Reviewerly/exHarmony huggingface dataset:
A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports

A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset.
Exploring Blockchain Technology and Digital Certificates in the Education Sector

As the fourth industrial revolution is in play across the globe, emerging technologies such as blockchain are increasingly affecting how students’ digital academic documents and certificates are issued and distributed. Higher Education Institutions (HEIs), students, employers, and other stakeholders in the educational sector face challenges regarding the falsification of academic records and digital certificates. However, relatively few adoptions of blockchain in education have been carried out by HEIs and businesses. Blockchain promises credibility, immutability, security, and consensus in light of issuing digital certificates and keeping academic records. Although blockchain comes with implementation challenges such as legal framework, scalability, and limited expertise, key stakeholders, including governments, academia, and businesses, must collaborate to explore and ensure the usage of blockchain-oriented educational solutions in the issuance of digital certifications around the world.&lt;br&gt;
<scp>OGIF</scp>: A novel Optimized Guided Image Filter for image enhancement

Abstract Image processing techniques are applied on various kinds of images such as medical images, microscopy images and remote sensing images for improving the visual qualities of these images as their credibility is questioned because of poor visibility, contrast, acquired noise etc. Image enhancement is necessary for removing the visual ailment of these images. Guided image filter (GIF) has been a suitable filter for image contrast enhancement, but the controlling parameters are taken as constant values in the conventional GIF. These control parameters vary for different kind of images; therefore, require manual tuning and adjustment which is often cumbersome, time‐consuming. The proposed work modifies the existing GIF by incorporating performance improvement in terms of optimal selection of control parameters. Thus, a novel optimized guided image filter (OGIF) is developed with the help of particle Swarm optimization (PSO) algorithm. The design methodology involves contrast and edge enhancement of the images which helps in accurate detection of region of interest (ROI) as well as noise removal. OGIF automatically adjusts the value of the controlling parameters in an optimal manner on which the detection of ROI is the most dependants. Image quality assessment (IQA) of enhanced images has been carried out using measurement of enhancement (EME), peak signal‐to‐noise ratio (PSNR in dB), measure of enhancement error using entropy (EMEE), standard deviation (SD) and entropy. The incremental change in the values of EME and EMEE advocates the suitability of OGIF for contrast enhancement.
Discovering fans and anti-fans among social media users based on their emotional reactions and comments

While audience profiling has been a critical concern in social media marketing, little research has used a systematic methodology to identify fans and anti-fans in social media communities. This study aimed to develop a fan and anti-fan detection model by analysing social media users’ mood responses and comments on fan page posts. The sentiment analysis of comments was conducted using a bidirectional long short-term memory (LSTM) model. A total of 83 posts, 849,820 emoticons and 216,688 comments were collected from two different fan pages over 14 days. Results showed that the proposed model, combining emotional reaction analysis and sentiment analysis of their comments, exhibited better fan and anti-fan identification capability than the single-dimensional behaviour model. It exhibited 96% accuracy, 100% precision and 93% recall in terms of community management. This study provides a novel, accurate and efficient way to identify fans and anti-fans that can help form more targeted marketing strategies for social media managers in a cost-effective manner.
Mixture of multivariate generalized Gaussians for multi-band texture modeling and representation

We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables to build a compact and accurate representation of texture images using multi-resolution texture transforms. The MoMGG model enables to describe the joint statistics of subbands in different scales and orientations, as well as between adjacent locations within the same subband, providing a precise description of the texture layout. It can also combine different multi-scale transforms to build a richer and more representative texture signature for image similarity measurement. We tested our model on both traditional texture transforms (e.g., wavelets, contourlets, maximum response filter) and convolution neural networks (CNNs) features (e.g., ResNet, SqueezeNet). Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-the-art methods.
Multi-key Fully Homomorphic Encryption without CRS from RLWE

Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys. However, most MFHE schemes require a Common Random/Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext. Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext. Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently. In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without a CRS. To this end, we remove the CRS by designing a relinearization algorithm without a CRS. Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a 3-round secure MPC protocol in the plain model from the RLWE assumption.
Optimization of Mirror Positioning for Single‐Camera 3D Displacement Measurements

