Reviewerly/exHarmony huggingface dataset:
Development of Trust Based Access Control Models Using Fuzzy Logic in Cloud Computing

Cloud computing is the technology that provides different types of services as a useful resource on the Internet. Resource trust value will help the cloud users to select the services of a cloud provider for processing and storing their essential information. Also, service providers can give access to users based on trust value to secure cloud resources from malicious users. In this paper, trust models are proposed, which comes under the subjective trust model based on the behavior of user and service provider to calculate the trust values. The trust is fuzzy, which motivated us to apply fuzzy logic for calculating the trust values of the cloud users and service providers in the cloud environment. We use a Mamdani fuzzy method with gauss membership function for fuzzification and triangular membership function for defuzzification. Parameters such as performance and elasticity are taken for trust evaluation of the resource. The attributes for calculating performance are workload and response time. And for calculating elasticity, we have taken scalability, availability, security, and usability. The fuzzy C-means clustering is applied to parameters for evaluating the trust value of users such as bad requests, bogus requests, unauthorized requests, and total requests.
Mutation Operator-Based Image Encryption Algorithm for Securing IoT

In the current scenario, most of the communications via IoT-enabled devices such as smartphones, etc. are in the form of images, and these devices are having less memory and require high-speed communications. For such types of communications, there is a need for a secure encryption technique. This work proposed a mutation operator-based image encryption algorithm. This work uses 64-bit block cipher and 80-bit key for encryption. Here, mutation operator is used to creating confusion and diffusion among attackers. This work generates a unique random key for each image encryption. The proposed work is implemented on MATLAB 2015 software and tested on various parameters for checking the efficiency of proposed work.
Wavelet transform of a gray-scale image in a finite GF(2) field

A biorthogonal wavelet transform in a finite GF(2) field is studied for gray-scale images. The wavelet transform is implemented on the basis of binary filters of length 4 and 8, with the initial image resolved into bit planes. As an example, an arrangement of embedded digital watermarks into blocks of wavelet coefficients is considered. It is shown that the proposed technique makes it possible to eliminate the averaging errors that accompany the standard approach.
Statistical Modeling and DNA Mixture Evaluation

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Research Guides: Off-Campus Library and Interlibrary Loan Services: Off-Campus Library Services

This LibGuide outlines the policies and procedures for off-campus students, faculty & staff utilizing the services and resources of the Shapiro Library.
The Quantum Approximate Optimization Algorithm: Performance on Max-Cut using Heuristic Parameter determination

The Quantum Approximate Optimization Algorithm (QAOA) is one of the promising near-term algorithms designed to find approximate solutions for combinatorial optimization problems. The algorithm prepares a parametrized state that is aimed to maximize the expectation value of the objective function of the problem. The circuit for QAOA consists of p layers, and it depends on 2p parameters, the determination of which can be carried out using a variational quantum eigensolver (VQE) subroutine. This is a variational approach for finding optimal parameters, where the quantum state is prepared with a quantum computer, and a classical computer is used for calculating the corresponding objective function value and performing an outer loop optimization routine in order to find optimal parameters. Earlier work on the algorithm has shown that QAOA is closely related to the Quantum Adiabatic Algorithm, which results in noticeable patterns in the optimal parameters. To this purpose, two methods, INTERP and FOURIER, were proposed to exploit this structure by Zhou et al. (2018). In this thesis, I extend upon these results by examining the effectiveness of one of their methods, the INTERP method, on the Max-Cut problem from graph theory. The performance of QAOA using this method was studied on several classes of graphs and benchmarked against the currently best-known classical algorithm Goemans-Williamson, which achieves an approximation ratio of ρ ≈ 0.878 on arbitrary graphs. The classes of graphs examined include cyclic, weighted and unweighted 3-regular, and Erdős–Renyi graphs with edge probabilities of 0.5 and 0.75 and graph sizes ranging from 4 to 16. Moreover, it was investigated if the INTERP method offers an advantage compared to random initialization of parameters and whether or not the method is polynomial in p. It was found that different graph classes lead to similar but different parameter patterns. Furthermore, the performance of QAOA using the INTERP method was dependent on the graph class considered. For the small graphs investigated it was found that QAOA outperforms Goemans-Williamson on ER-0.50, ER-0.75 and weighted 3-regular graphs from p=7 and beyond with respect to how well the expectation of the objective function approximates the optimal value, while for unweighted 3-regular graphs p=6 suffices. However, the calculation of the parameters is costly and requires a number of function evaluations that is on the order of the number of possible bipartitions of small graph with sizes n ≤ 16. Moreover, the numerical results suggested that indeed the method is polynomial in p advocating that the algorithm might be a strong alternative to Goemans-Williamson for large graph sizes, once hardware allows it.
SSNCSE_NLP@DravidianLangTech-EACL2021: Offensive Language Identification on Multilingual Code Mixing Text

