Reviewerly/exHarmony huggingface dataset:
Improved low-cost recognition system for handwritten Bengali numerals

Handwriting recognition is very important due to its numerous potential applications. This paper is concerned about the low-cost features extraction for the development of an improved Bengali handwritten numeral recognition system. Each numeral was first resampled to a binary image of fixed size. A set of new features based on shape analysis was derived from the resampled image, and a multilayer neural network was trained using the extracted features. The recognition accuracy of the developed system was tested on both training and test sets of a publicly available Bengali handwritten numerals database at three different resolutions. Besides accuracy, the reliability of the system was also estimated using Cohen's kappa. The highest accuracy, 99.12% with reliability about 99%, was obtained for the test database at resolution of 32×32. The use of PCA reduces feature dimension from 142 to 68 resulting in a slight reduction in accuracy to 98.80%.
Image Forgery Detection by using Machine Learning

Dense local descriptors and AI havebeen utilized with achievement in a couple of employments, as classificationof surfaces, steganalysis, and bowing zone. We build up a newimage counterfeit marker creating unequivocal descriptors recentlyproposed in the steganalysis field reasonably joining some of suchdescriptors, and redesigning a SVM classifier on the availabletraining set. The issue with the present making is that majorityof them see certain highlights in pictures changed by a particular tamperingmethod, (for example, duplicate move, joining, and so forth). This proposes the structure does notwork always transversely over different evolving frameworks. Mix of no under two pictures to make a completely phony picture is known as Image structure. It winds up being difficult to disengage between certified picture and phony picture in light of the closeness of different astounding changing programming endeavors. In this paper, we propose a two phase imperative altering way to deal with oversee direct learn featuresin referencing to see changed pictures in various pictureformats
Generation of general system parameters for Rainbow electronic signature scheme for 384 and 512 security bits

Today, there is rapid progress in the creation of quantum computers to solve various computational problems and for different purposes. At the same time, special efforts are made to create such a quantum computer that can solve the problems of cryptanalysis of existing cryptosystems: asymmetric ciphers, key encapsulation protocols, electronic signatures, etc. Prevention of such threats can be achieved by developing cryptographic systems that will be protected against both quantum and classical attacks, and be able to interact with existing protocols and communication networks. There is also a significant need for protection against attacks by side channels. Currently, significant efforts of cryptologists are focused on the NIST PQC open competition. The main idea of the NIST PQC competition is to define mathematical methods based on which standards for asymmetric cryptotransformations, primarily electronic signatures, as well as asymmetric ciphers and key encapsulation protocols can be developed. Three electronic signature schemes – Crystals-Dilithium, Falcon and Rainbow become the finalists of the third stage of the NIST PQC competition according to the results of the second stage. The first two are based on the mathematics of algebraic lattices, and Rainbow is based on multivariate transformations. Currently, a comprehensive analysis of the finalists is an important task for the entire global crypto community. The vast majority of schemes that have become finalists or alternative algorithms are based on problems in the theory of algebraic lattices. Special attention was also paid to the Rainbow electronic signature scheme based on multivariate transformations. The purpose of this work consists in a preliminary analysis of existing attacks on promising electronic signature Rainbow, definition of requirements to the system-wide parameters to ensure cryptographic stability of at least 512 bits against classical and 256 bits against quantum cryptanalysis, as well as development and practical implementation of Rainbow algorithms for generating system-wide parameters for 512 bits against classical and 256 bits against quantum cryptanalysis.
Analysis of the Big Data Methods, Challenges and Applications in Intelligent Transportation Systems

Big Data is data that grows beyond the capability of traditional data mining methods.A big voluminous, heterogeneous, and speediness of data generated from numerous sensors attached near roadside infrastructure and devices associated with the vehicles.We need to design Big data models for highly scalable, techniques for complex data types, robust to noise, insensitive to the input order, ability to handling high-dimensional data, interpretability, and usability.There are many challenges in enormous amount of data to stockpile, processing, and operate.Apache Spark, Hadoop, and Flink are the platforms of Big Data Analytics.Massive information processing in transportation systems facing difficulties in terms of data privacy, security, data storage, and data quality.Various Big Transportation data analytics are performed, such as video and audio analytics, image analytics, and historical analytics.The globe is monitored and managed with Web of Devices (WoD) efficiently and effectively.
ROS Based Autonomous Robot with Open CV

