Reviewerly/exHarmony huggingface dataset:
An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra $(\mathcal{A}; \oplus , \otimes )$, wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference ⊗ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.
Graduates of the Bachelor of History: Occupational Market, Demands and Level of Satisfaction of Vocational Training

However, since it is not possible to consider "homogeneous" characteristics of the labor market for all countries, due to the great differences and
Neural Networks

We have discussed so far the case of a single neuron. In this section we shall consider multiple layers of neurons whose outputs are fed into other layers of neurons, forming neural networks. A layer of neurons is a processing step into a neural network and can be of different types, depending on the weights and activation function used in its neurons (fully-connected layer, convolution layer, pooling layer, etc.) The main part of this chapter will deal with training neural networks using the backpropagation algorithm.
Assessment of Strategic Messages Communicated for Sustainable Solid Waste Management: A Study of Migori County, Kenya

Solid waste management has become great concern for governments and cities due to its effects on health, environment and climate change.The aim of sustainable solid waste management (SSWM) is to achieve circular economy and realize sustainable development goals.Communication plays a significant role in empowering communities with knowledge, influencing behaviour and promoting collaboration on waste management between governments and communities.Past research shows indiscriminate dumping of waste and inadequate knowledge on how to manage solid waste including reduce, reuse and recycle waste among communities in Africa.However, there is limited focus in research on messages communicated for SSWM.This study sought to assess strategic messages communicated for SSWM and media used in the communication.The study conducted in Migori county Kenya, using a mixed methods approach involved a total of 399 town residents sampled using purposive, stratified, and simple random sampling techniques.The study found lack of focus on strategic messages for SSWM in the communication by Migori county government.Communication is uncoordinated, conducted in public meetings, local radio, and by waste management supervisors.Most community members lack knowledge on SSWM practices.The study recommends communication of strategic messages for SSWM to improve community knowledge of SSWM; communication using participatory media accessible to the community; and inclusion of community knowledge in the construction of strategic messages for SSWM.
Prediction and Classification into Benign and Malignant using the Clinical Testing Features

Breast Cancer is the most often identified cancer among women and a major reason for the increased mortality rate among women. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. The advanced engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. Data mining techniques contribute a lot to the development of such a system, Classification, and data mining methods are an effective way to classify data. For the classification of benign and malignant tumors, we have used classification techniques of machine learning in which the machine learns from the past data and can predict the category of new input. This study is a relative study on the implementation of models using Support Vector Machine (SVM), and Naïve Bayes on Breast cancer Wisconsin (Original) Data Set. With respect to the results of accuracy, precision, sensitivity, specificity, error rate, and f1 score, the efficiency of each algorithm is measured and compared. Our experiments have shown that SVM is the best for predictive analysis with an accuracy of 99.28% and naïve Bayes with an accuracy of 98.56%. It is inferred from this study that SVM is the well-suited algorithm for prediction.
Optical Character Recognition for Classical Philology

This paper explains the technology behind recent improvements in optical character recognition and how it can be attuned to produce highly accurate texts of scholarly value, especially when dealing with difficult scripts like ancient Greek. Drawing upon several practical experiments using the Ciaconna OCR system (itself based on OCRopus), it shows: the impact of Unicode normalized forms on recognition accuracy; the importance of removing ambiguously encoded characters from training material; the advantage of using separate classifiers for different scripts; the helpful effects of image augmentation; and the effects of binarization levels. It also describes how Ciaconna embeds information about spell-check and dehyphenation within its output.
Design and Controller Development of Drone Capable of Landing, Takeoff, and Travelling on a Vertical Wall

This paper describes the development of a drone design and controller capable of landing and driving on the outer wall of a building, delivering the object to the target location and after completing the mission, then taking off from the outer wall and reflying. It suggests a solution that can fulfill a mission even in the windy conditions, which is the biggest weakness of the drone. Four motors and wheels are located on the drone’s body to drive on the wall, and the damper was designed based on impact analysis. To avoid interference with the wheels, brushless DC (BLDC) motors are installed at the bottom of the drone, and propeller guards are designed to prevent collisions with the wall. The thrust must be specially controlled to attach this drone to the wall and to take it off the wall.
Immersive technologies will reduce the likelihood of accidents at industrial plants

