Reviewerly/exHarmony huggingface dataset:
SAAS: Attack Detection and Prevention with Forensic in cloud Environment

Basically cloud computing security is essential nowadays, it has arisen as a technology to allow users as well as clients to access communications, data storage, software as well as exploitation Environment according to pay-as-what-use structure. Conventional digital forensic can’t be investigated due to some technical challenges like environmental as well as technical. The vibrant nature of cloud computing provides massive opportunities to identify malicious request using various security algorithms in cloud environment. Proposed research work identifies the current issues and provides solutions to reduce some challenges in the cloud environment. In this paper system proposed forensic investigation of cloud security for trusted and untrusted environments. System illustrated the various machine learning algorithms for eliminate the malicious request, and investigate the malicious user also. Proposed method generate the user log base snapshot during the active session and manual investigator can verify all logs and identify the malicious user. We offer a skilled technique for forensic examination in the cloud with the help of virtual machine (VM) and generate snapshots. The experimental analysis shows illustration of proposed security of system.
Plug-and-Play squeezing experiment on chip at telecom wavelength

We demonstrate a plug-and-play squeezing experiment entirely based on off-the-shelves guided-wave components and lithium-niobate integrated optics. We achieve −2 dB of squeezing thus validating our original approach as a ressource for real-world continuous-variable implementations.
The QoS analysis of web services composition based on Jackson queuing network

With the popularity of distributed computing and mobile internet, services composition has become a hot research in the field of service computing. The QoS of web services composition organised by BPEL is determined by the internal structure, the probability distribution in the variables value of BPEL process and the external factors such as the hardware and software processing capabilities of web servers and the load of web servers. At present, the generalised Petri nets, queuing Petri nets, Markov processes and stochastic process algebra model which are widely used and cannot have a comprehensive analysis in obtaining the actual performance of web services composition in internet environment. In this paper, a set of mapping rules are established to transform the internal and external factors which have impacts on the QoS of web services composition into Jackson queuing network, meanwhile, a set of performance indexes and their formula are given. Based on these performance indexes, we comprehensively analyse the performance of web services composition and locate the bottleneck of web services composition when the internal and external factors are changed so that, the performance of web services composition can be predicted before its deployment.
THE ANALYSIS OF PISA TEST 2018 ON STUDENT PERCEPTION OF READING OBJECTIVE, HOW IS INDONESIAN STUDENTS?

The purposes of the study are to know the students' perceptions of reading objective among students, which have above and below the average score of the OECD countries, and to know the Indonesian'student achievement among other countries.The study uses a quantitative method as a research method, and to determine the position of Indonesian students, it used M-Plus and Winsteps program.PISA 2018 data which has been released are used for research development purposes.The data were taken from questionnaires who were answered by 15 th -year-students of 80 countries.The data consist of 612.004 international students and 12.098 Indonesian students.The results of the Analysis of Variance states that there was a significant difference (P <0,000) of students' perceptions of reading objectives among students in countries which have above and below the average score of the OECD.While based on the analysis of Rasch Model, the reliability of Indonesian students was 0.36 while international data 0.51.Although they both are low, the reliability of Indonesian students is lower.The study concludes that the most students' states both (upper and below group) "Disagree" if the purpose of reading is only to fulfill obligations, merely hobbies, and spend time.Afterwards, students state "Agree" to discuss books with others and obtain the required information.
Transform Domain Image Watermarking using DCT, DWT and SVD

In the past of years of development of digital communication and network communication technology with increase in transferring documents all over the world, the authenticity and ownership of document is also important and challenging to control. To solve these problems so many techniques evolved which comes under transform domain and spatial domain based watermarking techniques. Most commonly used popular digital image watermarking techniques based on Transform Domain are like Discrete Cosine Transformation (DCT), Discrete Fourier Transformation (DFT), Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) transformations. But with lot of introduced techniques there must be a standard parameterized study required which compares them individually and explain their actual performance. In this paper, we implement watermarking using Transform Domain based techniques DCT, DWT and SVD and compare them practically on standard parameters like imperceptibility &amp; robustness with use of standard attacks. For the analysis of them we take standard images for implementation of digital image watermarking. All work done in Matlab platform. Experimental result shows the performance quality of DCT, DWT and SVD based watermarking techniques with standard quality parameters.
An Improved Frequent Pattern Mining Algorithm Based on TB-Tree and Tissue-Like P System