In this project, we have designed and developed an autonomous robot that is powered by Robot Operating System (ROS). The capabilities of the robot include autonomous navigation, image tracking and mapping. OpenCV has been implemented in the on-board microprocessor to process the images that are captured by the general purpose webcams on the robot. A microcontroller has also been used to control the motors. The ultimate aim of this project is to develop a mobile robot capable of making its own decisions based on the images received.
Scalable Video Streaming over Wireless Access iNetworks

Cloud transmission services provide a capable, flexible, and scalable a series of calculations method and offer an elucidation for the user demands of high quality and range of hypermedia system. Generally speaking, accessing hypermedia system video services through a large system is no longer a problem. The major video platforms, such as You tube and Amazon, have good management styles and provide users to share using more than one medium videos easily with enlarge services.
Group-Key Generation through Ternary Tree Based Rebuild Algorithm

Security plays essential role in any correspondence framework particularly in Group oriented correspondence. In Group oriented correspondence, entire correspondence will occur with the help of one secret key which is called Group Key. This gathering key must be changed at whatever point another part joins into the gathering just as a current part leaves from the gathering; which is known as rekeying. This gathering correspondence can be spoken to utilizing key tree. In case we utilize ternary tree, tallness of the tree will be expanded if the group members are more, so that rekeying activity takes additional time. Rather than ternary tree on the off chance that we use quad tree, so that the tree stature will be less, so that rekeying activity takes less time. So the correspondence will be increasingly secure and quicker. In this paper we are going to implement Ternary tree based Rebuild Algorithm.
Data Denoising By Noise Invalidation

In this thesis, the problem of data denoising is studied, and two new denoising approaches are proposed. Using statistical properties of the additive noise, the methods provide adaptive data-dependent soft thresholding techniques to remove the additive noise. The proposed methods, Point-wise Noise Invlaidating Soft Thresholding (PNIST) and Accumulative Noise Invalidation Soft Thresholding (ANIST), are based on Noise Invalidation. The invalidation exploits basic properties of the additive noise in order to remove the noise effects as much as possible. There are similarities and differences between ANIST and PNIST. While PNIST performs better in the case of additive white Gaussian noise, ANIST can be used with both Gaussian and non Gaussian additive noise. As part of a data denoising technique, a new noise variance estimation is also proposed. The thresholds proposed by NIST approaches are comparable to the shrinkage methods, and our simulation results promise that the new methods can outperform the existing approaches in various applications. We also explore the area of image denoising as one of the main applications of data denoising and extend the proposed approaches to two dimensional applications. Simulations show that the proposed methods outperform common shrinkage methods and are comparable to the famous BayesShrink method in terms of Mean Square Error and visual quality.
Socio monitoring framework (SMF): Efficient sentiment analysis through informal and native terms

Prediction and analysis of public expression is the trending topic of the current research arena.Opinion mining (a.k.a.Sentiment Analysis) is the automated orientation of public sentiments, views, suggestions, and opinions.It assists in estimating the popularity of products, events, services, and even political policies via user-generated content.Machine learning based supervised, semi-supervised, and unsupervised lexicon oriented techniques are applicable in the semantic orientation of public opinions about numerous real world entities.It is observed that socio channels contain real-time contents, which sometimes face the intricacy of informality, Slangs, Vernacular (Native terms), and sarcasm; however, these indicators provide high visibility of sentiments and opinions in terms of orientation.Unfortunately, the unclear perceptiveness of such contents lack in optimized orientation, and supervised machine learning systems are inappropriate where the Lexicon based opinion mining methods are preferred over learning based ones when training data is not adequate.In this paper, we seek to improve the performance of lexicon-based sentiment analysis by incorporating novel linguistic features such as vernaculars, slangs, and sarcasm for monitoring the social media contents up to a more realistic level.The core contributions are sarcasm detection and identification of vernacular terms.The performance of the proposed unsupervised lexicon-based framework over native, informal, and sarcastic opinion bearing terms is assessed via numerous experiments.For this, we utilized tweets relevant to two key domains, including Product and Politics.Experimental outcomes revealed that the proposed system outperformed the existing supervised and semi-supervised systems as 84.24%, and 82.35% of accuracies are achieved over informal and sarcastic contents for product and politics domains, respectively.The average accuracy for both domains is 83.29%.
Clustering student Instagram accounts using author-topic model

