Reviewerly/exHarmony huggingface dataset:
Implication of geological domains data for modeling and estimating resources from Nkout iron deposit (South-Cameroun)

This paper is devoted to determining whether the addition of geological information can improve the resource estimate of mineral resources. The geochemical data used come from 116 drill holes in the Nkout East iron deposit in southern Cameroon. These geochemical data are modeled on Surpac and Isatis softwares to represent the 3D geochemical distribution of iron in the deposit. Statistical analysis and then a variographic study is performed to study the spatial variability of iron. Estimation domains were defined based on the results of geological and geochemical analyses. Four domains were determined. These domains are the saprolitic domain in particular; the poor domain or fresh rocks such as amphibolites, granites, and gneisses; the rich domain or oxidized rocks (BIF) and the metasediment domain. Block modeling of the deposit is performed to estimate the resource. The grade of each block was estimated by using ordinary kriging and composites from each domain. This study also consisted of comparing two types of estimate, notably the domain estimate and the global estimate. The cross-validation made it possible to authenticate the obtained models. From this comparison, the domain estimation brings more precision the global estimate precisely on the error analysis while if we take into account the point clouds of the predicted and estimated values, the estimation by geochemical modeling provides the best results.
A Hybrid Image Steganography Method

This is age of fast communication and data revolution. Huge amount of data is transmitted and received in every second utilizing the current infrastructure. Providing the security and maintaining integrity of the data while it is being transferred through the public channels is a challenging task. Steganography can be used in this purpose by concealing the data inside text, image, audio or video file formats and send the files to the other end. This paper gives an overview of various image steganography methods. To hide secret information in images various steganographic methods are available which have their own pros and cons. In this paper we have discussed the types of image steganography. Here we propose two different image steganography methods using Modified Least Significant Bits (LSB) and Discrete Wavelet Transform (DWT) methods. Also, the results are analyzed using different parameters like Peak signal to noise ratio(PSNR), Mean Square Error(MSE) to find out the efficiency of the proposed methods.
The Role of Topics in Licensing Anaphoric Relations in VP-ellipsis

This paper addresses the role of topics in the licensing of anaphoric relations. I demonstrate that the C-domain plays a crucial role in accounting for valid and invalid cases of coreference. In particular, I argue that discourse anaphors are bound by a context operator, implying that the mechanism that corresponds to coreference is syntactically encoded. The crucial empirical evidence comes from the licensing of strict and sloppy readings in VP-ellipsis that follow from an alternative analysis of the role of focus binding proposed by Rooth (1992). I propose a topic hypothesis that requires that a coreferential expression enters into an Agree-relation with a topic head in the C-domain. Impossible cases of coreference are ruled out as violations of the locality constraint imposed by the syntactic Agree-relation.
RBOA Algorithm Based on Region Segmentation and Point Update

Robot bean optimization algorithm (RBOA) is a recently proposed cooperative target searching method for swarm UAVs based on bean optimization algorithm (BOA). Considering the shortcomings of low initialization coverage and low efficiency of free space search for RBOA, we propose a region segmentation and point update strategy for RBOA. The region segmentation strategy divides the search space for RBOA initialization to improve the search efficiency. The point update strategy updates the base position in the free space to improve the utilization of UAV information obtained from traversal search. Compared with the RBOA and other target search algorithms, this method is more efficient and effective in unknown environment search.
A hybrid filtering-based network document recommendation system in cloud storage

Since the key requirement of users is to efficiently obtain personalised services from mass network document resources, a hybrid filtering-based network document recommendation system is designed with the method of incorporating the content-based recommendation and collaborative filtering recommendation based on the powerful and extensible storage and computing power in cloud storage. The proposed system realises the main service module on Hadoop and Mahout platforms, and processes the documents containing the information of user interests by applying AHP-based attribute weighted fusion method. Based on the network interaction, the proposed system not only has advantages on the extensible storage space and high recommendation precision but also has an essential role in realising network resources sharing and personalised recommendation.
An Ensemble LSTM Architecture for Clinical Sepsis Detection

