Reviewerly/exHarmony huggingface dataset:
Data dependent web service selection

Data mining applications and services are becoming increasingly important, especially in this age of Big Data. QoS (Quality of Service) properties such as latency, reliability, response time of such services can vary based on the characteristics of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking these types of services since they do not consider these dataset characteristics. We have proposed a service selection methodology to predict the QoS values for data analytic services based on the attributes of the dataset involved by incorporating a meta-learning approach. Subsequently we rank the services according to the predicted QoS values. The outcome of our experiments proves the effectiveness of this approach with an improvement of above 20% in service ranking when compared to the traditional QoS selection approach.
GESER IN ENGLISH: A SHORT HISTORY OF TRANSLATION

In 1995 to mark the thousand-year anniversary of the Buryat Geser, the Kray Gesera (Land of Geser) ethnographic park was opened in the Okinskiy region of Buryatia.At the opening ceremony, the American Daniel
A Hybrid Multiresolution Texture Segmentation and its Application to Ikonos Satellite Imagery

In this thesis a method for segmenting textured images using Gabor filters is presented. One of the most recent approaches for texture segmentation and analysis is multi-channel filtering. There are several applicable choices as filter banks which are used for textured images. Gaussian filters modulated by exponential or by sinusoidal filters, known as Gabor filters, have been proven to be very usefyl for texture analysis for the images containing specific frequency and orientation characteristics. Resembling the human visual cortical cells, Gabor function is a popular sub-band filter for multi-channel decompositon. Optimum joint spatial/spatial frequency uncertainty principle and its ability to recognize and pass specific frequencies and orientations are attributes of Gabor filter that make it more attractive. Gabor function with these attributes could simulate the task of simple visual cells in the cortex. Gabor function has several parameters that determine the sub-band Gabor filter and must be determined accurately to extract the features precisely for texture discrimination. A wide selection range for each parameter exists and many combinations of these parameters are possible. Accurate selection and combination of values for the parameters are of crucial importance. Hence a difficult goal is minimizing the number of filters. On the other hand a variety of approaches of texture analysis and recognition have been presented in remote sensing applications, including land cover/land use classification and urban scene segmentation. With the avaiability of very high-resolution commercial satellite imagery such as IKONOS, it is possible to obtain detailed information on urban land use and change detection that are of particular interest to urban and regional planners. In this thesis considering the attributes of human visual system, a hybrid algorithm is implemented using multi-channel decomposition by Gabor filter bank for feature extraction in conjunction with Artificial Neural Networks for both feature reduction and texture segmentation. Three approaches are implemented to optimize Gabor filter bank for image segmentation. Eventually the proposed method is successfully applied for segmentation of IKONOS satellite images.
An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)

Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely.
Embedded Video Surveillance Based Moving Object Detection and Tracking

Background reckoning and the foreground, play prominent roles in the tasks of visual detection and tracking of objects. Moving Object Detection has been widely used in sundry discipline such as intelligent systems, security systems, video monitoring systems, banking places, provisionary systems, and so on. In this paper proposes moving objects detection and tracking method based on Embedded Video Surveillance. The method is based on using lines computed by a gradient-based optical flow and an edge detector gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for tracking objects using this feature. The proposed method is compared with a recent work, proving its superior performance and when we want to represent high quality videos and images with, lower bit rate, and also suitable for real-world live video applications. This method reduces influences of foreground objects to the background model. The simulation results show that the background image can be obtained precisely and the moving objects recognition is achieved effectively.
Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika

Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.
Handling IDN Homograph Attack using Facial Expression Password

With the advancement of technology and its applications in diverse areas, people are able to get access of the desired information, exchange ideas and connect with the world. But one of the areas of concern is that user shares personal information for example images, videos, sharing location etc. on various social networking websites very frequently. Another important aspect is sharing financial information for various activities such as payments and purchases which is misused using phishing attacks. There are different variety of attacks that one may come across. The objective of this paper is to make people aware of the increasing fishing attacks such as IDN and how using latest authentication technique of Facial Recognition system one record various facial expressions to secure himself from such attacks since only the user is aware what facial expression he/she has recorded for a particular website.
Active Mesh Based Motion Compensation Algorithm for Video Compression in Wavelet Sub Bands

The method of assessing the motion vectors is termed as Motion Estimation and consequently reconstructing the original frame using these motion vectors is termed as Motion Compensation. In this paper a new active mesh based Motion estimation and compensation algorithm in wavelet domain at subpixel level is proposed. To achieve the accuracy in estimating the motion vector from reference frame to predicted frame in sub pixel level a Wavelet based Counterlet transform is used. This new mesh based method is based by minimizing the mesh energy with energy functions as its input coefficients. The accuracy of the estimation and the compensation is improved by the feature points obtained by the energy functions. The analysis of the experimental results describes that there is improvement in the distortion of the performance rate for the video compression and this is achieved by the generation of the structured and the unstructured active mesh motion techniques in the wavelet sub-bands. The energy criteria of the input coefficients helps to retain the energy in the sub-bands which is a condition that helps in significant gaining of the PSNR required for better outcome.
Proposition of a dynamic steganography system based on the chaos properties and image registration for distributing an encryption key

