Reviewerly/exHarmony huggingface dataset:
Handling Solution Tidal Flood in Kaligawe Area by Polder System Drainage

Kaligawe area is one of the main routes of North Java Coastal traffic and also the gateway of Semarang city from the east. Since more than 5 years this area proned by flooding due to a combination of land subsidence, increasing influence by tidal movement from sea the inability of free flow discharge of river water. In periods of flooding long traffic jams occur for more than 10 kilometers in length. The purpose of this research is to map the flood problem in Kaligawe area, identify the causes of flooding, analyze the technical handling solution and formulate the measures to prevent the flooding in this region. The research method is based on detailed field survey, measurement and observations of water levels, secondary data collection that consist of land subsidence’s, rainfall intensities, tidal elevation. The data are analyzed qualitatively and quantitatively. Among the results of this study is to formulate measures to prevent the number and degree of flooding. These measures can be divided in short, medium term and long term stages. The short term measures consist of construct a temporary weir to regulate water discharge and pump installation including pump strategy. The medium term stage consists of making a polder system, the long term stage consists of spatial sustainability and institutional management of Operation and Maintenance of Polder system.
Comparative Analysis of Color Image Denoising Techniques

The choice of efficient algorithms for denoising digital images is still a scientific challenge to the intersection of functional analysis, statistics and computing.There have been a few distributed calculations and each approach has its suppositions, its key operational points, and its constraints.In this paper, a brief comparative analysis of some significant work in the field of image denoising is Presented.Image denoising techniques which are suitable for color images are classified and compared by using parameter like Peak signal to noise ratio PSNR.The visual and quantitative outcomes demonstrate that the ROAD TGM filter outflanks the current filtering methods in reestablishing the original image which is deteriorated by impulse noise.
Detection and Avoidance of Web Vulnerability using XSS

In today’s life style web applications have become so much essential part. We make use of web applications in most of our day-to-day activities. Hence it has become a big challenge to protect these web applications from hacking. Databases are central to modern websites as they provide storage medium for critical information. It may be of any companies’ sensitive information. Henceforth these websites are targeted by malicious users to gain authority. This paper provides necessary security to the websites, blogs from being attacked and miss leaded. It detects the attack and soon after well avoid by script posting. The application also demonstrate login through SQL injection[16][17] without having the proper required credentials.
Olivine in picrites from continental flood basalt provinces classified using machine learning

Abstract Picrites, dominantly composed of highly forsteritic olivine, can serve as important constraints on primary magma composition and eruption dynamic processes in global continental flood basalt (CFB) provinces. Picrites are commonly divided into high-Ti and low-Ti groups based on whole-rock TiO2 content or Ti/Y ratio. Here, we use an artificial neural network (ANN) to classify the individual olivine in picrites from global CFB provinces according to whether their parental magma is high-Ti or low-Ti to better understand the primary origin and magmatic processes. After training the ANN on 1000 olivine major element compositions data points, the network was able to differentiate chemical patterns for high-Ti and low-Ti olivine and classify olivine into correct types with an accuracy of &amp;gt;95%. Moreover, we find that two types of olivine mix in some single samples from Etendeka, Emeishan, and Karoo CFB provinces. Combining the results with chemical markers of source lithology, we suggest that the two types of olivine originate from two different sources and their olivine populations mixed during the ascent. This mixing then makes the spatial and temporal variation of picrites types in some CFB provinces unclear.
Metrology Atlas Research on the CNKI Literature Big Data of Sports Artificial Intelligence Based on COOC/VOSviewer

Through the bibliometric method and VOSviewer, an emerging literature big data bibliometric visualization tool, the keywords of CNKI literature big date of sports artificial intelligence are sorted by word frequency as reflected by co-occurrence matrix network model, and clustering visualization atlas. The results of analysis show that the literature high-frequency words of sports artificial intelligence research in the current CNKI database include artificial intelligence, sports, college students, interests, and Computer vision. And the co-occurrence matrix network for keywords consist of 91 nodes and 381 lines; with artificial intelligence, sports and college students as the hotspots, the clustering characteristics are embodied in five aspects, namely system, content, direction, scope and trend.
Towards Improving Offline Signature Verification Based Authentication Using Machine Learning Classifiers