Abstract Camera‐based 3D displacement measurements are only possible when two different viewing angles of the respective measurement point are available. We earlier introduced a simplified 3D reconstruction algorithm for camera‐mirror measurements where the mirror reflects the second view onto the camera sensor to eliminate the need for a second (costly) high speed camera. In this study, we concentrate on formulating guidelines on how to position the mirror with respect to the camera and the measured structure, the goal being to minimize errors arising in the 3D reconstruction procedure. The focus lies on the angle between the optical axes of both camera views. We hereby also take into account limitations in the experimental setup that the special camera‐mirror‐setup brings with it. This study is conducted in terms of simulations using the animation software BLENDER as well as real experiments on a simple vibrating structure.
How does ICT literacy influence reading literacy score in Indonesia: first attempt using spatial analysis approach

Purpose The aims of this research include (1) to identify the scores of reading literacy in 34 provinces and (2) to determine the impact of ICT literacy with other variables on reading literacy in Indonesia. Design/methodology/approach Thematic maps and Spatial Autoregressive Regression were applied to 2019 AKSI Survey data. Findings The results showed that only D.I. Yogyakarta, DKI Jakarta and Kepulauan Riau have a high percentage of reading literacy scores in the excellent category. The ICT literacy and teachers' competency scores significantly affect the percentage of reading literacy. Meanwhile, the percentage of lack of learning materials and GRDP per capita has no significant effect. Originality/value Previously, the national exam has been used to determine the quality of education in Indonesia, but it is ineffective because it only measures cognitive aspects. In 2015, the Ministry of Education initiated the AKSI survey, which measures cognitive (reading, math and science literacy) and non-cognitive aspects, as an effort to improve the quality of education in Indonesia. Some literature states that reading literacy is the most basic indicator for determining the quality of education, but in Indonesia, it is the lowest achievement. To improve reading literacy scores, the government has to utilize technological advances through School Digitization. However, this should be supported by the ICT literacy of students. Presently, there is no study to evaluate the impact of ICT literacy on reading literacy, which is also affected by regional value differences.
UCSNet: Priors Guided Adaptive Compressive Sensing Framework for Underwater Images

Image acquisition and reconstruction play an important role in underwater detections and explorations. However, the limited underwater acoustic communication channels and narrow bandwidth resources will have a great impact on the performance of the traditional data acquisition methods, resulting in the loss of details and blur in reconstructed underwater images. Compressive sensing theory (CS) which can reconstruct images from fewer measurement than that required by Nyquist sampling law has been proved to have good effect on image sampling and reconstruction. Nevertheless, the existing CS methods are not suitable for underwater images because most of them are designed for on-land images which have huge differences from underwater images. In this paper, we propose a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. In particular, our framework is composed of three sub-networks: underwater priors extraction and guidance network (UEGN), sampling matrix generation network (SMGNet) and channel-wise reconstruction network (CWRNet). Specifically, inspired by the underwater imaging physical models, UEGN is designed to extract features of underwater priors information and combine them adaptively. UEGN also introduce the imaging process into CS task to make sampling and reconstruction consistent with underwater imaging characteristics. SMGNet uses underwater content degradation to assist the analysis of structural information to generate sampling matrices. Considering the monotony of color tones caused by light absorption in underwater images, CWRNet embedded with the channel-wise module (CWM) is designed to enforce the whole network to allocate different number of sampling points on luminance and chrominance channels respectively and make feature maps extracted from them complement with each other. Experimental results demonstrate that our proposed framework can achieve both PSNR and SSIM gains on underwater images reconstruction quality and have greater visual quality than other state-of-art methods under fixed sampling ratios.
Parallel Graph Signal Processing: Sampling and Reconstruction