The purpose of frequent patterns mining is to find interesting items that appear together in a set of transactions, as an effective data mining technology, it has been widely used in many fields.Membrane computing is a new research direction of bio-inspired computing, which utilizes parallel and distributed computing models to solve problems effectively.In this paper, a new algorithm called ECTP, for mining frequent patterns, is proposed, which is based on TB-tree and evolution-communication tissue-like P system.The efficiency of the algorithm is improved by early pruning TB-tree by threshold and the parallelism of tissue-like P system.The result indicates that the algorithm is accurate and effective, gives some hints to use parallel operation principle mechanism in membrane computing systems.
Algoritma K-Means Untuk Klasterisasi Tugas Akhir Mahasiswa Berdasarkan Keahlian

School of Information and Computer Management (STMIK) Indonesia Padang is a private university under the auspices of the Higher Education Service Institution (LLDIKTI) Region X, producing graduates who are competent in the field of system analysts and database administrators. Requirements to meet undergraduate graduates (S1) final year students need to complete a final project or thesis. Final year students at STMIK Indonesia Padang often experience confusion in taking the final assignment topic. This is due to the fact that the final year students have not been able to direct their potential in determining the final assignment topic. In this case, researchers conducted the process of grouping final level students using the Data Mining K-means Clustering technique. The process of grouping final-level students is done by utilizing the data of course values ​​from the field mapping system analysts and database administrators. In this grouping two clusters will be produced, namely students taking the final assignment of system analysts and database administrator. So by using this K-means Clustering method, students have direction in taking the final assignment topic. The results obtained from 40 data samples used were students who took the topic of the final project system analysts as many as 20 students and students who took the final assignment of database administrators were 20 students.
MEDIA

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 57, Issue 8 p. 22857B-22857C Social and Cultural MEDIA First published: 28 September 2020 https://doi.org/10.1111/j.1467-825X.2020.09637.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume57, Issue8September 2020Pages 22857B-22857C RelatedInformation
Deep Convolution Neural Network Based Breast Cancer Bigdata Analysis for Crowd Cloud Sourcing

Breast cancer is one of the dangerous diseases leads fast death among women. Several kinds of cancers are affecting people, but breast cancer affects highly women. In medical industry removal of women breasts or major surgery is taken forward as the solution, where it reoccurs after surgery also. Only solution to save women from breast cancer is to identify and detect the earlier stage of cancer and provide necessary treatment. Hence various research works have been focused on finding good solution for diagnosing and classifying the cancer stages as benign, malignant or severe malignant. Still the accuracy of classification needs to be improved on complex breast cancer datasets. Few of the earlier research works have proposed machine learning algorithms, which are semiautomatic and accuracy is also not high. Thus, to provide a better solution this paper aimed to use one of the deep learning algorithms such as Convolution Neural Networks for diagnosing various kinds of breast cancer dataset. From the experimental results, it is obtained that the proposed deep learning algorithms outperforms than the other algorithms.
Collision Detection

Think of a video game without collision detection in some shape or form. It’s difficult! Collision detection is ubiquitous and part of many aspects that make up a game. Is the player standing on a platform or should they be falling? How can we prevent the player from stepping beyond the edge of the world map? Did the player get hit by a swirling hammer? Were we successful in picking up the shiny coins? All those things are decided by collision detection. This is what we will explore in this chapter. How can we detect if two objects collided with each other, and what should be done when those collisions happen? One other interesting use case for collision detection is to detect what game object a player clicked using the mouse or their finger.
K-Means Clustering - An Access to Overcome Uncertainty in Data Clustering of Distributed Networks

Clusteringisaprocedureofdividing a lot of information (or objects) into a lot of significant sub-classes, called bunches, help clients comprehend the characteristic gathering or structure in an informational index. Clustering has wide applications, in Economic Science (particularly statistical surveying), Document order, Recognition, Spatial Data Analysis and Image Processing Thewaytowardgatheringalotofphysicalor dynamic items into classes of same articlesis called grouping. A group is an accumulation of information questions that are near each other inside a similar bunch and are not atall like the articles in different groups. A bunch of information articles can be dealt with together as one gathering thus might be considered as a type of informationpressure. In spite of the fact that characterization is a compelling methods for recognizing gatherings or classes of items, it requires the frequently exorbitant naming of a huge arrangement of preparing tuples or examples, which the classifier uses to display each gathering. Clustering is likewise called information division in certain applications since clustering parcels substantial informational collections into gatherings as per their similitude. A decent clustering strategy will deliver top notch groups intra- class (that is, intra-group) comparability is high, the between class likeness is low and nature of a clustering result additionally relies upon closeness measure utilized by the technique and ,the nature of a clustering technique is likewise estimated by its capacity to find a few or the majority of the concealed examples ,nonetheless, target assessment is dangerously normally done by human/master examination. When all is said in done, the significant grouping techniques can be isolated into the accompanying classifications Partitioning strategies, various leveled techniques, Density-based strategies, Grid-based strategies, Model- basedtechniques.Thispaperutilizesfuzzycimplies grouping with PSO for clustering procedure of diabetic forecastdataset.
Cloud Computing Security Management using CSP