Signatures have been accepted in commercial transactions as a method of authentication. Digitizing credentials reduce the storage space requisite for the same information from a few cubic inches to so many bytes on a server. The most frequent use of offline signature authentication is to reduce the turnaround time for cheque clearance. In this paper, machine learning classifiers are used to verify the signature using four image based features. BHsig260 dataset (Bangla and Hindi) has been used. We used signatures of 55 users of Hindi and Bangla each. .Six classifier i.e. Boosted Tree, Random forest classifier (RFC), K-nearest neighbor, Multilayer Perceptron, Support Vector Machine (SVM) and Naive Bayes classifier are used in the work. In the paper, the results of Writer independent model show that accuracy of Hindi off-line signature verification is 72.3 % using MLP with the signature sample size of 20 and that of Bangla is 79 % using RFC with the signature sample size of 23.In user dependent model, for some users, we achieved accuracy of more than 92 % using KNN and SVM.
Predictive Performance of Logistic Regression for Imbalanced Data with Categorical Covariate

Logistic regression is often used for the classification of a binary categorical dependent variable using various types of covariates (continuous or categorical). Imbalanced data will lead to biased parameter estimates and classification performance of the logistic regression model. Imbalanced data occurs when the number of cases in one category of the binary dependent variable is very much smaller than the other category. This simulation study investigates the effect of imbalanced data measured by imbalanced ratio on the parameter estimate of the binary logistic regression with a categorical covariate. Datasets were simulated with controlled different percentages of imbalance ratio (IR), from 1% to 50%, and for various sample sizes. The simulated datasets were then modeled using binary logistic regression. The bias in the estimates was measured using MSE (Mean Square Error). The simulation results provided evidence that the effect of imbalance ratio on the parameter estimate of the covariate decreased as sample size increased. The bias of the estimates depended on sample size whereby for sample size 100, 500, 1000 – 2000 and 2500 – 3500, the estimates were biased for IR below 30%, 10%, 5% and 2% respectively. Results also showed that parameter estimates were all biased at IR 1% for all sample size. An application using a real dataset supported the simulation results.
The Online Sales Application Of Black And White Print Based On Yii Framework On Higher Education E-Commerce Website

&#x0D; Raharja Internet Cafe as a facility in Raharja College which is provided for Personal Raharja in helping provide the need for lecture activities. Raharja Internet Cafe has a problem that the system of selling black and white print products that apply consumers must come directly to the RIC room at LV-002 at Raharja College, but Raharja Internet Cafe cannot accommodate many consumers because of the limited area. These problems are the background for the establishment of an electronic sales system (e-commerce) based on Yii framework with the aim of facilitating the sale of black and white print products for consumers and staff of Raharja Internet Cafe. E-commerce website at Raharja Internet Cafe is a web-based application with a structured programming concept. The e-commerce application development Shop Copy Nicky uses 4 (four) stages according to the steps that exist in software development, including the stages of observation, analysis, literature study consisting of 10 (ten) literature and implementation. The conclusion of the e-commerce website development on Raharja Internet Cafe is that the website built can make it easy for consumers to make transactions, and Raharja Internet Cafe can get comprehensive and real time reports about sales data, and payment systems for consumers that are easier because they use payment system that is done online.&#x0D; &#x0D; &#x0D; Keywords: E-Commerce, Black and White Print, Raharja Internet Cafe
A Face Recognition System Using Directional Binary Code Algorithm And Multi-Svm

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.
Governance and Assessment of Future Spaces: A Discussion of Some Issues Raised by the Possibilities of Human–Machine Mergers

This article explores potential privacy, security, and ethical issues raised by technologies that allow for human–machine mergers. The focus is on research, development and products at the intersection of robotics, artificial intelligence, Big Data, and smart computing. We suggest that there is a need for a more holistic approach to the assessment of such technology and its governance. We argue that in order to determine how the law will need to respond to this particular future space, it is necessary to understand the full impacts of human–machine mergers on societies and our planet—to go beyond these three aforementioned issues. We aim to encourage further discussion and research on this as well as the broader organism-machine merger question, including on our FLE5SH (F = financial, L = legal, E5 = economic, ethical, equity, environmental, and ecosystem, S = socio-political, H = historical) framework for the governance and assessment of these and other future spaces.
The Current Situation of Internet Addiction and Its Impact on Sleep Quality and Self-Injury Behavior in Chinese Medical Students

