Reviewerly/exHarmony huggingface dataset:
Cloud Data Storage using Attribute based Encryption with Verifiable Outsourcing Auditor Security Schemes

Cloud storage services turned out to be continuously elegant. Owing to the implication of security, a few cloud storage secret composing schemes are anticipated to monitor data from those that don't approach. Every single such plan accepted that cloud storage providers region unit safe and can't be hacked; at the same time, in pursue, a few experts may drive cloud storage providers to uncover client insider facts or confidential data on the cloud, hence by and large going around storage mystery composing schemes. Amid this paper, we tend to bless our style for a pristine cloud storage mystery composing subject that permits cloud storage providers to make persuading false client privileged insights to protect client security. Since coercers can't reveal whenever acquired insider facts zone unit genuine or not, the cloud storage providers ensure that client security keeps on being immovably secured. The vast majority of the anticipated schemes expect cloud storage service providers or dependable outsiders taking care of key administration square measure solid and can't be hacked; nonetheless, in pursue, a few substances would conceivably capture interchanges among clients and cloud storage providers at that point urge storage providers to unfasten client privileged insights by misuse government control or diverse proposes that. All through this case, scrambled information square measure thought to be known and storage provider's square measure asked for to unfasten client insider facts
VMS Mean and Minimum Utilization VM Selection Algorithm for Cloud Datacenter

Cloud datacenter provides the various resources on-demand for the end consumers on pay-per-use through internet connection. The request for computing resources has increased which leads to the growth of power consumption inside datacenters. The major challenge is how to balance the profits by decreasing the consumption of energy and violation of SLA. VM Consolidation techniques helps VMs to migrate between the host machines in reducing energy consumption, but again which VM to select for migration between the host machine is again the challenging task. In this paper VMs Mean and Minimum Utilization VM Selection Algorithm is proposed.To select the suitable VM’s in host machine for the migration, VMs Mean method is used which finds the lowest CPU utilizing VM. To test this algorithm CloudSim Toolkit is been used. The parameter such as EC, SLAV, PDM, ESV, VM migration and SLATAH is used as a metric to evaluate the Algorithm efficiency.
Indian Sign Language Recognition Using Canny Edge Detection

n our society, it is very difficult for hearing impaired and speech impaired people to communicate with ordinary people. They use sign languages to communicate, which use visually transmitted sign patterns, generally includes hand gestures. Sign languages being difficult to learn and non-universal, there is a barrier of communication between the hearing impaired and ordinary people. To break this barrier a system is required that can convert sign language to voice and vice versa in real-time. Here, we propose a real-time two-way system, for communication between hearing-impaired and normal people, which converts the Indian Sign Language (ISL) letters into equivalent alphabet letters and vice versa. In the proposed system, using a camera, images of ISL hand gestures are captured. Then Image pre-processing is done so that these images are ready for feature extraction. Here, a novel approach of using the Canny Edge Detection Algorithm. Once the necessary details are extracted from the image, it is matched with the data set, which is classified using Convolutional Neural Network, and the corresponding text is generated. This text is converted into a voice. Similarly, using a microphone, the voice input of an ordinary person is captured and converted into text. This text is then matched with the data set and a corresponding sign is generated. This system reduces the gap in communication between hearing-impaired and ordinary people. Our method provides 98 % accuracy for the 35 alphanumeric gestures of ISL
Breast cancer diagnosis using a Minkowski distance method based on mutual information and genetic algorithm

Breast cancer is one of the most frequently diagnosed cancers and can lead to death in women worldwide. Diagnosing breast cancer is one of the most challenging tasks as symptoms may only be present in later stages. This paper presents a mutual information genetic algorithm (MIGA) where MIGA is a combination of two algorithms, mutual information (MI) and genetic algorithm (GA). The method of this work is as follows: attributes of breast cancer patients were collected using the Breast Cancer Wisconsin Diagnostic dataset. A breast cancer diagnosis system was developed using a GA and hybrid algorithm (genetic and K-nearest neighbour) and then used MI and GA. GA fitness was calculated using the Minkowski distance method. The obtained solutions are verified for three algorithms (GA, GA+KNN, and MIGA). Finally, the proposed MIGA algorithm results show that the highest accuracy (99%) obtained with the GA-based MI features.
DOMESTIC COMMUNAL WASTEWATER TREATMENT PLANT EVALUATION IN SINDANGRASA, BOGOR, INDONESIA