The cloud was defined by lots of experts, yet the NIST (National Institute of Standards and Technology) has presented the definition: “a model for enabling comfortable, on-demand network access to a shared pool of configurable computing resources The aim of this paper is a model for safe data sharing on cloud computing with intension to provide data confidentiality and access control over shared data, it also removes the burden of key management and files by users. The system also supports dynamic changes of membership and enables clients to reach the data they require even when the owner does not exist in the system. In the proposed system, a new security system is introduced, it provides a mechanism through which communication is safely achieved as well as it protects users and their hidden information from unauthorized users. The Entities in Proposed System consist of three parts: CSP, Users (owner ,clients ) and TPA , in this paper the focus will be on the CSP and the users. The proposed system are provides data confidentiality, access control of share data, removes the burden of key management and file encryption/decryption byusers, support dynamically of users membership. The use of a digital signature ensures the integrity and confidentiality of sharing data sent by users so that it cannot be read by the recipient TPA as it encrypts, sends a new encrypted signature and sends it to the CSP so that it cannot read its content CSP proved to be effective in the security of cloud computing.
Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Image Stitching is becoming more popular in field of computer vision because of rapid development of efficient algorithms that replaces the high cost wider lens cameras and commercial image stitching tools. The existing methods used global geometric transformation in registration stage and hence suffered from object deformation, parallax error, ghosting effect and motion blur in output result. In this paper, newly developed Hybrid Warping of weighted linearized homography matrix and similarity transform matrix is implemented over standard image stitching database. The visual quality of stitched image using proposed method has been examined in terms of performance metrics of Full Reference Image Quality Assessment (FRIQA) such as Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Reduced Reference Image Quality Assessment (RRIQA). Also, the performance analysis of proposed method is compared against existing image stitching methods in terms of field of view and stitching time. This analysis has ascertained the outperformance of Novel Hybrid Image Stitching method.
SecuCheck: Engineering configurable taint analysis for software developers

Welcome to the 21st IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2021)! We hope that you enjoy the conference and that it will inspire you in your future research. Should you require a certificate of participation please contact Alexander Serebrenik, the general chair of SCAM, at a.serebrenik@tue.nl. For questions related to the Morressier virtual platform please contact support@morressier.com.
Performance Optimization for Mobile Web

Abstract Most of the existing web pages have poor visibility, high bounce rates, and low conversion rates due to performance issues on mobile devices. The aim of this research work is to develop a framework which optimizes the performance of mobile web. The primary technology used in the framework is Google’s AMP (Accelerated Mobile Pages). The proposed framework converts a non-AMP web page into an AMP compliant web page, with additional feature of internationalization, and caches it on cloud for future reference. The proposed framework is tested on various content rich sites and performance of converted AMP pages is com- pared with original pages based on various parameters likes first meaningful paint, first interactive, etc.
PENGARUH GAYA HIDUP DAN MOTIVASI TERHADAP KEPUTUSAN MENJADI MAHASISWA

The purpose of this study was to determine the correlation between motivation and lifestyle on the decision to become a student at a university. The research method used is a quantitative method with a collection technique in the form of distributing questionnaires. The results of the study found that there was a positive and significant influence between lifestyle and motivation on the decision to become a student.
Advances in Motion Detection and Tracking of Human as a Target