This study proposes topic model to cluster a group of high school teenager's Instagram account in Surabaya, Indonesia by using the author-topic models method. We collect valid 235 Instagram accounts (133 female, 102 male students). We gather a total of 3,346 captions of Instagram posts from 18 senior high schools. We find topics that define their Instagram's post or caption; these seven topics are namely: feeling, Surabaya events, photography, artists, vacation, religion and music. Through the process, the lowest perplexity come from 90 iterations, which suggests six groups of topics. The six topics are concluded based on the lowest perplexity value and labelled according to the words included in the topic. The topic of photography discussed by six schools. Photography, artists and vacation are discussed by three schools, while feeling and religion and music are being discussed by two and one school respectively.
Block-Chain Technology -Security, Platforms

The current paper is focused on its security issues with its prospective applications for educational applications and explored how blockchain Technology can be used to solve some education problems.Blockchain Technology can be integrated into multiple areas like cryptocurrencies,smart contract ,Bank,Blockcahin with video games,finance,judiciary , commerce and IOT.Blockchain Technology has already changed peoples lifestyle in many areas due to its great influence. In this paper the Three types of Blockchain engineering 1.0,2.0,3.0 with its protocol stack layer working ,its advantages and applications are checked .Along with different platforms on which Blokchain is running with its features is highlighted and evolution phases in Blockchain .This paper aim is to test amenities of security,related to the Blockchain and need of innovation of Blockchain.
QoS-based semantic web service selection

This thesis discusses the dynamic web service selection in the semantic context with QoS constraints. The goal of this work is to investigate the mechanism of automated QoS-based semantic web service selection. Semantic Web Service (SWS) aims to achieve the automation of web service tasks, such as service discover, selection, composition and invocation. The task of semantic web service selection is further investigated through this thesis. An architecture is proposed to achieve this task by considering QoS parameters. The QoS parameters are classified into dynamic and static attributes in the architecture. The dynamic attributes are evaluated and measured as an overall value by applying utility functions. This overall value can be modeled in the semantic context for the purpose of service selection. Furthermore, the architecture directly models the static QoS attributes in the semantic context for service selection. Finally, an open SWS challenge scenario named hardware purchasing is used in several experiments in order to evaluate the proposed architecture.
LOCAL KNOWLEDGE ARTICLES MAPPING IN UNIVERSITAS PADJADJARAN JOURNALS

This research discusses the bibliometric mapping of local knowledge articles in Universitas Padjadjaran through the journal Universitas Padjadjaran using VOSviewer software. The method used in this research is quantitative with bibliometric analysis using co-words. The database used as research material is 503 local knowledge articles with various search keywords. The results of this research are the development of the number of local knowledge articles in Universitas Padjadjaran which has increased since 2001-2020.West Java is the region that has the highest number of local knowledge articles. The most popular topics are culture and local wisdom.
Measuring Security for Applications Hosted in Cloud

Despite the numerous benefits of cloud computing, concerns around security, trust and privacy are holding back the cloud adoption. Lack of visibility and tangible measurement of the security posture of any cloud hosted application is a disadvantage to cloud service customers. Decision to migrate workloads on the Cloud requires thoughtful analysis about security implications and ability to measure the security controls after hosting. In this paper, we propose a framework to quantitatively measure different aspects of information security for Cloud applications. This framework has a system through which we can define applications specific controls, gather information on control implementation, calculate the security levels for applications and present them to stakeholders through dashboards. Framework also includes detailed method to quantify the security of a Cloud application considering different aspects of security, control criticalities, stakeholder responsibilities and cloud service models. System and method provide visibility to Cloud customer on the security posture of their cloud hosted applications.
Improved Image Denoising with the Integrated Model of Gaussian filter and Neighshrink SURE

Image denoising, being an important preprocessing stage in image processing, minimizes the noise interfering with the information content of the image. The denoising problems are addressed by various techniques starting from the Fourier transforms to wavelets. Because of the localized time frequency features and advantages of multi resolution capabilities, the wavelets have been extensively used in the denoising process. The development of algorithms for the wavelet thresholding or shrinkage strategies along with different filters have resulted in the betterment of image quality after the denoising. Even though the image denoising algorithm based on a combination of Gaussian and Bilateral filters, shows good performance but lacks in consistency with respect to the noise levels and also the type of images used. This paper discusses the advantages of NeighShrink SURE rule in developing an effective thresholding strategy, thereby proposing a improved denoising method incorporating the NeighShrink SURE rule along with combination of Gaussian filter model. The methodology employs the use of subband thresholding derived from the NeighShrink SURE rule. The outcome of the proposed method exhibits a comparatively improved performance in Peak Signal to Ratio (PSNR) and Image Quality Index (IQI) values of the test images.
Food Supply Chain Traceability using Block Chain