This corrects the article "The Current Situation of Internet Addiction and Its Impact on Sleep Quality and Self-Injury Behavior in Chinese Medical Students" in volume 17 on page 237.
Information, Analysis Ximmingning Four Neurons Chemical Name of the Information in Intellectual Evidence

The purpose of the research is to extract images from the experimental database by computing the aggregate indicators and introduce them in the parallel processing of models. At present, we have been trying to explain the process of decision-making in algebra of widespread errors and counterfeiting nets in the Ximming network. When deciding whether algorithms in these models are heterogeneous, it does not allow for adequate mathematical formulation of neural networks' transparency problem. As a result, isolation of the images depends on the specialist in neurosurgeons and, in principle, the recommendation.
A Report on the Fourth Workshop on Emerging Software Engineering Education (WESEE 2021)

In this report, we present a pre-organization summary of the fourth workshop on emerging software engineering education (WESEE) held in virtual mode on February 25, 2021, co-located with the 14th Innovations in Software Engineering Conference (ISEC 2021), February 25–27, 2021. The 4th edition of WESEE, a half-day workshop, focuses on open discussions and brainstorming sessions with a theme “Software Engineering Education during COVID-19 Pandemic and Beyond”. The workshop consists of two invited talks of 45 minutes each, two hands-on activities, followed by an open-ended concluding discussion session (30 minutes). A total of 70-75 participants including academic faculty members, students, and industry practitioners participated virtually in the workshop.
A New Hybrid Cryptography Technique in Wireless Sensor Network

The wireless sensor network is a large number of tiny nodes installed in insecure environment for monitoring, gathering and transferring data and are prone to security threats for its limited resources. In order to transmit the data and to protect from different attacks in the network maintained. To achieve confidentiality, authenticity and authorization of data which secure the data from different attacks cryptographic algorithm were used. The number of keys used in the cryptographic algorithm determines the secu the data. Cryptographic algorithms are broadly classified into two types symmetric cryptography and asymmetric the symmetric key cryptographic algorithm, a secret key is in the network and in asymmetric key cryptographic two keys are used for data security. In wireless symmetric key cryptography required more storage to store the key among all the nodes of the network and cryptography more computation time is require encryption and decryption. To avoid memory and computation overhead we proposed a hybrid cryptosystem to handle the security in the wireless sensor network. Initially shared key is exchanged among nodes using ECC which is a public key algorithm. Data is encrypted and decrypted using RC4 symmetric key algorithm. Various performance measures such as time taken for encryption and decryption process and memory needed for storing cipher text data. The proposed model shows faster encryption of data and takes less memory for key s compared to the traditional approach.
FairAccess2.0: a smart contract-based authorisation framework for enabling granular access control in IoT

In this paper, we explore access control area as one of the most crucial aspect of security and privacy in IoT. Actually, conventional security and privacy solutions tend to be less tailored for IoT. Then, designing a distributed access control with user-driven approach and privacy-preserving awareness in an IoT environment is of paramount importance. In this direction, we have investigated in our previous work a new way to build a distributed access control framework based on the blockchain technology through our proposed framework, FairAccess. The first version of FairAccess was based on the Bitcoin's UTXO model. However, this version presented limitations in expressing more granular access control policies. To tackle this issue, this paper upgrades the proposed framework to FairAccess2.0 that uses SmartContract concept instead of the locking/unlocking scripts. Thus, we show a possible working implementation based on ABAC policies, deployed on the ethereum blockchain. The obtained results show the efficiency of FairAccess2.0 and its compatibility with a wide range of existing access control models mainly the ABAC model. Finally, a performance and cost evaluation, discussion and future work are elaborated.
The Water Potability Prediction Based on Active Support Vector Machine and Artificial Neural Network

The total amount of water on the planet is approximately 1.4 billion cubic kilometers, but only 2.5% of it is fresh water. Among freshwater resources, human drinkable water resources account for only 0.3 % of them. In Africa, hundreds of thousands of people fall ill or even die from drinking unclean water every year. If the water potability can be predicted accurately, then it can save a lot of the human, material and financial resources of the country and the region on the drinkability of water resources, which is an important step for the application of machine learning in water resources monitoring. In the technology field, today's machine learning technology has become mature, and it has been applied in many fields such as finance, biology, and medical care. The algorithms in machine learning can be used to help humans quickly identify whether water can be drunk so that the efficiency of identifying water availability is greatly improved. Among the many machine learning methods, artificial neural networks and support vector machine algorithms became popular in machine learning due to their large processing data and fast calculation speed. Therefore, selecting the above two algorithms to judge the drinking ability of water resources is expected to better achieve the desired purpose. In conclusion, after constant tuning of parameters and changing the calculation mode, a high-precision artificial neural network model and SVM prediction model were obtained, which make judgments in an extremely efficient manner and predictions highly accurate.
Clustering refinement

