and Recent Strategies

Innovation permeates deep into every part of our modern life and, in doing so, easily enables attackers to readily penetrate and attack a targeted enterprise at the least conceivable expense. Regardless, it's challenging for any company to notice it swiftly, even after being prepared for any unexpected occurrence. Furthermore, while Cloud computing lets its users maintain their data throughout the grid in an online environment, that data can be accessed easily from anywhere worldwide. However, as security becomes a crucial rising concern, most people, mainly those oblivious to the increasing number of attacks and technical breakthroughs, are concerned about storing their data on the cloud. This paper discusses how to solve the aforementioned issues while providing an ingenious approach to numerous security risks in the cloud computing space.
Infographics based Teaching Learning Process for Enriching Education System

Infographics have long been an effective and engaging technique for conveying information. Even before the development of lexical languages, pre-era humans used graphical representation as a means of communication. Infographics have long been a fundamental component of efficient communication and learning. Experimenting with the effectiveness of learning using Infographics was the goal of this work. Infographics not only help students study more successfully, but they also assist teachers and professors in a variety of ways. The purpose of this study is to present an analysis of using infographics in the classroom as teaching learning aids by looking at where and how they are used as a visual communication tool and as a learning tool for students.
Python Code Smell Detection Using Machine Learning

Python is an increasingly popular programming language used in various software projects and domains. Code smells in Python significantly influences the maintainability, understandability, testability issues. This paper proposes a machine learning-based code smell detection for Python programs. We trained eight machine learning models with a dataset based on 115 open-source Python projects, 39 class-level software metrics, and 22 function-level software metrics. We intended to identify five code smell types in both class and function levels, i.e., long method, long parameter list, large class long scope chaining, and long based class list. Correlation-based feature selection (CFS) and logistic regression-forward stepwise (conditional) selection were employed to improve the performance of the model. This research concluded with an empirical evaluation of the performance of the machine learning approaches against the tuning machine method. The results show that the machine learning method achieved 99.72% accuracy when identifying long method and long base class list. The machine learning-based code smell detection also outperformed the tuning machine method. Moreover, we also found a set of high-impact features that contributed most when identifying each type of code smell.
Arduino Based Secure Electronic Voting System with IoT and PubNub for Universities

The Internet of Things is playing a significant role in the present era of technology to develop methods of communication in day-to-day life. By establishing seamless communication with the digital world, it has allowed us to communicate, analyse, and organize our daily operations in an effective manner. Voting is significant and intricate procedure that requires transparency. Manual voting is always laborious and transparent danger is larger. The challenge of conducting fair elections in universities is always highly challenging due to election rigging among many student organizations. Therefore, a secure electronic voting system based on the Arduino microcontroller and pubNub is suggested in this research work. The students can generously and without any restrictions cast their votes in online to this sophisticated, contemporary, and reliable technology. The fundamental aim of this article is to use the internet of things to automate the voting process, which will increase the accuracy, speed, and overall security of the elections. Students' fingerprints are typically taken by institutions as part of the admissions procedure. The voting process will be verified using these fingerprints. To verify the veracity of the suggested system, a test case prototype has been put into practice during experimentation. Utilizing online views and graphs, real-time findings are demonstrated.
A Framework to Detect Crime through Twitter Data in Cyberbullying with EFFDT Model

Due to enormous availability of internet, social networking and micro blogging websites such as twitter, instagram, are increased. The users registered with the website utilize the webpage as a platform to express their thoughts as comments and convey their opinions to the global users. Cyber bulling has become one of the serious issues in recent days because of controversial comments and thoughts exposed in the micro blogging websites that impacts the society and certain group of peoples in a negative way. The amount of negatively impacted comments on micro blogs are getting increased in recent days, it is required to identify those traits as a crime impacted feeds for police attention. Texting is a technique used to make a classification of large weights into clusters of related data. The proposed framework is focused on collecting various tweets from the micro blogs, further pre-processing it using natural language processing (NLP) for the features selection, is implemented using partial spam Optimization (PSO). Based on the feature extraction process, the classification model is developed using ensemble approach. The proposed approach considers Ensemble feed forward decision tree (EFFDT) model to classify different types of negative tweets from the given database. The machine learning algorithm namely Support vector Machine (SVM) algorithm and K-Nearest Neighbour (KNN) algorithm are used for comparison with the proposed method. The performance result of these algorithms are compared in terms of precision, F1Score, accuracy and further compared to the state of art approaches.
Detection of Credit Card Fraud using Machine Learning Algorithms

The use of online transactions in regular life has increased over the past ten years due to continuous development in technology and network connectivity. Online transactions are easy, simple, and user-friendly is the main reason for new users to constantly join the vast population benefitting them from the system. The main demerit of the online transaction system is a misuse of the system is defined as theft or one's credit card information which is used for self-profit without the permission of the cardholder. In this paper we have uses different ML algorithms such as regression, random forest classifier, and decision tree to control the heavily imbalanced dataset. Finally, in this research, we will calculate the accuracy, precision, recall, f1 score, and confusion matrix.
Cloud Energy-Efficient Load Balancing: A Green Cloud Survey