Graph signal processing (GSP) extends classical signal processing methods to analyzing signals supported over irregular grids represented by graphs. Within the scope of GSP, sampling and reconstruction represent fundamental tools that have received considerable attention. For very large graphs, however, many of the current methods struggle with the computational and memory requirements. Vertex domain and randomized sampling strategies somewhat ameliorate the computational requirements, but these algorithms perform poorly at preserving signal fidelity for graphs with hundreds of thousands of vertices. To address these shortcomings, this paper introduces a new and scalable approach that can be easily parallelized. This new approach uses existing graph partitioning algorithms in concert with vertex-domain blue-noise sampling and reconstruction, performed independently across partitions. In the reconstruction, some degree of overlapping is added to the partitions to induce trans-partition smoothness in the recovered signal. We also propose two sampling schemes based on the spatial characteristic of the graph that minimizes the recovery error. The first combines graph partitioning with the Void-and-Cluster algorithm, while the second approach uses Error Diffusion. We conclude this paper with experiments on synthetic and real data that show the effectiveness of these new approaches on very large graphs.
Fake Reviews Classification using Deep Learning

Customer decisions are heavily influenced by online reviews. Scammers and spammers can now influence consumer behavior by spreading false information in the form of reviews, either by promoting nonexistent goods or by disparaging rival goods. This means that identifying bogus from genuine reviews is more crucial than ever. For text classification, the standard method employs a bag-of-words model to represent text, leading to sparsity and word representations learned from neural networks with poor capacity for handling unknown words. In this work, we offer a method that uses an ensemble of models built using an aggregation methodology to make predictions based on data from three individual models trained using a multi-view learning approach. Our technology is based around a central idea of using bag-of-n-grams in conjunction with parallel convolution neural networks to extract valuable information from review text (CNNs). With the same amount of computing needed to train deep and sophisticated CNNs, we can leverage local context with an n-gram embedding layer that has tiny kernel sizes. In order to better extract feature representations from text, our CNN-based architecture takes n-gram embeddings as input and processes them with concurrent convolutional blocks. In addition to including linguistic aspects of the review text and non-textual information associated with reviewer behavior, our method for identifying fraudulent reviews also considers reviewer activity. We test our method using the openly available Yelp Filtered Dataset, and get F1 scores as high as 92% for recognizing fraudulent reviews.
High-density salt &amp; pepper noise removal using machine learning

This paper proposes an additional ‘Median Layer’ to the existing Convolutional Neural Network (CNN).The work primarily focuses on removing ‘High Density Impulse Noise’ present in an image. The proposed median layer performs median filtering on all feature channels. The developed network is an end-to-end network that does not require any non-trivial preprocessing tasks. Based on the experiments performed, results show that, by inserting a median layer to a CNN, the proposed architecture has given convincing results, when compared to the existing approaches.
Effect of GloVe, Word2Vec and FastText Embedding on English and Hindi Neural Machine Translation Systems

One of the most useful applications of Natural Language Processing is language translation. However, with the invention of machine translation, users who are comfortable in their local languages can understand, study, or search for content produced in any language of the world. Neural machine translation (NMT) is a new machine translation approach that was developed recently. NMT can benefit from the use of dense word representations, known as Word Embeddings. In this paper we have studied the effect of 3 pre-trained word embeddings, GloVe, Word2Vec and FastText (for the languages English and Hindi) on English and Hindi neural machine translation systems. The performance of the models have been evaluated using the BLEU metric. In the end the proposed models have been compared with Google translate as well.
Blockchain for 5G-Enabled IoHT—A Framework for Secure Healthcare Automation

Medical care has evolved into a lifeline for many people; due to this, there is an explosion in the field of medical big data. Wearable technology based on the Internet of Things (IoT) is being used by healthcare professionals to speed up diagnosis and treatment. There are now numerous sensors and devices, which are connected to the Internet. Internet of HealthThings (IoHT) can be used in many different ways in healthcare. Sensors and remote-monitored medical devices can help patients get more accurate and efficient diagnostics. This paper focuses on healthcare automation using the Internet of Things (IoT)-enabled devices. IoT-driven healthcare models can keep patients healthy and safe, enabling doctors to provide better care. In addition, healthcare frameworks significantly reduce healthcare costs and improve performance. Blockchain systems are transparent and thus safe to store and share private information. Data collected by medical devices attached to the body is kept in a cloud server alongside relevant medical reports as part of the proposed framework. The use of blockchain and 5G technology means that patients' data can be securely transmitted at high speeds and with rapid response times.
Image Encryption Based on Cyclic Chaos, PRNG and Arnold’s Cat Map

