Reviewerly/exHarmony huggingface dataset:
Elemental Proxies for Paleosalinity Analysis

Salinity is a fundamental property of watermasses that is useful in paleoenvironmental and paleoecological studies, yet the theory of application of geochemical proxies to paleosalinity reconstruction is underdeveloped.Here, we explore the use of three elemental ratios for paleosalinity reconstruction: boron/gallium (B/Ga), strontium/barium (Sr/Ba), and sulfur/total organic carbon (S/TOC) ratios.We compiled a large set of modern aqueous and sedimentary chemical data representing a range of salinity facies (i.e., freshwater, brackish, marine) in order to test the relationships of these proxies to ambient watermass salinity and to determine their viability for paleosalinity analysis.Sediment data were limited to fine-grained siliciclastic units (muds/shales/mudstones) without significant carbonate content, in which the elements of interest were mainly acquired through adsorption of dissolved species, forging a connection between elemental proxy values and watermass chemistry.In modern systems, watermass salinity is correlated with these proxies, yielding r of +0.99 and +0.76 for aqueous and sediment B/Ga, +0.66 and +0.54 for aqueous and sediment Sr/Ba, and +0.98 for aqueous sulfate and +0.66 for sediment S/TOC (all significant at p(a) < 0.01).These relationships establish the basis for use of these elemental ratios as paleosalinity proxies.Elemental crossplots permitted estimation of approximate salinity thresholds for each proxy: (1) B/Ga is < 3 in freshwater, 3-6 in brackish, and > 6 in marine facies; (2) Sr/Ba is < 0.2 in freshwater, 0.2-0.5 in brackish, and > 0.5 in marine facies; and (3) S/TOC is < 0.1 in freshwater and > 0.1 in brackish and marine facies.S/TOC did not discriminate effectively between brackish and marine facies, probably because microbial sulfate reduction (MSR) is generally Corg-limited rather than sulfate-limited in both facies.The accuracies of these thresholds for prediction of the salinity facies of sediments are ~88 % for B/Ga, ~66 % for Sr/Ba, and ~91 % for S/TOC.Although the Sr/Ba proxy is slightly less robust owing to difficulty in removing all carbonate Sr influence and/or to greater mobility of Sr and Ba in the burial environment, we strongly advocate use of multiple proxies in order to support paleosalinity interpretations.The application of these proxies are also illustrated in two ancient studies, to test the validation of these proxies.
Face Detection with Machine Learning and Open CV Classifier

In the last few years, face recognitions owned considerable consideration and liked together of the foremost used functions within the area of image evaluation and recognition. Face detection reflects on consideration of an incredible section of face attention operations. The technique of face detection in pixels is elaborate with many features’ variabilities provided throughout human faces. Faces include pose, expression, smile, role and orientation, pores and complexion, the presence of glasses or facial hair, variations in digicam gain, lighting conditions, and photo resolutions. Haar Cascade classifier is of outstanding assist when performing this undertaking smoothly. Face detection goes to possess a dramatic impression on the face detection field, as a result, familiarizing yourself with its functions like attendance recording system with the help of camera, Mask detection system. In this paper, we proposed a face detection system for the utilization of computer learning, especially OpenCV. The mandatory step required is face detection which we did with the usage of a broadly used step referred to as the haarcascade_frontalface_default classifier, python and its module.
A Novel System for Recognizing Recording Devices from Recorded Speech Signals

The field of digital audio forensics aims to detect threats and fraud in audio signals. Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech, recognize speakers, and recognize recording devices. User-generated audio recordings from mobile phones are very helpful in a number of forensic applications. This article proposed a novel method for recognizing recording devices based on recorded audio signals. First, a database of the features of various recording devices was constructed using 32 recording devices (20 mobile phones of different brands and 12 kinds of recording pens) in various environments. Second, the audio features of each recording device, such as the Mel-frequency cepstral coefficients (MFCC), were extracted from the audio signals and used as model inputs. Finally, support vector machines (SVM) with fractional Gaussian kernel were used to recognize the recording devices from their audio features. Experiments demonstrated that the proposed method had a 93.4% accuracy in recognizing recording devices.
Intelligent Transportation System by Controlling Traffic using Video Processing in Mat Lab