Motion estimation of a target is the major area with higher computational complexity in video processing. It is the progression of discovery the motion patterns that describe the transformation from one frame to another in a sequence of video. Therefore, it is reasonable to carry out motion estimation only where movement is present. Image data in an image series remains mostly the same between frames along the target motion. To make use of the image statistics redundancy in image sequences, there is a need to guess motion. Motion estimation is valid for video compression improvement, stereo correspondence, object tracking and finding optical flow. Many precise methods have been proposed in the framework of one or more of these applications. Most motion estimation algorithms either operate directly in the image domain or finding the similar metric that measures how alike two pixels or two patches of pixels. In this paper, a review of a variety of motion estimation technique is presented.
A Novel System for Early Detection of Breast Cancer using Area and Entropy Features of Malignant Tumor

Computer aided detection and classification system has been developed to detect breast cancer at an early stage by predicting the area and texture of malignant tumours. Noise removal and image enhancement is carried out in the pre-processing stage by using adaptive median filter and contrast limited histogram equalisation techniques. Improved watershed segmentation technique with appropriate internal and external markers, have proved to be an efficient approach in detecting the region of interest. The detected tumours are classified using feedforward artificial neural network that are trained using textural features. Area and entropy features extracted from malignant tumours aids in early detection of breast cancer by categorising malignant tumours as belonging to stage I or stage II. The overall efficiency of the system, for identifying stages of malignant tumour is 92%, which has been identified to be high when compared to all existing systems. Mammogram images from Mammographic Image Analysis Society (MIAS) database was used for training the system and efficiency of the system was tested using real time hospital images.
Isolated Telugu Speech Recognition On T-DSCC And DNN Techniques

Communication is the major path to convey the information. Speech is the best mode for conveying the information. Human to human information can be exchanged through some particular language. But the interaction between human and machine is the major challenge which deals with ASR (Automatic speech recognition). This research recognizes speaker independent data which gives good results by using T-DSCC (Teager energy operator delta spectral cepstral coefficients) feature extraction technique and DNN (Deep Neural Networks) feature classification technique. This paper also uses CASA technique for pre-processing the speech signals. This research is done by creating the database for 10 most speak able isolated words in Telugu.
Comparison of Priority Areas and Rehabilitation Risk Areas for Post Disaster by K-Means Method

Among the inhibiting factors for rehabilitation in Padang City is the absence of an assessment of priority areas and rehabilitation risk areas. This study aims to classify these factors into three clusters and the method used in this study was K-Means method. Disaster average data from 2017 until 2019 as well as data on rehabilitation efforts are used in this method. The results achieved indicate that the rehabilitation efforts carried out have not been evenly distributed in the areas prioritized for rehabilitation. This result can also be an input for the Regional Disaster Management Agency of Padang City in mapping and rehabilitating post-disaster areas and evaluating previous rehabilitation efforts.&#x0D; Keywords: cluster, k-means, priority, rehabilitation, risk
New approaches to the professional development of specialists of the bodies and institutions of the penal system: experience and opportunities

The article analyzes the relevance of the problem of professional development of specialists of the penal system in the field of educating juvenile convicts in the conditions of the penal system; emphasis is placed on professional deficiencies and manifestations of the employee's subjective stand when working with juvenile convicts in a pre-trial detention center or an educational colony. The subject of the article is an analysis of the main documents of educational policy in the field of upbringing of juveniles, the results of a control snapshot of the organization of educational work in colo- nies, as well as a quantitative analysis of additional professional training programs for employees implemented in the penitentiary system. The purpose of the study is to substantiate the importance of updating the content of training for prison staff, showing the inadequacy of educational programs on the education and socialization of juvenile convicts. The methodological basis of the research was formed by the analysis, synthesis and formal-logical methods. The value and practical significance of the study lies in identifying a list of issues that are relevant for inclusion in additional professional training programs when training an employee of the penitentiary system; in focusing attention on ensuring the formation of universal pedagogical actions of an employee working with juveniles. The work makes a conclusion on the need to search for modern approaches to improving the qualifications of pedagogical personnel of the penal system when working with juveniles in educational colonies and pre-trial detention centers. Key words: juvenile convicts, education, staff, professional development, subject’s stand, training programs for penitentiary institutions, universal pedagogical actions.
Gradient iterative-based kernel method for exponential autoregressive models

Two kernel method-based gradient iterative algorithms are proposed for exponential autoregressive (ExpAR) models in this study. A polynomial kernel function is utilised to transform the ExpAR model into a linear-parameter model. Since the order of the linear-parameter model is large, a momentum stochastic gradient algorithm and an adaptive step-length gradient iterative algorithm are developed. Both these two algorithms can estimate the parameters with less computational efforts. Finally, a simulation example shows that the proposed algorithms are effective.