Social networks made a huge impact in almost all fields in recent years. Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. Everyday we have to deal with texts, emails or different types of messages in which there are a variety of attacks and abusive phrases. It is the moderator’s decision which comments to remove from the platform because of violations and which ones to keep but an automatic software for detecting abusive languages would be useful in recent days. In this paper we describe an automatic offensive language identification from Dravidian languages with various machine learning algorithms. This is work is shared task in DravidanLangTech-EACL2021. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages ( (Tamil-English, Malayalam-English, and Kannada-English)) collected from social media. This work explains the submissions made by SSNCSE_NLP in DravidanLangTech-EACL2021 Code-mix tasks for Offensive language detection. We achieve F1 scores of 0.95 for Malayalam, 0.7 for Kannada and 0.73 for task2-Tamil on the test-set.
Smartphone-based GNSS Method for Covid-19 Information on Android (Case Study: Surabaya City)

Abstract Coronavirus disease 2019 (Covid-19) is the respiration diasease that can be transmitted from individual to another. There have been 3906 ODP (individual on monitoring) cases, 3303 PDP (patient under surveillance) cases, 2918 confirmed cases, 766 recovered patients, and 282 deaths in Surabaya, as recorded on June 6th, 2020. GIS (Geographical information system) is a science based on computer software, to provide digital form information and the earth’s geographic surface analysis. GNSS (global navigation satellite system) is the positioning system method using satellite, which can be used by a lot of people at the same time and designed to obtain three-dimension position. The combination of Smartphone’s GNSS positioning and the spatial data of Covid-19 distribution in Surabaya is expected to be able to give useful information about health condition toward citizen, especially about Covid-19 pandemic via application based. The outcome is a color-level based distribution of Covid-19 cases which can be accessed android smartphone. Sub district with the highest rate of Covid-19 cases does not necessarily have proper health protocol facilities, and so does other sub districts with high rates of Covid-19 cases. In fact, none of Covid-19 referral hospital located in the sub district with high rate of Covid-19 cases. Based on the results of the questionnaire, the applications namely “Cegah Covid-19 Surabaya” has received good responses. This application is currently able to run on Android smartphones with the versions of Android 10, Pie, Oreo, Nougat, Marshmallow, and Lollipop.
Novel Image Compression and Deblocking Approach Using BPN and Deep Neural Network Architecture

Medical imaging is an important source of digital information to diagnose the illness of a patient. The digital information generated consists of different modalities that occupy more disk space, and the distribution of the data occupies more bandwidth. A digital image compression technique that can reduce an image's size without losing much of its important information is challenging. In this paper, a novel image compression technique based on BPN and Arithmetic coders is proposed. The high non-linearity and unpredictiveness of the interrelationship between the pixels present in the image to be compressed is handled by BPN. An efficient coding technique called Arithmetic coding is used to produce an image with a better compression ratio and lower redundancy. A deep CNN based image deblocker is used as a post-processing step to remove the artefacts present in the reconstructed image to improve the quality of the reconstructed image. The effectiveness of the proposed methodology is validated in terms of PSNR. The proposed method is able to achieve about a 3% improvement in PSNR compared with the existing methods.
Randomized extraction of the New Norwegian corpus

Randomized extraction of the New Norwegian Corpus (Nynorskkorpuset).Contains sentences in New Norwegian (Nynorsk) from the year 2000 and after. Tab-separated, one word pr. line, lemmatized and morphologically tagged, year and domain information is given. Annotation is done with the Oslo-Bergen tagger. Sentences in the Bokmal standard have been removed.This corpus is intended for use in the development of language technology.Size: 3,3 million sentences, 57,5 million words.
TRADITIONAL SPHERE AND INNOVATION IN MUSIC: ART STUDY ON THE THEORY OF COGNITION