Laptop vision techniques square measure used for analysis of traffic police investigation videos that is gaining a lot of importance. This analysis of videos is helpful for public safety and for traffic management. In recent time, there has been Associate in nursing exaggerated scope for analysis of traffic activity mechanically. Laptop based mostly police investigation algorithms and systems square measure won’t to extract info from the videos that is additionally known as as Video analytics. The method of distinguishing instances of planet objects is understood as object detection. It detects the quantity of vehicles on every road and betting on the vehicles load on every road, this technique assigns optimized quantity of waiting time (red signal light) and period (green signal light). This technique could be a totally machine-driven system that may replace the traditional pre-determined fixed-time based mostly traffic system with a dynamically managed traffic system.
Peningkatan Kompetensi Guru dalam Penulisan Artikel dan Publikasi pada Open Journal System (OJS)

Through community service program we do the training, workshop and assistance for economics teachers as the member of MGMP (Musyawarah Guru Mata Pelajaran) in Bukittinggi City, West Sumatera. The aims of this program are (1) to improve the ability of teachers in the writing of scientific articles, (2) the teachers are able to publish their articles in online journals (3) to motivate the teachers who do not yet have the article to conduct the classroom action research. The participants of this training are 26 economics teachers from high school in Bukittinggi. This activity get positive response from the participants but only two participant can finish the article during the training. Therefore, continues improvement is still need to improve teacher abiliy ini writing scientific article. Keyword: teacher competency, article, publication.
Adopting Blockchain Technologies in Cloud for Efficient Data Storage and Enhanced Security

In recent years, an innovative and potential technology called the blockchain has entered the developers community. The cloud storage has enabled the ground breaking development with several constraints associated with storage, security and cost. The block chain technology overcomes the cloud's most threatening distress security issues. The important factor is that cloud replaced the legacy enterprise solutions. Similarly, the decentralized ledgers will substitute the centralized cloud. This technology has solved the query raised by the majority of the enterprise executives by simply providing a better way to prevent forgery of the data with enhanced security.
Document Clustering based on Phrase and Single Term Similarity using Neo4j

Document similarity generally rely on single term similarity such as cosine similarity. To achieve better document similarity, along with single term phrase- more informative feature can be used. To find out shared phrases across the corpus the Document Index graph (DIG) representation model is used. Document representation - DIG model incrementally construct the graph and simultaneously finds the shared phrase between current document and previously inserted documents from the graph. The similarity between documents is mainly depends on the number of shared phrases and single term similarity – known as hybrid similarity. The hybrid similarities are used with wellknown density based clustering technique DBSCAN to assess their effect on quality of the clusters. Experimental results shows that hybrid similarity gives more accurate degree of document similarity and performs better cohesive clustering.
A personalised recommendation algorithm based on probabilistic neural networks

Collaborative filtering is widely used in recommendation system. Our work is motivated by the observation that users caught in their attention relationship network, and their opinions about items will be directly or indirectly affected by others through such a network. Based on behaviours of users with similar interest, the technique focuses on the use of their opinions to recommend items. Therefore, the quality of similarity measure between users or items has a great impact on the accuracy of recommendation. This paper proposes a new recommendation algorithm with graph-based model. The similarity between two users (or two items) is computed from the connections on graph with nodes of users and items. The computed similarity measure is based on probabilistic neural networks to generate predictions. The model is evaluated on a recommendation task which suggests that which videos users should watch based on what they watched in the past. Our experimental results on the YouKu and Epinions datasets demonstrate the effectiveness of the presented approach in comparison with both collaborative filtering with traditional similarity measures and simplex graph-based methods and further improve user satisfaction, our approach can better improve the overall recommendation performance in precision, recall and coverage.
The Role Of Debates And Awareness CampsIn Teacher Education Pedagogy Helps Teachers The Better Communicators IN Teaching Learning Process-

India after independence made innumerable efforts in all the parameters that helped to grow as one nation. The one such parameter was the concept of Equality-this is mentioned in the constitution too. Invested in primary, secondary, and Higher Education. India has diversity in caste, religion, gender, locations language socioeconomic status, creed, etc. Like western countries it also believed in education as an agent in achieving Equality; Diversity and Inclusivity, subsequently it appointed education commissions to achieve such goals. University Education Commission said each person should have the educational opportunity of the kind and to the extent that is suited to his capacity and interest and which represents his fair share of the total educational resources” All the commissions recommended for achieving equality, diversity, and inclusivity. Equality in sports, opportunity, Education, women, etc is possible through a Diversified curriculum, pedagogy, and evaluation techniques. the present topic “ The Role of Debates and Awareness camps to study Equality, Diversity, and Inclusivity –In Teacher Education Pedagogy, curriculum and Evaluation ‘’ is a novel one. It is a theoretical paper –the information is gathered through secondary sources.
Enhancing Reusability and Measuring Performance Merits of Software Component using Data Mining