One of the causes of accidents at industrial enterprises is most often the human factor. In pursuit of zero injury, industrial companies strive to improve the quality of employee training. Solutions based on immersive technologies are being introduced into the production process, for example, simulators for training employees to work with equipment based on virtual reality (VR) or three-dimensional graphics (3-dimensional, 3D). Such technologies replace the practice of working on real equipment and allow deeper training of skills in a safe environment. In addition, using virtual reality, you can simulate any emergency situations that are unacceptable to be created on real objects. All this subsequently helps to reduce the negative impact of the human factor on production, and therefore, to reduce the likelihood of injuries, accidents or even fatalities at enterprises.
Reversible Watermarking in Medical Image Using RDWT and Sub-Sample

This paper proposed a reversible medical image watermarking scheme using Redundant Discrete Wavelet Transform (RDWT) and sub-sample. To meet the highly demand of the perceptional quality, the proposed scheme embedding the watermark by modifying the RDWT coefficients. The sub-sample scheme is introduced to the proposed scheme for the enhancement of the embedding capacity. Moreover, to meet the need of security, a PWLCM based image encryption algorithm is introduced for encrypting the image after the watermark embedding. The experimental results suggests that the proposed scheme not only meet the highly demand of the perceptional quality, but also have better embedding capacity than former DWT based scheme. Also the encryption scheme could protect the image contents efficiently.
Multi-Layered ‘Odd-Even’ Reversible Embedding for Encrypted Images

Reversible Data Hiding (RDH) has been under research for the past two decades. Recently RDH in encrypted images (RDH-EI) draws more attention among the researchers. Since it is used effectively in privacy protection and cloud computing. In this paper, a RDH-EI using LSB based odd-even embedding technique scheme is proposed. Initially the cover image is encrypted using block and pixel scrambling and bit plane mix ordering. Block and pixel scrambling is done through four random walks/Space Filling Curves (SFCs). Each block and pixel is tested for randomness of information source through each random walk. Finally the secret message is embedded in each block of image based on testing the pixel value and position value in the block using LSB embedding. This method avoids overflow/underflow issues that usually happens in RDH/LSB embedding.
Challenges in Internet of Things

Internet of Things (IoT) or Cyber Physical systems (CPS)denote culmination of inter connected equipment, sub systems, objects and living things with notable identifiers (UIDs) and has the capability to exchange information in the network without human or machine intervention. The real benefit of IoT is to establish a smart communications with the current systems and make the information visible to everyone. This paper discusses various challenges in IoT related to security, data management, identity management and network related issues
Efficient Registration of Land using Block Chain Technology

A Block chain as a technical solution for real estate(land registry)transactions that is a technical demo shows technology and development of the solution. In the proposed system, the knowledge about the block chain and what are all the ways the concepts of block chain will be used in real life applications such as land registry, vehicle registration, financial records,etc. In this paper we can detailed study about the land registry concept .There merits and demerits on using the concepts of block chain on this fields are also analyzed.
Context-aware ubiquitous and mobile learning systems: research gaps and challenges

There is a tendency for mobile and ubiquitous learning systems to be context-awareness as context provides valuable information that can be used for content or service adapted to the environment and the user. These systems are applied in both formal and informal contexts. In this study, we carried out a systematic mapping with the aim of providing a more updated panorama of the state-of-the art, identifying gaps and future works in mobile and ubiquitous context awareness learning systems, especially in characteristics such as the most used entities in the context, adaptation of content, extraction of user information and gamification strategies that have been applied in an informal context. Our results show that the location and the user's profile are the entities of the most used context; that the most used type of adaptation is the adaptation of educational resources; that the gamification strategies allow to improve the learning experiences in the informal contexts; and that there are several studies aimed to extract user's information indirectly.
Risk Factors And Security Issues In Various Cloud Storage Operations