The communal domestic wastewater treatment plant functions to collect and treat domestic wastewater at the source location according to the communal scale treatment capacity. The communal Wastewater Treatment Plant (WWTP) in RT (neighborhood association) 01 / RW (citizen association) 09 Sindangrasa was built in 2016 using the Anaerobic Baffled Reactor (ABR) system. This study aims to evaluate the Communal WWTP by analyzing the quality of wastewater compared to PerMenLH No. 68 of 2016, calculates the processing efficiency and analyzes the water quality of the community wells around the Communal WWTP compared to regulation of PerMenKes No.416/MENKES/PER/IX/1990. Data collection techniques using the Slovin technique in the pre-research stage, direct sampling on location and testing in an accredited laboratory. The results showed the quality of wastewater exceeds the quality standard for BOD, TSS and Total Coliform parameters, the quality of residents' well water exceeds the quality standard for parameters pH, Cr VI and Total Coliform. Communal WWTPs in RT 01/ RW 09 Sindangrasa have not been efficient in treating residents' domestic wastewater. The management of Communal WWTPs needs to be improved so that the effluent quality and processing efficiency values ​​meet the specified regulatory standards.
Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition

tutorial Evolutionary computation and evolutionary deep learning for image analysis, signal processing and pattern recognition Share on Authors: Mengjie Zhang Victoria University of Wellington, Wellington, New Zealand Victoria University of Wellington, Wellington, New ZealandView Profile , Stefano Cagnoni University of Parma, Parma, Italy University of Parma, Parma, ItalyView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 1343–1372https://doi.org/10.1145/3377929.3389856Online:08 July 2020Publication History 0citation202DownloadsMetricsTotal Citations0Total Downloads202Last 12 Months74Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Neuroevolution for deep reinforcement learning problems

tutorial Share on Neuroevolution for deep reinforcement learning problems Author: David Ha Google Brain, Tokyo, Japan Google Brain, Tokyo, JapanView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 550–593https://doi.org/10.1145/3319619.3323370Published:13 July 2019Publication History 1citation93DownloadsMetricsTotal Citations1Total Downloads93Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation

Aiming at the weakness of the current evolutionary algorithms for high-dimensional and numerical optimisation problems of global convergence, a novel chaotic grey wolf optimisation (NCGWO) is proposed for solving the high-dimensional optimisation problems. Firstly, the six chaotic one-dimensional maps are introduced and their mathematical models are improved with their mapping ranges being in the interval (0, 1). Secondly, the diversity experiments are conducted to test the results of the chaotic maps. The experiments show that the initial population by chaotic maps is superior to the GWO algorithm and the Sine map is best. Finally, the CSGWO algorithm is also proposed based on the NCGWO algorithm with the parameter C by Sine map. The simulations demonstrate that the performance of the GWO algorithm can be improved by the chaotic maps for high-dimensional and numerical optimisation problems, and the effectiveness of the CSGWO algorithm is superior to other evolutionary algorithms and achieves better accuracy and convergence speed.
Comparative Case Study Difference Between Azure Cloud SQL and Mongo Atlas MongoDB NoSQL Database

Azure SQL and Atlas Mongodb NoSQL(Azure instance) databases are the most popular, systematic process to database solutions. Which Azure SQL database is also referred to as RDBMS (Relational Database Management Systems). The data are structured into tables or associations. The Atlas Mongodb NoSQL database is called a non-relational database management systems. The data are included in unstructured tables or associations. In this research, evaluate both the Azure SQL and Atlas Mongodb NoSQL databases. During the experiment compare the loading time, response time, and retrieval time of both Azure SQL and Atlas Mongodb NoSQL databases, and justify which one is fast, efficient and better performance.
What Is Machine Learning?