Abstract Advanced validation of cluster analysis is expected to increase confidence and allow reliable implementations. In this work, we describe and test CluReAL, an algorithm for refining clustering irrespective of the method used in the first place. Moreover, we present ideograms that enable summarizing and properly interpreting problem spaces that have been clustered. The presented techniques are built on absolute cluster validity indices. Experiments cover a wide variety of scenarios and six of the most popular clustering techniques. Results show the potential of CluReAL for enhancing clustering and the suitability of ideograms to understand the context of the data through the lens of the cluster analysis. Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in unsupervised analysis.
Preventing Car Damage using CNN and Computer Vision

This research contain convolutional neural network are used to recognize whether a car on a given image is damage or not, from where it is damage and severity of the damage. Using transfer learning to take advantage of available models that are trained on a more general object recognition task, very satisfactory performance has been achieved, which indicate great opportunities of this approach. Car accidents are stressful and the auto claims process is ripe for disruption. Using computer vision to accurately classify vehicle damage and facilitate claims triage.
Python

Python is one of the preferred languages for data science in the industry primarily because of its simple syntax and the number of reusable machine learning/deep learning packages. These packages make it easy to develop data science products without getting bogged down with the internals of a particular algorithm or method. They have been written, debugged, and tested by the best experts in the field, as well as by a large supporting community of developers that contribute their time and expertise to maintain and improve them.
Placing Event-Action-based Visual Programming in the Process of Computer Science Education

Based on research results and experience, students who finish K-12 education lack the necessary computational thinking skills that they would need to continue their studies effectively in the field of computer sciences.Our goal was to examine the currently used methods and programming languages in K-12 education and to find and present an alternative approach.Using visual programming environments in education to develop students' computational thinking and algorithmic skills is a widespread practice in K-12 education.These environments mostly provide simplified versions of "real" programming languages.In this paper, we present event-action-based visual programming, as an alternative to today's most frequently used methods, which do not restrict the students' development ability to simplified and basic applications while retaining the advantages of visual languages.We organized four workshops in which we presented this programming approach to four distinct groups involved in education.The participants were guided to develop a multiplatform mobile application using Construct 2 event-action-based visual programming.At the end of the sessions we collected data in the form of group interviews and questionnaires on the possibilities of including event-action based visual programming in computer science education.Based on the results, the participants found the method suitable for beginner programmers to help them lay the foundations for more complex, textbased programming languages and to develop a positive attitude towards programming.
Exploring Data Security Scheme into Cloud using Encryption Algorithms

Cloud computing is come out as computing network throughout web. Cloud information pamper accumulating of the data within the cloud additionally has sharing qualifications amid manifold clients. Since malfunction of human being or hardware and constant software package blunder, cloud information is interrelated to information veracity. Numerous systems have been anticipated in order to tolerate equally the data proprietors as well as the community auditors to review cloud data truthfulness unmistakably devoid of salvage the intact data commencing the cloud servers. A third party inspector can carry out reliability inspection and also the distinctiveness of the signer on collective information which is held in reserve private from them. Throughout this work, exploration for auditing the truthfulness of public information surrounded by the cloud with imaginative client deletion whereas immobile protecting distinctiveness seclusion. This work have predisposition to additionally improve presented method; formerly any patron revolutionize the consequence from table then it have a predisposition to scrutiny that is import and repeatedly refurbish inventive value.
Image To Speech Synthesizer with Reference to Assamese Numerals

In this research work we have shown the methodology for converting printed Assamese numerals to its corresponding utterance. We have implemented as an initial effort which will read only four digit numerals. We are using Image processing techniques to convert an image of Assamese numerals into textual/digital form. In the second phase the numerals will be pronounced as a number by Google speaker. In this system, images are stored in a dataset and then inputted data is compared with the dataset image using template matching technique. After recognition of the text output will be displayed as a speech waveform. This work has many applications in today’s digital world.