Presently a days, cloud computing is a rising and method for registering in software engineering. Cloud computing is an arrangement of assets and administrations that are offered by the system or web. Distributed computing broadens different figuring methods like framework registering, appropriated processing. Today distributed computing is utilized as a part of both mechanical, research and scholastic fields. Cloud encourages its clients by giving virtual assets through web. As the field of distributed computing is spreading the new procedures are producing for cloud security. This expansion in distributed computing condition likewise expands security challenges for cloud designers. Customers or Users of cloud spare their information in the cloud subsequently the absence of security in cloud can lose the client's trust. In this paper we will discuss about on cloud database and information mining security issues in different viewpoints like multi-occupancy, flexibility, unwavering quality, accessibility on different divisions like modern and research regions, and furthermore examine existing security methods and methodologies for a safe cloud condition through enormous information ideas. What's more, this paper additionally study different parts of mechanical, training and research areas. This paper will empower scientists and experts to think about various security dangers, models and apparatuses proposed in existing distributed storage.
Teacher’s Motives in Applying Communication Accommodation Strategies in Secondary ELT Class

The objective of this study is to describe teacher’s motives that underlie the use of communication accommodation strategies in secondary ELT class. This study employed a qualitative approach by observing the strategies used in ELT class and interviewing teacher to ask the reason for performing those features. The participant of this study was one English teacher from secondary ELT class. The result showed that teacher used more convergence features in accommodative strategies rather than divergence. Besides, teacher’s motives in applying communication accommodation strategies were categorized as affective motives to emphasize the closeness with students and cognitive motives to improve students’ understanding in classroom interaction. Teacher was dominant in cognitive motives rather than affective motives when using communication accommodation strategies in secondary ELT class.&#x0D; Keywords: Communication Accommodation Strategies, Affective Motives, Cognitive Motives
E-Voting Infrastructure System for Indonesia

In digital era, Indonesia as democratic country needs to implement e-voting for presidential and governor election. Some countries have implement e-voting and two of them are more populated than Indonesia. E-voting can make voting process and vote distribution better. In this paper we proposed IT infrastructure for e-voting using load balancing, cryptography, and block chain. Also this proposed model can be discussed further about application and system replication to produce similar data from all servers.
Low Power FPGA Implementation of Multi-View Video Coding with Hybrid Compression and Decompression Algorithm

The video is one of the most useful and most appealing medium to represent some information. More usage of digital multi-media via communications media, wireless communications, intranet, internet and cellular mobile leads to the uncontrollable growth of data in media. The video compression technique is used in this research work to improve the processing speed of the entire system. In this work, Low Cost - Multi Video Coding - Hybrid Compression and Decompression (LC-MVC-HCD) method is used to reduce computation complexity. The combinational of Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) algorithms are denoted as hybrid algorithm. Based on this hybrid algorithm, the compression process is performing which improves the video coding efficiency of MVC. The LC-MVC-HCD methodwas implemented in the Matlab, Xilinx and Cadence tool. In Application Specific Integrated Circuit (ASIC) implementation, the area, power, and delay minimized by using the cadence encounter tool with 180nm and 45nm technology. In Field Programmable Gate Array (FPGA) implementation, the number of Lookup Tables (LUTs), slice, and flip-flop are minimized based on two different kinds of Virtex devices such as Virtex -6 and Virtex-7. In Matlab, Peak Signal to Noise Ratio (PSNR), computational time and bit error rate were analyzed for the LC-MVC-HCD method. The experimental outcome showed that the proposed methodology has improved performance ASIC and FPGA up to 2-3% compared to existing methods like Direct mode decision MVC and LC-MVC-DWT.
On using the wisdom of the crowd principles in classification, application on breast cancer diagnosis and prognosis