Sepsis is a life-threatening condition that has to be treated at an early stage.Doctors use the Sequential Organ Failure Assessment score for the earliest possible recognition.In addition, the practitioner's many years of experience help in order to facilitate an immediate response.Mortality decreases with every hour that sepsis is detected and treated with antibiotics.In this years Phys-ioNet/Computing in Cardiology Challenge the objective is to automatically detect sepsis six hours before the clinical prediction.This paper describes the implementation of an Long Short-Term Memory network for an early detection of sepsis in provided hourly physiological data.An utility score of 0.29 was achieved when testing on the full hidden test set.All entries were submitted using the team name "404: Sepsis not found".
Network Approach To Managing The Region's Education

Collaboration between different network actors is crucial for regional development.Stability of networks, distribution of power among actors within the network, adaptation of actors to the changing environment, and network effectiveness are important issues of network theory.We demonstrate how these ties relate to each other and which ones are crucial for collaboration in a government-funded collaborative network in the educational sphere in the Republic of Buryatia.Our analysis of two experiences in education shows that a central organisation in a small network may help to solve the problem by coordinating other organisations and freeing others from managing relationships.Moreover, centralised integration might facilitate the power distribution among actors and contribute to more network effectiveness.In our two examples, networks had missions of both decision-making and implementation.Central organisations could not make all decisions or impose decisions on other organisations.Therefore, a collegial governing body may help to improve the legitimacy of decision-making and the effectiveness of implementing decisions.
N-gram based Machine Translation for English-Assamese: Two Languages with High Syntactical Dissimilarity

To bridge the language constraint of the people residing in northeastern region of India, machine translation system is a necessity. Large number of people in this region cannot access many services due to the language incomprehensibility. Among several languages spoken, Assamese is one of the major languages used in northeast India. Machine translation for Assamese language is limited compared to other languages. As a result, large number of people using Assamese language cannot avail lots of benefits associated with it. This paper has focused on the development of the English to Assamese translation system using n-gram model. The n-gram model works very well with the language pair having high dissimilarity in syntax compared to other models. The value of n has a very big role in the quality and efficiency of the system. Bilingual Evaluation Understudy (BLEU) score differs significantly with the change of the n-gram. This model uses tuples to reduce the consumption of excess memory and to accelerate the translation process. Parallel corpus has been used for training the n-gram based decoder called MARIE. The number of translation units extracted using n-gram model is much less than the translation units extracted using phrase based model. This has a high impact on system efficiency.
International Scientific Conference on Informatics: Problems, Methodologies and Technologies

Annual international scientific conference «Informatics: problems, methodologies, technologies» is being held since 2001. Conference is a platform for international cooperation and collaboration of scientists from near and far abroad (Belarus, Kazakhstan, Uzbekistan, Azerbaijan, Germany, Bulgaria, Turkey, Iraq, Afghanistan). From the Russian side, the scientists from the leading universities take part in conference: Moscow State University, Bauman Moscow State Technical University, Moscow Power Engineering Institute, Higher School of Economics - National Research University, Novosibirsk State University, Southern Federal University etc. In total, scientists from Top-100 leading universities and dozens of scientific organizations yearly participate in conference. The subject matter of the topics is presented by several scientific schools on data processing and protection, modelling in fundamental and applied researches and telecommunication.
Aspect-level sentiment analysis on goods and services tax tweets with dropout DNN

Sentiment analysis (SA) is a primary use case for natural language processing, where data scientists analyse comments on social media to get instant feedback and improvement in future product releases. In this study, SA was performed on the goods and services tax (GST), which is one of the greatest tax reforms in India. In this study, crucial statements (tweets) about GST were analysed using POS-N-gram tokenisation approach to extract word tokens for classifying sentiments or opinions. The objective of this proposed work is to improve the sentiment classifications accuracy of the review data with an optimal number of reduced terms. In this study, a novel approach improved PCA is proposed for dimensionality reduction and a dropout deep neural network classifier is proposed for sentiment classification. These methods are evaluated on the proposed dataset (Corpus-1) and two other benchmark datasets like movie reviews (Corpus-2) and SemEval 2016 (Corpus-3) datasets. Experimental results clearly evident that the proposed approach outperforms the existing methods.
Chatbot Design Method Using Hybrid Word Vector Expression Model Based on Real Telemarketing Dat