The first Checkers program from machine learning pioneer Arthur Samuel debuted in 1956, demonstrating artificial “intelligence” capabilities [68].
Performance Examination and Feature Selection on Sybil User Data using Recursive Feature Elimination

Machine Learning (ML) research greatly helps in predicting model-based outcomes with high levels of accuracy based upon the training and testing of the models through the datasets. The social networks constitute one of the domains where ML can be used effectively to ensure the authenticity and security of the valid users. With the increase in usage of Online Social Networks (OSNs), the cases of spam and malicious activities can be found in abundance and Sybil nodes pose one such kind of safety and security hazard. Sybil account detection is not an easy task since they mimic the actual behavior of human accounts up to a great extent. In this paper, we look at one such scenario of Sybil accounts on the OSN, Twitter where machine leaning models have been used to train the machine with the existing datasets so as to be able to detect these malicious users before they can bring harm to the normal communication of the genuine users. Since the datasets used are so vast, the process of feature selection has been carried on the datasets as part of pre-processing before the actual classification as it assists in enhancing the model performance. Support Vector Machine–Recursive Feature Elimination (SVM-RFE) and Logistic Regression–Recursive Feature Elimination (LR-RFE) techniques have been used in this study for the selection of significant features. The classification model is trained on the selected features using Random Forest (RF) and K-Nearest Neighbor (KNN) algorithms. We also analyzed the biasing effects of fake accounts on the human accounts datasets during the process of features selection and classification. It has been shown that the RF algorithm outperformed KNN on the feature sets selected through SVM-RFE and LR-RFE.
A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

© 2012 IEEE. Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms. © 2019 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.
Public key and levelled attributes access policy oriented fully homomorphic encryption scheme

Fully homomorphic encryption (FHE) has been considered as significant transition of cryptography, a difficult aim which could answer the world's obstacles of security and trust. FHE is also considered among the golden solutions for achieving security in cloud computation, MPC, data banks, etc. FHE was conceptualised in 1978, but it took approximately three decades to invent the first credible construction of FHE. The research in this area erupted after Craig Gentry's construction of FHE in 2009. Considerable developments have been made in getting more practical and efficient solutions. In this paper, we have gathered the state of art information in this field through surveying various schemes. We proposed detailed construction of our fully homomorphic system. The security and correctness analysis for the proposed procedure along with the experimental results are also given in the paper.
Rainfall Prediction using Bpnn

Gauge of precipitation and ground water of an express area will assist water aid executives to preference the application of water and limit of water. The records for envisioning organized in time direction of movement i.e., functions that helpers for parent are yr, month, common precipitation, ground water level. For this conjecture lower back expanded neural frameworks with widely appealing weight replace at n-degree disguised layer changed into made to constrain the bungle fee and enhance the preference. Our system engaged with information estimate and statistics discernment challenge to mining process. for purchasing ANN based figure with APRIORI-NN based totally portrayal method become taken into consideration to widen and assist water aid chiefs.
Development of a Wireless Surface Electromyography (sEMG) Signal Acquisition Device for Power-Assisted Wheelchair System

Muscle fatigue due to long-term of manual wheelchair propulsion is an issue faced by most of users. A power-assisted wheelchair developed to amplify propulsion force that activated by surface electromyography (sEMG). A sEMG detection wireless device to trigger the assistive system is an advantage to ease installation on user’s body and wheelchair which is developed in this study. A device that operated on Arduino Nano processor connected to 2 (two) Myoware sEMG sensors to record muscles electrical potential (EP), recognise the pattern and activate DC motors wirelessly connected through radio frequency. Data monitoring on personal computer and smart phone associated with Bluetooth to store and ease observation on recorded information. Tested on 1 healthy participant by propelling a manual wheelchair on tiled floor. Developed device’s performance tested and the result are average sampling rate (33.55 ms) and average reading latency was just 12.38 ms. Compared to wired device, sampling rate is faster by % and reading latency slower by 1.04 %. Result demonstrate that developed wireless device would improve in speed in signal reading and enhancement on reading latency is needed to provide a reliable device for power-assisted system in the future.
Security Competence as an Integral Part of Competence-Based Learning Approach: Russian and European Experience