Keys have a crucial role in the security of cryptosystems and their distribution is an important primitive in cryptography. This article presents a new dynamic system of steganography (data hiding) for concealing data in a particular sequence of images intended to regulate in an unlimited time the secure transfer of the key between the different correspondents; the proposed key exchange protocol using an image registration system and a chaotic sequence is comparable to the Diffie-Hellman key exchange protocol. The use of chaos makes it possible to increase the level of confidentiality by sending to the receiver a register which contains parameters of a chaotic sequence; knowing these parameters, the receiver can extract the chaotic sequence. The purpose of using the image registration is to find the transformation between two or several images; these transformations become a tool with the chaotic sequence calculated to recover the key on the receiver side.
Tingkat Korelasi Prestasi Akademik Terhadap Siswa SMP Menggunakan Metode Backpropagation

Student academic achievement plays a very important role in determining the quality of a school. Student grades sometimes change every semester, there are increases and decreases. There is an assumption that students whose grades are in the previous semester will be in the next semester and vice versa. is expected to make it easier for us as educators to see the extent to which changes in student academic achievement. The data tested is the data of 60 class VII students in 2 semesters. Furthermore, it will be tested using the MatLab Application, then the results of the changes that occur will come out. The results of this study found that they did not know the value of semester 1 with the value of semester 2 TP. 2019/2020 is very good with an architectural pattern of 10-10-1 with a value of 95.3%. So students who excel in semester 1 are likely to excel in the following semester. So that it can help the school in seeing the Correlation Level of Student Academic Achievement at SMPN 3 Lengayang.
The Effect of Best First Search Optimization on Credit Card Fraudulent Transaction Detection

In the digital world, Recently growth of online shopping site for purchasing clothes, electronic items, glossary etc and online transaction for transfer money is increasing day by day . At the same time, criminals have become able to doing fault and earning money through wrong ways .that’s why fraud grows. With the development of Machine Learning in the field of Computer Science and Engineering, its application in the different domain also in fields like Medical, Marketing, Telecommunication, finance, etc. The reason for the popularity of Machine Learning in these domains is due to its high accuracy prediction. That’s why over many years, machine learning has been used in fraud detection. With the advancement of technology in online transactions, fraud is the greatest issue for businesses and has become difficult to recognize than the traditional form of this crime. Historically, the area of Fraud Detection is interrelated to Data Mining &amp; Text Mining. Due to the sudden growth of fraud whose outcome is loss of trillions of rupees worldwide every year, various modern techniques in detecting fraud were proposed that are progressed without interruption and applied to many business fields. Bank frauds worth ₹2.05 trillion happened in the last 11 years, among which there were overall 53,334 fraud issues in the usage of RBI data. The principle purpose behind this write up is to review different methods in identifying frauds corresponding to the unusualness in the transactions. The supervised and unsupervised machine learning algorithms will be used to identify fraud and the best first search optimization will be analyzed to compare both results, i.e., before and after optimization.
QoS-based data analytic service selection: a comparative study of different learning models

QoS-based web service selection has been studied in the service computing community for some time; however, data characteristics are not considered. In this work, we have studied the use of different machine learning algorithms as meta-learners in predicting the performance of data analytic services for the given dataset. We used a meta-learning algorithm to incorporate meta-features in the selection process and we used clustering services as an example of data analytic services. We have also investigated the impact of the number of data features on the performance of the meta-learners. We found that, out of the 5 classification models, SVM showed the best results in predicting the recommended service for the given dataset with an accuracy of 78%. When it comes to regression models, MLP was the best regressor. We recommend considering only simple meta-features that can be collected for most datasets, as those proved to be sufficient to achieve good prediction accuracy.
Design and Development of Efficient Multipath TCP using GMM clustering for Big Data in Public Cloud Data Center

Currently new topologies have been introduced in many data centers that provide location independence and larger aggregate bandwidth by making different multiple paths in the network core. This work proposes transport of data from TCP (Transmission control Protocol) to multi-path TCP (MPTCP) for maximum utilization of paths over network flow. In spite of its added advantages, some sort of work on MPTCP to be carried out on cloud environment and further, efficient way of using MPTCP on real-world cloud application still looks like unclear problem. Further, the work also concerned on MPTCP usage in most effective and feasible way for cloud and data center environments over various conditions on network. The experiment is conducted by clustering the public cloud data using Gaussian Mixture Model (GMM) based Expectation and Maximization (EM) algorithm and communicated over a network using MPTCP. The results shows that the proposed method yields high-speed data transfer and low communication delay when compare to traditional TCP technique.
Analysis of Outlier Detection on Structured Data