In the development of commercial promotion, chatbot is known as one of significant skill by application of natural language processing (NLP).Conventional design methods are using bag-of-words model (BOW) alone based on Google database and other online corpus.For one thing, in the bag-of-words model, the vectors are Irrelevant to one another.Even though this method is friendly to discrete features, it is not conducive to the machine to understand continuous statements due to the loss of the connection between words in the encoded word vector.For other thing, existing methods are used to test in state-of-the-art online corpus but it is hard to apply in real applications such as telemarketing data.In this paper, we propose an improved chatbot design way using hybrid bag-of-words model and skip-gram model based on the real telemarketing data.Specifically, we first collect the real data in the telemarketing field and perform data cleaning and data classification on the constructed corpus.Second, the word representation is adopted hybrid bag-of-words model and skip-gram model.The skip-gram model maps synonyms in the vicinity of vector space.The correlation between words is expressed, so the amount of information contained in the word vector is increased, making up for the shortcomings caused by using bag-of-words model alone.Third, we use the term frequency-inverse document frequency (TF-IDF) weighting method to improve the weight of key words, then output the final word expression.At last, the answer is produced using hybrid retrieval model and generate model.The retrieval model can accurately answer questions in the field.The generate model can supplement the question of answering the open domain, in which the answer to the final reply is completed by long-short term memory (LSTM) training and prediction.Experimental results show which the hybrid word vector expression model can improve the accuracy of the response and the whole system can communicate with humans.
Driver Distraction System using Face, Eye, Yawn And Pulse Detection

Secure car driving is an important entity for safety and is the first priority for any automobile driver and is the main reason for designing a system which captures the state of the drivers eyes and his/her facial variations combined with pulse fluctuations, altogether they gives us parameters to decide whether the driver should be notified or not. Eye detection - more precisely it analyses the eyes and check if they're closed or open using camera module the amount of frames during which eyes are closed is decided. When this number of frames is above a selected threshold, the drive will get a alert. Camera module periodically takes snaps so as to confirm safety..A true time system which captures the state of the driver which will benefit many of us round the globe.
Optimal Load Distribution of Thermal Generating Units using Particle Swarm Optimization (PSO)

This paper shows load planning of two thermal generating units feeding a load of 200 MW using Particle swarm optimization (PSO). The PSO involves selection of population size or number of particle, fitness function. The advantages of PSO over conventional method are better and reliable solution, better convergence rate. Initially fitness function, population size corresponding to each variable are decided, thereafter PSO is used for finding most optimal solution of the generating units under different set of iteration. The performance of system using PSO is compared with conventional method in terms of the tolerance band.
K-Means Algorithm for Clustering Afaan Oromo Text Documents using Python Tools

With the advancement of technology and proliferation of computers in the country, the amount of Afaan Oromo language news documents produced increasingly which becomes a difficult task for news agencies to organize such huge collection of documents items manually. To solve this problem, researches is conducted using unsupervised machine learning python tools for Afaan Oromo news document clustering with low cost and best quality of clustering solution. In this research work focusing on k-means clustering analysis which produced better results as compared to the other cluster analysis both in terms of time requirement and the quality of the clusters produced.
Consistency formation of fuzzy multi-attribute group decision making based on alternative adjustment

Analytic hierarchy process (AHP) is a method usually used in group decision making. The process of group decision making using AHP is essentially an individual preference of the decision maker incorporating process. In this process, it becomes one of the cores for studying theory and method of group decision making, how we incorporate effectively different forms of preference information. Therefore, in this paper, we propose a solution which makes consistency preference of decision maker group form finally by decision alternative adjustment. We can make clear the deference of decision preference of each division on basis of group division by means of decision information of the decision maker and put forward to employ flexibly AHP for consistency formation of fuzzy multi-attribute group decision making. Thus, an effective dynamic group decision making process is formed. A group decision making example of market approves of new tissue product shows the feasibility of the proposed method.
Deformation detection algorithm of shallow and large-span tunnel support structure based on wireless sensor network

In order to improve the safety of tunnel support structure, a deformation detection algorithm based on wireless sensor network is proposed. The wireless sensor network nodes were deployed with the randomly distributed autonomous network grid structure, and the deformation force parameters of the support structures of shallow span and large span tunnels were obtained with the pressure sensor, and the power spectrum characteristic quantities of the sensor parameters were extracted. The time-frequency characteristic decomposition method is used to realise the beamforming processing deformation parameters. The quantitative analysis ability of the improved structure deformation is detected according to the detection of deformation amplitude and the estimation of related parameters. The experimental results show that the detection accuracy of the method is improved from 87% to 100%, and the detection performance is improved, which verifies the effectiveness of the method.
SMART GLOVE Sign to Speech Conversion and Home Automation Control for Mute Community