Since the end of the 20th century, educational standards have been changing, and many countries have switched from a traditional approach to a competence-based one. Despite the numerous works on the issue, there is still no standardized definition of competencies and their components. The study of the competencies, including new digital competencies, is necessary because education within the framework of the competence-based approach helps to reduce the gap between theory and practice, which makes it possible to train specialists ready to work in a new digital era, where one of the main tasks is the formation of the digital economy. The digital economy implies the development and creation of digital, high-tech products within the country. The ability to safely operate digital products is reflected in security competence, which is important not only in professional activities but also when using the internet, computer programs, and so on, for personal purposes. In this article, we will analyze different approaches to the concept of security competence within the prism of competence-based learning in Russia and Western countries, including European and American experiences.
A Rough Set Pooled Fitness Function Based Particle Swarm Optimization Algorithm using Golden Ratio Principle for Feature Selection

Particle Swarm Optimization, a nature based stochastic evolutionary algorithm that iteratively tries to improvise the solution pertaining to a particular objective function. The problem becomes challenging if the objective function is not properly identified nor it is properly been evaluated which results in slow convergence and inability to find the optimal solution. Hence, we propose a novel rough set based particle swarm optimization algorithm using golden ratio principle for an efficient feature selection process that focusses on two objectives: First, that results in a reduced subset of features without conceding the originality of the data and the second is that yields a high optimal result. Since many subset of features might result with a meaningful solution, we have used the golden ratio principle to extract the most reduced subset with a high optimal solution. The algorithm has been tested over several benchmark datasets. The results shows that the proposed algorithm identifies a small set of features without convincing the optimal solution, thus able to achieve the stated objectives.
Dirichlet-multinomial Model with Varying Response Rates over Time

It is believed that overdispersion or extravariation as often referred is present more in survey data due to the existence of heterogeneity among and between the units.One approach to address such a phenomenon is to use a generalized Dirichlet-multinomial model.In its application the generalized Dirichlet-multinomial model assumes that the clusters are of equal sizes and the number of clusters remains the same from time to time.In practice this may rarely ever be the case when clusters are observed over time.In this paper the random variability and the varying response rates are accounted for in the model.This requires modeling another level of variation.In effect, this can be considered a hierarchical model that allows varying response rates in the presence of overdispersed multinomial data.The model and its applicability are demonstrated through an illustrative application to a subset of the well known High School and Beyond survey data.
Quality Of Arguments Used In The First-Round Presidential Debate: Critical Pragmatics And Stephen Toulmin‘s Perspective

It is important for politicians to have a good argumentative skill. For state leaders, the ability to think logically, to use rhetoric, and to argue systematically, scientifically, sharply, and eloquently is very crucial. The year 2019 is the political year for Indonesia. Political campaigns leading to the presidential and the legislative election, both in national and regional levels, will happen in 2019. The focus of this research is to investigate the depth and breadth of the arguments stated by the presidential candidates and how those arguments are presented. The research substantial data source is in the form of speech transcript of the recorded video of the first-round presidential debate by two pairs of president and vice-president candidates. The research data were the debate arguments found in the video transcript and the contexts surrounding them. The data gathering method used observation by employing recording and note-taking techniques. After the data were gathered, they were selected and classified based on their types for further analysis. The analysis method was distributional method and content analysis. Both data analysis methods were applied to yield significant results of the study. The results showed that there are simple patterns of argument containing claim, subclaim, data, and warrant. The orders of elements of arguments might be varied. The research results also showed that there were various pragmatic meanings found in the arguments used by the president and vice-president candidates. The results of the study which was analyzed using the critical pragmatic perspective reflect how far the candidates were concerned with the marginalized, the underprivileged, and the subjugated people.
Foundations for an Applied Science of Data Visualization

A conceptual framework for visualization design is based on human perception. The nature of claims about sensory representations is articulated, with special attention paid to the work of perception theorist J. J. Gibson. This analysis is used to define the differences between a design-based approach and a science-of-perception-based approach. A classification of abstract data classes is provided as the basis for mapping data to visual representations.