Breast cancer diagnosis and prognosis are an oblique processes, where errors can be fatal, it is done by experts only. Therefore, researchers are using the promising potentials of classification algorithms to detect malignant and benign tumours. Classification techniques vary widely, from individual classifiers such as rules, trees and functions to ensemble classifiers that combine serval classification algorithms. In this paper, we examine the use of wisdom of crowds' principles in classification of breast cancer for diagnosis and prognosis. We use four well-known datasets and run a collection of 53 algorithms combined with majority voting to simulate the wisdom of crowds. Furthermore, we report the results obtained from all of 53 algorithms executed individually on all four datasets. Therefore, this article can be perceived as a review for these classification methods as well. Moreover, we compare the results obtained from applying majority voting using the best five classifiers, to those obtained by applying the wisdom of the crowds' principles. The results will show that outcome of having too many classifiers reduces the accuracy dramatically. Consequently, it is not advisable to use more than five strong classifiers.
Fuzzy Positional Function Via Fuzzy Filter

The research was based on the principle of defining the fuzzy positional function applying the fuzzy filter. Significant properties were obtained and examples were provided to clarify these properties.
A Secure Framework for Communicating Multimedia Data in Cover Images using Hybrid Steganography Algorithms in Wireless Local Area Network

This paper presents a critical analysis on new and original proposed algorithm based on hiding any data has been used that overcomes the disadvantages of the existing algorithms and helps to provide less similarity between cover image and stego image and obtain accuracy upto 69.6 percentage and increases its robustness using metrics called mean square error and peak signal to noise ratio. In the wireless environment cryptography suffers from various spyware programs that shows corrupted secret information to innocent users who uses image steganography services from user. In our proposed prototype helps to authenticate the sender to make the unnoticeable image from original image. In our proposal work discovers a secure authentication communication model would able to cover multimedia data like first text to be hide, second image to be hide and third audio secret data to be hide in cover image without much noticed to any user in between network. The proposed algorithm has been tested against various existing algorithms to develop how effectively the hybrid steganography algorithm is works, and how effectively it is overcoming the drawbacks of the present image steganography algorithms. The present work is projected to serve the purpose of prevention of changing secret data in cover image after evaluating the distraction values using PSNR and RMSE quality metrics under various image data set taken from facebook shared images . In the scientific investigation, researchers found that three reasons to show that given secure communication is successfully designed with the help of hybrid steganography algorithm that could break attackers intention using TLNUS merged with AES and Key based positioning system[4] gain access the sensitive information available in remote system
Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.
Comparing K-means, K-medoids and ISODATA Clustering Algorithms for a Cloud Service Search Engine

The evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. Cloud computing theoretically provides all computing needs as services. Accordingly, a large number of cloud service providers exist and the number is constantly increasing. This presents a significant problem for a user to find a relevant service provider, and calls for developing a specialized search engine to help users select suitable services matching their needs. Towards this goal, we developed a search engine that crawls the web sites of various service providers, extracts service attributes from their JavaScript Object Notation (JSON) files and normalizes the attributes in a service table. Those attributes are clustered using one of three different algorithms (K-means, K-medoids, and ISODATA). The requirements of a given user are then matched against the centroids of the various clusters to help obtain the closest match. In this paper, we compared the three algorithms with respect to time and accuracy. The ISODATA algorithm exhibited the best performance.
Analysis of web browser for digital forensics investigation

In today's digitalised world, a lot of information is getting online; the size of online data is getting huge day by day, and thus here emerges the field of data science. An internal malicious user can exploit a browser, and the main hindrance comes when all the browsing data is deleted. A forensics investigation needs to extract all the pieces of evidence like history, cookies, URL, sessions, and saved passwords from the cloud space provided by the browser. The research method is mixed qualitative and quantitative. A scenario is created in a virtual environment where there is a victim machine whose browser is exploited. Two different platforms are used for the authentication and verification of pieces of evidence collected. The culprit is being caught based on matching web browsing activities from the victim machine and another machine in the same place as the victim machine.
Intuitionistic Nanogonal Fuzzy Numbers and its Application in FDE

In this paper, we have introduced a new way to solve an ordinary differential equation by using Nanogonal intuitionistic fuzzy numbers. A new version of INFN addressed here. Here we formed different types of NIFN System and we discussed the properties said fuzzy numbers. Demonstration of Nanogonal intuitionistic fuzzy solution in FDE is carried out with INFN.Finally one examples problem also solved with pictorial representation of said method.