Outlier detection has played an important role in all research areas for data analysis in various domains.Outlier is involved according to human error, sensors or mechanical faults and environment.It is detected to get data quality for all applications.On the other hand the good outliers can occur by chance in new contributions in research .The aim of this study is to discover new trend with outlier in dataset.The problem statement is how to detect the outlier analysis based on different datasets between before and after removing outlier.In this system, a box and whisker plot and the robust J48 algorithm are applied for outlier detection and classification.
Automatic classification of the emotional content of web documents

This thesis proposes automatic classification of the emotional content of web documents using Natural Language Processing (NLP) algorithms. We used online articles and general documents to verify the performance of the algorithm, such as general web pages and news articles. The experiments used sentiment analysis that extracts sentiment of web documents. We used unigram and bigram approaches that are known as special types of N-gram, where N=1 and N=2, respectively. The unigram model analyses the probability to hit each word in the corpus independently; however, the bigram model analyses the probability of a word occurring depending on the previous word. Our results show that the unigram model has a better performance compared to the bigram model in terms of automatic classification of the emotional content of web documents.
Secure Data Deduplication in Cloud Environments

With the tremendous growth of available digital data, the use of Cloud Service Providers (CSPs) are gaining more popularity, since these types of services promise to provide convenient and efficient storage services to end-users by taking advantage of a new set of benefits and savings offered by cloud technologies in terms of computational, storage, bandwidth, and transmission costs. In order to achieve savings in storage, CSPs often employ data dedplication techniques to eliminate duplicated data. However, benefits gained through these techniques have to balanced against users' privacy concerns, as these techniques typically require full access to data. In this thesis, we propose solutions for different data types (text, image and video) for secure data deduplication in cloud environments. Our schemes allow users to upload their data in a secure and efficient manner such that neither a semi-honest CSP nor a malicious user can access or compromise the security of the data. We use different image and video processing techniques, such as data compression, in order to further improve the efficiency of our proposed schemes. The security of the deduplication schemes is provided by applying suitable encryption schemes and error correcting codes. Moreover, we propose proof of storage protocols including Proof of Retrievability (POR) and Proof of Ownership (POW) so that users of cloud storage services are able to ensure that their data has been saved in the cloud without tampering or manipulation. Experimental results are provided to validate the effectiveness of the proposed schemes.
Design and Development of Various Cloud Computing Architectures Improving the Security

numerous engineers have deliberate their very own format for introducing the distributed computing basis. The modern dispensed computing designs contrasts from severa points of view. protection has been the massive problem as patron related statistics and handling is embraced utilizing the framework gave by way of using outsider professional co-ops fluctuates especially. it's far vital to understand the degree of incorporation of security into the distributed computing framework and in some time discover the excellent layout that contains satisfactory and tight protection framework. in this examination paper, a correlation of present models from the factor of view of attention of protection foundation internal dispensed computing framework is added along an intensive engineering that is included with each a part of protection considering
DISTANCE LEARNING TECHNOLOGY FOR THE DEVELOPMENT OF INFORMATION LITERACY SKILLS OF ADULT LEARNERS

The given study focuses on the specific of adult learning. The aim of our research is the following: with the help of a distant course to improve the level of media literacy, critical thinking and build psychological resilience to manipulative impact and the fraudulent attempts of social engineers. We demonstrated that despite the obstacles caused by the lack of face-to-face contact, online courses could be used as an alternative form of studying. To our research we use the following methods: questionnaires, information collection, and statistical data processing. For the hypothesis verification, we use the following methods The course consists of four lessons and includes topics about information checking, developing critical thinking, and building psychological resilience to dishonest media content and resisting fraudulent actions of social engineers. We conducted a survey before and after the training, the results of which demonstrated that students significantly improved their levels of media and cyber literacy after attending online classes (21%). Our approach to an organization and holding an online study allowed us to keep participants interested and didn’t turn our lessons into boring lectures. Thus, in the results, we succeeded to achieve the aim. Moreover, we saw, that distance learning can be an effective tool for adult learning if we take into consideration the specific features of an online format. The example of the given training showed that, if necessary, online learning can be employed as an alternative and quite an effective tool for providing knowledge and skills.
Security Of Connected Objects

The expansion of connected objects continues to create privacy issues for people and becomes a target for attacks on information systems.Faced with this threat, the security measures to adopt are still at the heart of the debate.They consist in providing better control of risks within information systems by answering certain issues that take into account availability, integrity, confidentiality and traceability.Many are still indifferent about the consequences this has on information systems.In this work we proposed to show on the one hand, the impact of connected objects on information systems and on the other hand proposed the security policy set for these objects.Thus, intrusion tests on connected objects are implemented in order to identify the vulnerabilities and the consequences of these on information systems.The connected objects studied in our case present vulnerabilities that are exploitable.These flaws impact the security of information systems.We then became interested in the security policy implemented to improve the security of connected objects.