In software industry, it is necessary to reduce time and efforts in software development. Software reusability is an important measure to improve development and quality of software. Enhancing reusability will reduce delivery time of software products, decreases the development labor, software defects and cost of the development process. Software reuse is the best solution factor to acquire the existing knowledge from software warehouse. Measuring the reusability level of the software is essential to achieve the goals of reuse. Data mining is the process of extracting useful patterns and analyzing data sets from large data sets. The reusability of a software component chooses the right measurement and enhances the preventability of an application for reuse. The software metrics are used as quantitative measures to establish and evaluate the components. In this paper, measuring the software reusability using some classification algorithms on a specific software reuse data set is discussed. The system is implemented using R data mining tool and performance of the automation system is developed for reusability prediction like precision, recall, f-measure. The experimental result shows that the model can be effectively used for efficient, accurate, quicker and economic identification of reusable components from the existing software resources. This paper seeks to provide comparative analysis of H-SOM and Naïve Bayes algorithm classifiers of Dengue datasets
A Survey on Evolutionary Computation Approaches to Feature Selection

Feature selection is an important task in data miningand machine learning to reduce the dimensionality of the dataand increase the performance of an algorithm, such as a clas-sification algorithm. However, feature selection is a challengingtask due mainly to the large search space. A variety of methodshave been applied to solve feature selection problems, whereevolutionary computation techniques have recently gained muchattention and shown some success. However, there are no compre-hensive guidelines on the strengths and weaknesses of alternativeapproaches. This leads to a disjointed and fragmented fieldwith ultimately lost opportunities for improving performanceand successful applications. This paper presents a comprehensivesurvey of the state-of-the-art work on evolutionary computationfor feature selection, which identifies the contributions of thesedifferent algorithms. In addition, current issues and challengesare also discussed to identify promising areas for future research. Index Terms—Evolutionary computation, feature selection,classification, data mining, machine learning. © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Breast Cancer Detection using Gradient Boost Ensemble Decision Tree Classifier

Detection of any abnormalities in the human is a big challenge faced by many of the field experts. One such challenge is to detect the Breast Cancer. The prime mottobehind in making this paper is to detect the breast cancer with the help of breast images in an advanced and appropriate way. In this study, an attempt is made in such a way by applying the combination of various existing technics in the extracted breast images for getting better result in detecting the Breast Cancer. Consequently,feature extracting images are appliedusing Light gradient boosting ensemble decision tree classifier for identifying benign and malign features of an image. As a result, the normal and abnormal breast cancer image is detected by combining above applications. Besides, classification accuracy and minimize classification time metrics are also achieved more appropriately than the existing detectingtechnics.
Design and Development of a Sign Language Gesture Recognition using Open CV

There are different kinds of people in this world who have different types of disabilities.Being not able to speak is considered as serious disability.For helping those people, we create an application that can solve the problem of having normal conversation with others.We developed a sign language application for the easy communication between deaf and dumb people and normal people.The purpose of our paper is to detect gestures through the webcam and display what the gesture is.Here, we have considered different signs from American Sign Language system corresponding to 26 alphabet letters.In order to make this work, a gesture is shown to the computer's webcam resulting in generation of corresponding letter from the images that were defined in dataset.There were actually some of the models, making this Paper success.The first one is CNN, that is used to differentiate gestures and the second is ROI, used to remove background images that are not necessary and also to indentify the hands having different skin tones.
Complexity Analysis and Accuracy of Image Recovery Based on Signal Transformation Algorithms

In this paper we compare and analyze the complexity of three functions: Fast Fourier transform (FFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), used in image transformation. The purpose of all the algorithms is to shift the signal from space or time domain to frequency domain for de-noising or compression. We compare the simulated process time of both one and two dimensional FFT, DCT and DWT (Symlet and Debauches 1) using image and speech signal. The process time is found lowest for FFT and highest for DWT, provided its basis function governs the process time and DCT provide the moderate result. Finally the quality of compressed image under the three mathematical functions are compared, where DWT is found as the best and FFT yields worst result.
Evolving visual artefacts based on consumption patterns