This article uses its own methodology for the study of tradition and innovation in music. In parti-cular, cultural, philosophical and psychological roots were briefly enriched on an interdisciplinary basis. Although tradition and innovation are in harmony, it is said that at times there is a rivalry between the old and the new. But science has theoretically differentiated that it is created in accordance with social needs and aesthetic tastes of the growing and modern generation. A. Camus, Z. Freud on the works of such thinkers as Freud, the main reasons for the emergence of innovation in music were identified. The fact that it is likely to change on a different scale due to the realities of the time has been proved by examples related to the art of jazz. In order to study the traditional field of music and innovation, we will first consider its methodological and theoretical foundations also. This, in turn, means "development", "tradition", "new", "innovation" and so on. It is necessary to focus on conceptual concepts and to explain the situation in relation to the problem. There are also philosophical, cultural, sociological, art, etc. issues related to the traditional field and the art of innovation. Human development is always in the process of replacing the old with the new. It means accepting innovation while preserving some elements of the old. But there are no specific barometers to determine how much of the old will be preserved, how much the new will occur in the same phenomenon. While some industries dominate the level and weight of the old, in some industries many of its elements are lost, leaving only a small level, and innovation prevails. This phenomenon is a kind of law that covers all parts of public life, not only in the field of culture and art.
Active Contour Model Integrating Global and Local Information

Active contour model based on global information cannot segment intensity inhomogeneity images effectively. Although active contour model based on local fitting can segment images with intensity inhomogeneity effectively , they are senstive to the initial contour and often fall into local minima. Based on the above issues, we proposed active contour model integrating global and local information in this paper. First of all, we took the image statistics as the global item and the image local fitting information as the local item, then combined the above two through the weight parameter to obtain a new Signed Pressure Force (SPF) function integrating global and local information ; afterward, the new SPF function is introduced into the Selective Binary and Gaussian Filtering Regularization Level Set (SBGF-RLS) model so that we constructed a new active contour image segmentation algorithm; finally, we selected four sets of different types of images for MATLAB simulation experiments, and compared the proposed model with LBF Model, CV model and SBGF-RLS model . Experimental results show that the proposed model can quickly and accurately segment images with weak boundaries and uneven gray levels, and has the advantage of being insensitive to initial contours.
The Influence of Knowledge Generation on Organizational Creativity in CASNOS Foundation

This study aims to test the relationship between knowledge generation and organizational creativity. The second method was surveyed (analytical) using exploratory research through the survey as a data collection tool. The validity coefficient was measured as a reasonable degree of stability was obtained for a sample of (46) individuals. The data was analyzed using statistical package (SPSS V25). Descriptive Statistic Among the findings is that CASNOS has a medium level of the knowledge generation process, and there is a statistically significant effect of knowledge generation on the organizational innovation of CASNOS (El-Oued), according to the dimensions adopted in the study model.
LEGAL ASPECTS OF DEEPFAKES

The purpose of this article is to bring closer the legal aspects of deepfake. Beginning with what exactly deepfake is and what its history is, in what areas it is and can be used and what risks and benefits it brings. This article distinguishes between civil law and criminal law aspects. Furthermore, the authors have indicated examples of legal regulations of deepfake, which were adopted in such legal systems as the USA or China. At the end of the article, the authors give the potential evolutionary paths of legal solutions concerning deepfake technology.
Machine Translation Believability

Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based on their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.
Public Health Innovation through Cloud Adoption: A Comparative Analysis of Drivers and Barriers in Japan, South Korea, and Singapore

Governments are increasingly using cloud computing to reduce cost, increase access, improve quality, and create innovations in healthcare. Existing literature is primarily based on successful examples from developed western countries, and there is a lack of similar evidence from Asia. With a population close to 4.5 billion people, Asia faces healthcare challenges that pose an immense burden on economic growth and policymaking. Cloud computing in healthcare can potentially help increase the quality of healthcare delivery and reduce the economic burden, enabling governments to address healthcare challenges effectively and within a short timeframe. Advanced Asian countries such as Japan, South Korea, and Singapore provide successful examples of how cloud computing can be used to develop nationwide databases of electronic health records; real-time health monitoring for the elderly population; genetic database to support advanced research and cancer treatment; telemedicine; and health cities that drive the economy through medical industry, tourism, and research. This article examines these countries and identifies the drivers and barriers of cloud adoption in healthcare and makes policy recommendations to enable successful public health innovations through cloud adoption.
APPLICATION OF ROLLED NEURAL NETWORKS FOR DIAGNOSIS OF COVID-19 ON THE BASIS OF PULMONARY X-RAYS