Green Cloud Computing is a scalable use of computing resources that uses a virtualized computing platform. Cloud services enable people and companies to use software and equipment that is remotely managed by other parties. Meanwhile Organizations are attempting to cut down on the electricity used by idle resources as cloud computing platforms are used more frequently. By properly dividing the load and decreasing resource consumption, load balancing can assist in lowering energy consumption. Utilizing an effective load balancing algorithm, the service provider's ultimate purpose is to utilize the computer resources as effectively as possible and to maximize revenues. Discussions on cloud computing don't necessarily center on resource consumption and energy efficiency. With effective load balancing, resource usage may be reduced, which not only lowers costs but also makes businesses more environmentally friendly. Load balancing also makes it possible for cloud computing to scale, which is one of its key characteristics. Therefore, increasing resource efficiency and a distributed system's performance in this way will lower energy usage but also carbon emissions to obtain “green computing.” Different energy-conscious load balancing methods are reviewed, and its relevant benefits, drawbacks, and performance parameters are shown.
TRANSLATION ERROR IN GOOGLE TRANSLATE FROM ENGLISH INTO ROMANIAN IN TEXTS RELATED TO CORONAVIRUS

Both the emergence of the pandemic and lack of knowledge and/or time needed to translate texts related to this topic brought about an increased interest in analyzing Neural Machine Translation (NMT) performance. This study aims to identify and analyze lexical and semantic errors of language aspects that appear in medical texts translated by Google Translate from English into Romanian. The data used for investigation comprises official prospects of 5 vaccines that were approved to be used against the current coronavirus. The focus is on the lexical and semantic errors, as researchers state that these errors made by Machine Translation have the highest frequency compared to morphological or syntactic errors. Moreover, the lexical errors may affect the meaning, the message, and may easily lead to mistranslation, misunderstanding and, therefore, misinformation. The texts to be analyzed are collected from official websites and translated using Google Translate and Google Languages Tools. From the data analyzed, there are 22 lexical and semantic errors that are approached through descriptive methodology. By examining types of errors in translation from English into Romanian and analyzing the potential causes of errors, the results will be used to illustrate the quality and accuracy of Google Translate when translating public health information from English into Romanian, to observe how much the message is affected by the error, in order to sharpen up linguistic awareness. The results of the study can ultimately help improve of the quality of NMT in terms of better lexical selection and attempt to give inputs as a contribution for a more adequate translation into Romanian by Google Machine Translation.
A Novel Lightweight Traffic Sign Recognition Model Based on YOLOv5

How to strike a balance between detection speed and recognition accuracy has become a major challenge in real-time object detection. In this research, the YOLOv5 (You Only Look Once version 5) model was lightweight and optimized to improve the detection speed and accuracy of the network. To prune the backbone and neck are to simplify the network structure and reduce the parameters. The lightweight structure of the C3 module was designed and incorporated into the attention mechanism to improve the feature extraction capability of the network. For the public traffic sign dataset, the label assignment strategy and the loss function of YOLOv5 were refined to alleviate the imbalance between positive and negative samples and to better compute the loss, resulting in more stable and efficient training. Compared with other mainstream single-stage models, it achieves a better trade-off between speed and accuracy. With only 0.85 M parameters, 91.9% of mAP (mean average precision) and 360 FPS (frames per second) were achieved, which were 16.26% mAP and 26.67 FPS higher than the conventional YOLOv5n, respectively. The performance of our lightweight model in traffic sign detection far exceeds the most advanced achievements.
Modelling the impact of safety threats on professional journalistic reporting of protests by Indigenous People of Biafra among Nigerian journalists

This study aimed to determine the impact of safety challenges on professional journalistic reporting of IPOB protests in Nigeria. A total of 400 Nigerian journalists who were covering IPOB activities were surveyed. Data were analysed using multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA) and correlational analysis. The result of the study showed that journalists recovering IPOB activities in South East Nigeria faced safety challenges both online (verbal abuse, threat of attack, personal messages of harassment and reputational threats) and offline (physical attacks, harassment, damage to working devices) safety challenges. Additional results showed that women faced more safety challenges online than men, but men faced more offline safety challenges than women. Put together, women faced both online and offline safety challenges more than men. Also, journalists in private media outfits faced offline challenges than their counterparts in public media stations who faced more online safety challenges. Put together, journalists in private media stations faced more safety challenges than those in public media outfits. Safety challenges impact professional journalistic reporting in the area of routines, orientation and autonomy. Training was found to be an efficient way of assisting journalists to better handle risky assignments.
Precise Nondestructive Parity Measurement of Artificial Atoms Using a Superconducting Resonator and Homodyne Measurement

Abstract In this paper, a robust and accurate protocol is proposed to realize nondestructive parity measurement of artificial atoms using a superconducting resonator and homodyne measurement. With the help of an additional microwave driving field, the cavity field evolves into a coherent state or remains in a vacuum state according to the parity of the atoms. Consequently, the parity information of atoms can be read out by a homodyne measurement on the cavity. Parity information can be obtained without destroying the original state of the system, which means that the state can be further applied to other quantum information processing tasks after a parity measurement. The numerical simulation results show that the protocol is robust against systematic error, random noise, and decoherence. Therefore, this protocol can provide a feasible viewpoint for nondestructive parity measurement.