In information visualisation, the visual artefacts should have a functional dimension, allowing the analysis of information, and an aesthetic dimension, to seize the users' attention to the information being displayed. However, in the data aesthetics field, the main concern is to produce aesthetically appealing artefacts. With this project, our goal is to try to join these two fields by exploring the aesthetic dimension of a functional visualisation model characterised by a series of parameters that can make the visualisation more functional and/or more aesthetically appealing. In concrete, we propose a framework based on interactive evolutionary computation (IEC) to evolve the parameters of the visualisation model, enabling the user to explore possibilities and to create different aesthetics over the same data. Our case study will be based on a dataset containing the consumption patterns within a Portuguese retail company. Through different validation methods - automatic fitness function, usage scenarios, and a user study - we show that our system is able to create a wide and diverse set of emergent visual artefacts that can be intriguing and aesthetically appealing for the user.
Artefact-based approach for improving social presence in e-learning

Requirements elicitation is an initial process in software development. This process is used to gather an accurate set of requirements in order to produce unambiguous system requirements for system developers. In developing e-learning, the element of social presence can help the students to socially interact and feel connected virtually. Thus, there is a need to improve requirements elicitation process by identifying social presence factors in e-learning. The objective of this paper is to improve requirements elicitation process through artefact-based approach in e-learning domain. Usability evaluation for the artefacts are carried out in study one, study two and study three by using multiphase method design. The artefact-based approach results prove that social presence requirements can be elicited and thus contribute to the knowledge of requirements engineering. The evaluation results also prove that artefact-based approach for social presence requirements is acceptable as the process for motivating social interaction in the e-learning domain.
Diffeomorphic Matching

This chapter discusses variational registration methods, minimizing registration costs with a guarantee that the optimal solution is a diffeomorphism. The main focus will be on methods optimizing over flows associated with differential equations, using concepts introduced in the previous chapters.
Priority Based Mechanism for Resource Sharing in Cloud

Cloud computing technologies are getting matured day by day. Revolutions in underlying software, virtualization and hardware technologies related to storage, processing and computing technologies has helped cloud computing service providers to win trust of concerned stake holders. However, the exponentially increasing demand of cloud based resources has made task of resource management and utilization more and more challenging. A novel load balancing technique in cloud computing environment is presented in this paper. The virtual machines are implemented on an open source cloud computing platform on open source operating system. A virtual machines’ priorities based load balancing approach presented here indicates improvement in overall waiting time for load balancing. The mechanism prioritizes load balancing on same priority level virtual machines or lower priority level virtual machines.
What Determines Social Distancing? Evidence from Advanced and Emerging Market Economies

The health and economic consequences of COVID-19 are closely tied to individual compliance with recommended protective behaviors.We examine the determinants of this compliance using survey data from the COVID Behavior Tracker for 29 advanced and emerging market economies between March and December 2020.Social distancing behaviors vary significantly by age, gender, occupation, and individual beliefs about COVID-19.In addition, those who trust their government's response to COVID-19 are significantly more likely to adopt recommended behaviors and to self-isolate if advised, highlighting the need for well-coordinated actions on the health and economic fronts.We also find that mobility restrictions, such as stay-at-home orders, and mask mandates are associated with reduced social interactions and persistent increases in compliance.Together, these drivers account for over two-thirds of the regional differences in compliance, confirming their important role in increasing social distancing and containing the pandemic.
Spider monkey optimisation: state of the art and advances

Algorithm simulated by the social behaviour of understandable agents has become prominent amid the researchers in modern years. Researchers have advanced profuse algorithms by replicating the swarming behaviour of different creatures. Spider monkey optimisation (SMO) algorithm is a novel swarm intelligence based optimization which is a replica of spider monkey's foraging behaviour. Spider monkeys have been classified as animals with fusion-fission social structure, where they pursued to split themselves from huge to lesser hordes and vice-versa depends upon the accessibility of food. SMO and its variants have successful in dealing with difficult authentic world optimization problems due to its elevated effectiveness. This paper depicts a useful analysis of SMO, its variants, applications, advancements, usage levels and performance issues in various popular yet trending domains with a deep perspective. The key motto behind this analytical point of view is to inspire the practitioners and researchers to innovate new solutions.
Entropy Coding

In this chapter, we discuss the two most important entropy encodings. The historically first one is the so-called Shannon-Fano coding. In 1952, however, David Huffman developed the so-called Huffman coding, which can be shown to be the best possible entropy coding. Therefore, it is used in almost all applications today. From the frequency distribution of the characters in the file to be compressed, both Shannon-Fano and Huffman build up a tree structure from whose leaves the associated bit pattern can be read for each character.