The object of the study is the process of classification of lung radiographs for the diagnosis of COVID-19. The research is based on the use of deep convolutional neural networks, which make it possible to store spatial information and analyze complex images, preventing the attenuation of the gradient. The principle of operation of convolutional neural networks and the advantages of their use in application to complex images, in comparison with artificial neural networks based on a multilayer perceptron are considered. The main assumption of the study is the hypothesis that the use of a deep convolutional neural network for the classification of radiographs of the lungs will obtain a high-accuracy result in the diagnosis of COVID-19 and will automate the diagnostic process. The urgency of the problem of automated diagnosis of COVID-19 on the basis of lung radiographs is considered. Training of high-performance architectures of deep convolutional neural networks, with the use of additional methods of image processing to prevent retraining. The results of neural networks are compared and statistical information is given to assess the quality of their work. The analysis of the results of the artificial neural network, using image division by the Lyme method. The expediency and prospects of using deep convolutional artificial neural networks for automation of COVID-19 diagnosis on the basis of pulmonary radiographs are substantiated. Common errors of artificial neural networks and possible approaches to their prevention are analyzed. The disadvantages of using the considered approaches and the difficulties that may arise in automation are considered, according to the results, possible options for improving the quality of the deep convolutional neural network are proposed.
Graph Convolutional Networks with Multi-headed Attention for Code-Mixed Sentiment Analysis

Code-mixing is a frequently observed phenomenon in multilingual communities where a speaker uses multiple languages in an utterance or sentence. Code-mixed texts are abundant, especially in social media, and pose a problem for NLP tools as they are typically trained on monolingual corpora. Recently, finding the sentiment from code-mixed text has been attempted by some researchers in SentiMix SemEval 2020 and Dravidian-CodeMix FIRE 2020 shared tasks. Mostly, the attempts include traditional methods, long short term memory, convolutional neural networks, and transformer models for code-mixed sentiment analysis (CMSA). However, no study has explored graph convolutional neural networks on CMSA. In this paper, we propose the graph convolutional networks (GCN) for sentiment analysis on code-mixed text. We have used the datasets from the Dravidian-CodeMix FIRE 2020. Our experimental results on multiple CMSA datasets demonstrate that the GCN with multi-headed attention model has shown an improvement in classification metrics.
Design and Implementation of Drone Using Deep Learning

In this modern world of technology, there are multiple applications of robots which are using deep learning to interact with the human world, instead of using working humans. So, developing a system that would help us detect and recognize a face has become necessary to find out criminals and track for any unwanted objects flying nearby. The use of such unmanned air vehicles (UAV) to detect criminals and also alert for any moving objects that try to attack can help in both surveillance and infiltration will lead us to a future where no person will be required to carry out such missions physically, as they will be monitoring from a safe distance. The use of OpenCV library in python to detect and recognize a face made it possible to implement this system. In this research, Haar cascades are used to detect and recognize a face, and the code is implemented onto a Raspberry Pi. Using deep learning, the pictures of the face stored during detection can be used to recognize the person and hence successfully implementing the core software for this project. To track unwanted objects that may attack, an IR sensor is attached to the drone which when detects an obstacle and takes a picture on the Pi Camera sending it directly via email to the user.
Domain adaptation in practice: Lessons from a real-world information extraction pipeline

Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs. In this work, we describe an effort to do just that – combining state-of-the-art neural methods for negation detection, document time relation extraction, and aspectual link prediction, with the eventual goal of extracting drug timelines from electronic health record text. We train on the THYME colon cancer corpus and test on both the THYME brain cancer corpus and an internal corpus, and show that performance of the combined systems is unacceptable despite good performance of individual systems. Although domain adaptation shows improvements on each individual system, the model selection problem is a barrier to improving overall pipeline performance.
Fast Graph Learning with Unique Optimal Solutions

We consider two popular Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction. For each, we pick a popular model that we: (i) linearize and (ii) and switch its training objective to Frobenius norm error minimization. These simplifications can cast the training into finding the optimal parameters in closed-form. We program in TensorFlow a functional form of Truncated Singular Value Decomposition (SVD), such that, we could decompose a dense matrix $\mathbf{M}$, without explicitly computing $\mathbf{M}$. We achieve competitive performance on popular GRL tasks while providing orders of magnitude speedup. We open-source our code at this http URL