Communication plays a vital role for human beings. This project helps in improving the communication with the dumb people using flex device technology. a tool is developed that may translate totally different signs as well as Indian language to text also as voice format. The people who are communicating with dumb people might not perceive their signs and expressions. Through language, communication is feasible for a deaf-mute person without the means of acoustic sounds. The aim behind this work is to develop a system for recognizing the language, that provides communication between people with speech impairment and normal people, thereby reducing the communication gap between them. Compared to different gestures (arm, face, head and body), hand gesture plays a vital role, because it expresses the user's views in less time. within the current work flex sensor-based gesture recognition module is developed to acknowledge English alphabets and few words and a Text-to-Speech. Hence, an approach has been created and changed to listen to the gesture-based communication. it will be very useful to them for conveying their thoughts to others.
The role of knowledge management practices on innovation performance in a public telecommunication company

The aim of this research is to identify the role of knowledge management practice in enhancing the innovation capability of an organisation. The study was conducted in a national public telecommunication company using 100 participants in Jakarta. Multiple regressions were used to analyse whether knowledge acquisition, knowledge sharing, and knowledge application can contribute to the organisation's innovativeness. The results showed that partially, only knowledge sharing and application had significant and positive relation to innovation capability. While simultaneously, knowledge management practices in the company could impact the innovation capability. The insights from the study were that knowledge management practices should be systematically implemented in the organisation. The sharing and application of important knowledge throughout the organisation would help increase the innovative thinking of the employees, hence indirectly affect the innovation capability of the organisation.
Sentiment Analysis

In this chapter, we cover one of the most interesting and widely used aspects pertaining to natural language processing (NLP), text analytics, and machine learning. The problem at hand is sentiment analysis or opinion mining, where we want to analyze some textual documents and predict their sentiment or opinion based on the content of these documents. Sentiment analysis is perhaps one of the most popular applications of natural language processing and text analytics, with a vast number of websites, books, and tutorials on this subject. Sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. From a real-world industry standpoint, sentiment analysis is widely used to analyze corporate surveys, feedback surveys, social media data, and reviews for movies, places, commodities, and many more. The idea is to analyze the reactions of people about a specific entity and take insightful actions based on their sentiments.
Electronic or Printed? Peculiarities of Reading Practices of Students and Researchers

This article examines two ways to practice reading of educational and professional literature of students and researchers in terms of electronic and printed forms of reading. The text structures the bases of preferences of reading from the screen and paper texts, reveals the peculiar features of reading practices and their perception in the context of learning and scientific work, specifying the differences for different levels of academic and scientific environment, separately for students and researchers, including postgraduates and teachers. It describes the actual features and instrumental guidelines that are typical for the prevailing screen reading in the present and senses that reading printed texts has for students (aesthetic and kinesthetic motives) and researchers (sacralizing the book, making a home library).
The Application of Fisher Scoring Algorithm on Parameter Estimation of Normal Distributed Data

In statistics, parameter estimation is the estimation of a population using sample data. A population data certainly has a certain distribution. Fisher Scoring is a form of Newton's method which is commonly used in solving the maximum likelihood equation. The focus of this research is to estimate distributed data using the fisher scoring algorithm
Towards securing cloud data in the multi-cloud scenario

Cloud computing has emerged to be the accepted computing model which provides services on-demand.The most used service layer is infrastructure as a Service (IaaS) to outsource data to the cloud.With this service, organizations and individuals can avail of cloud services in pay as you use fashion instead of investing money for such infrastructure.Cloud provides many such benefits to its users.However, as the cloud servers are remote and assumed to be untrusted, users are worried about data security.Initially, a single cloud was used to store data.With the advancements in technologies and for reliability reasons, the concept of multi-cloud has emerged.The security and reliability issues with a single cloud can be overcome with multi-cloud systems.The rationale behind this is that a single cloud might have malicious insiders.When two or more clouds collaborate and provide services to end-users, it is expected to have more reliability and possible reduction in malicious insiders.This paper focuses on studying the potential security of data that is stored in multi-cloud.We built an algorithm and prototype application that demonstrates the concept of securing data in a multi-cloud environment.The empirical results revealed that the proposed system could ensure the data outsourced to cloud computing where a multi-cloud scenario prevails.