In this paper, the main idea is to have a transparent food supply chain by the use of blockchain technology. The architecture uses an authentication protocol which is similar to the protocol involved in the crypto currency technology. For this feature to be available in all the products a radio frequency identification (RFID)-based sensor was used. The architecture is built by having the cyber layer having the Blockchain and RFID sensor in the physical layer. The main idea of RFID is to provide real time monitoring of data by the use of a unique identifier. For a food supply chain to be transparent, every product has to have a proper link to the concerned origin and the status of the products. In order to achieve this blockchain provides a highly secured digital database for each of the food products. In order to prevent cyber-attacks different security analysis was being conducted at different architectural stages.
Similarity Detection in Large Volume Data using Machine Learning Techniques

When unauthorized copying or stealing of intellectual properties of others happen, it is called plagiarism. Two main approaches are used to counter this problem – external plagiarism detection and intrinsic plagiarism detection. External algorithms compare a suspicious file with numerous sources whereas intrinsic algorithms are allowed to solely inspect the suspicious file in order to predict plagiarism. In this work, the area chosen for detecting plagiarism is with programs or source code files. Copying the entire source code or logic used in a particular program without permissions or copyright is the stealing that happens in the case of source codes. There exist many ways to detect plagiarism in source code files. To perform plagiarism checking for a large dataset, the computational cost is very high and moreover it’s a time consuming job. To achieve a computationally efficient similarity detection in source code files, the Hadoop framework is used where parallel computation is possible for large datasets. But the raw data available to us is not in a suitable form for the existing plagiarism checking tools to work with, as their size is too high and they possess features of big data. Thus a qualifying model is required for the dataset, to be fed into Hadoop so that it could efficiently process them to check for plagiarism in source codes. To generate such a model, machine learning is used which incorporates big data with machine learning.
Round Trip Latency Based Authentication Scheme in Fog-Enabled Cloud Computing System

In fog-enabled cloud computing framework, one of the most challenges is security promises due to the compromised passwords. To overcome this issue, different multifactor authentication schemes have been developed that requires additional authentication credentials along with the standard password to authenticate the login. Among those schemes, Communication Latency-based Authentication Scheme (CLAS) increases the protection of conventional web authentication schemes by leveraging the Round Trip network communication Latency (RTL) between clients and authenticators together with standard password. It uses RTL of clients to secure against password compromise. On the other hand, it can support compromise of either the password or the profiled location of a user. This makes it susceptible to same location attacks. As a result, an integration of additional profiling features is needed to attain more robust and flexible defense against password compromise. Hence in this paper, an extended CLAS is proposed that mainly investigates the mobility and same location challenges in CLAS. Initially, the legitimate login failures are solved by handling both selective and arbitrary mobility of users. For selective mobility case, CLAS generates an individual profile for each location and the user may be granted access if his/her real-time login profile matches any of the stored reference profiles. For arbitrary mobility case, CLAS is integrated with two-factor authentication mechanism to authenticate the user. In addition, the defense against Mimic attacks is improved by utility metric-based location anonymization and obfuscation of RTL algorithms. By using these algorithms, the user’s locations are anonymized and the values of RTL are obfuscated to defend against user compromise attempts for impersonating the RTL by getting nearer to the user location. Moreover, a keystroke dynamics measure is introduced with obfuscated RTL measures to effectively defend the same location attacks. This technique alleviates the potential impacts of network instabilities on RTL measurements. As well, it increases the authentication sample space and so improves the security guarantee of CLAS. Finally, the simulation outcomes illustrate that an extended CLAS has the ability to reduce both false positive and false negative rates.
Analisis Hambatan Pembelajaran Bahasa Indonesia di Sekolah Dasar

Learning Indonesian in elementary school can help students to get to know themselves, their culture and other cultures, express ideas, participate in society, and discover and use their analytical and imaginative abilities. In the implementation of Indonesian language learning, there are many gaps, so that it is less able to achieve the learning objectives that have been determined optimally. This research was conducted in class 5 SDN Tegalsari 1 Banyuwangi, East Java, Indonesia. The purpose of this study was to determine the obstacles in the process of learning Indonesian in elementary schools. This research uses a case study approach. Data was collected by observing the Indonesian language learning process, interviews, and checking the average test scores of students. The data collected were analyzed using interactive analysis according to Miles and Huberman. The results of the research are 1) Teachers have not used media in the learning process, especially learning Indonesian; 2) The learning carried out is still conventional; 3) Students in learning tend to memorize the subject matter. Through learning innovations that are in accordance with the characteristics of elementary school students, it is expected to be able to create meaningful learning.