At present, globe of modern picture amid the data exchange is more critical. So nowadays, security issues while data exchange have become the major concern. Consequently, chaotic frameworks are exceedingly utilized in picture encryption applications due to its arbitrariness properties. Arnold's cat map is best used for shuffling of image. Picture encryption strategy utilizing Arnold cat map is proposed, but after a few cycles, the transformed picture will return to the initial picture so keeping the security concerns in mind in addition with Arnold cat map, Cyclic Chaos and PRNG is also proposed to provide dual layer of security. This makes the intruder difficult to decode the content. Further, security of image encryption techniques is analyzed by using histogram analysis, correlation analysis, peak signal to noise ratio (PSNR) and mean square error (MSE). The security analysis demonstrates that the proposed encryption system is secure as the cross-correlation esteem is nearly equal to 1, and the PSNR esteem is on an average 62 which is very high and MSE esteem very low, thus makes very difficult for the intruder to break the proposed image encryption algorithm.
Enhanced Multi-label Classification Model for Bully Text Using Supervised Learning Techniques

With the hasty growth of social media users, cyberbullying has evolved as a form of bullying that involves sending electronic messages. Cyberbullying is defined as the use of technological breakthroughs to bully another person. Bullies might utilize social media to abuse victims because it gives an upscale setting in which to do so. Cyberbullying detection is extremely vital; as a result, the net info is just too massive; thus, it is impractical to be half-track by humans. Machine learning will be useful to find language patterns of the bullies and thence will generate a model to mechanically find cyberbullying actions. This analysis aims to construct a classification model with optimum accuracy in distinguishing cyberbully speech mistreatment naïve Bayes, linear SVC, and logistic regression of machine learning. Linear SVC outperforms well on the ‘insult’ label out of the different five labels. It will determine the bully text and label it into the six label classes.
Sosyal Medya Verileri İle Bir E-Ticaret Sitesinin Çok Ölçütlü Karar Verme Yöntemleri İle Değerlendirilmesi

Evaluation of an E-Commerce Site with Multi-Criteria Decision Making Methods with Social Media Data Social media has reached 4.14 billion users in 2021.This number continues to increase every day.Digital platforms that users use for many purposes such as information acquisition, communication, entertainment are defined as a whole.Current and potential customers of the target audience of businesses spend a lot of time on these platforms.For this reason, it has become a necessity for businesses to show a presence on social media.Currently, it has become impossible for businesses that do not have a presence on social media to grow and compete in the market.In this study, user shares and services of an ecommerce site on social media were evaluated.For this purpose, four alternative service categories were identified by analyzing user shares.Five main and eleven sub-criteria were identified that were effective in evaluating these categories.Determined criterion weights analytical network process method was used.Alternative rankings were obtained using the PROMETHEE solution with determined criterion weights.Current, practical, and using a large pool of data, this study contributes to the literature in evaluating e-commerce sites.
Sentiment Analysis Model Using Word2vec, Bi-LSTM and Attention Mechanism

Within the interdisciplinary field of data science, this paper proposes a sentiment classification model applied to text, specifically tweets, using neural networks. To do that, after gathering and pre-processing the data, firstly, word2vec model is used to convert tweets into word vectors. Secondly, Bi-LSTM neural network is used to capture the semantic meaning of the text. Thirdly, attention mechanism is added to extract the most relevant words. Fourthly, a sigmoid function is used to achieve the emotion classification. Thus, after crossing each layer of the neural network, the model finally returns the result of the binary classification to detect the emotion of the social media text data. The presented model gives an accuracy of 84.12%, surpassing other existing models for this purpose.