Reviewerly/exHarmony huggingface dataset:
Developing an LSTM-based Classification Model of IndiHome Customer Feedbacks

The development of companies in the service sector is inseparable from problems with the customer service process. The latest trends show that customers are more comfortable using social media and chat services as feedback delivery media compared to sending emails.In this study, we classify user feedback from an Internet Service Provider (ISP) in Indonesia named IndiHome. In this study, there are two test scenarios carried out to classify, the first scenario uses Short Term Memory combined with Word Embedding, and the second scenario uses Naive Bayes combined with TF-IDF. based on the test results, obtained accuracy on the LSTM method combined with Word Embedding with an f1 score of 87.98 % and accuracy obtained at Naive Bayes combined with TF-IDF with an f1 score of 76.77 %. From the tests that have been done, different results are obtained and have a big difference, which is 11.21%. The difference in the results of this classification is influenced by several factors, namely the first factor is the extraction of features used and the second is the data used. In feature selection, TF-IDF is more ideal for data that has a large or long document size because the representation seen from the whole document, in contrast to Word Embedding that is not affected by the data size because the representation is seen from words. so this is what causes the method of using LSTM combined with Word Embedding to produce higher results.
Quantum blockchain networks

This paper explores and suggests possibilities for the design of quantum blockchain systems that are inspired by quantum processing techniques. Quantum states are defined that can be processed either by a physical quantum computer or virtually by emulation on a classical computer where such states can be entangled across different nodes in the system. The collapse of quantum state variables are explored utilizing non-deterministic smart contract processing. A quantum blockchain network is realized with different nodes in the system interacting with each other though a communication network. Different networking use-cases are explored such as to determine which user is given access to a network at a given time, or to select the best access node for a given user.
Expert System for Monitoring Elderly Health Using the Certainty Factor Method

A person who is in the elderly phase will experience various decreases, ranging from decreased memory or senility, hormone production, skin elasticity, muscle mass, bone density, strength and function of body organs, and the immune system. As a result, it is difficult for the elderly or the elderly to fight against various kinds of disease-causing bacteria or viruses, comorbidities, and adaptation to the social environment. Due to the complexity of this health problem, improvements can not only be made in the aspect of health services but also improvements in the environment and engineering of population factors or hereditary factors, but it is necessary to pay attention to behavioral factors that have a considerable contribution to the emergence of health problems. This research uses the certainty factor (CF) method which can provide a measure of belief in a symptom as a measure of how much the value is in the later diagnosis. The purpose of making this expert system is so that patients, patient families, and medical teams can monitor the health of the elderly daily. The results of this study indicate that using the CF method has an accuracy rate of 91 percent for the prediction of patients who have cholesterol
The virtual archaeology and interpretative process: Case study of the virtual reconstitution of a Hercules marble statue from the nameless temple of Tipasa

This research concerns the virtual reconstruction of a marble statue, in which a fragment was discovered during excavation work carried out in the 18th century at the antique site of Tipasa, then a virtual restitution of this statue in its original location. Owing to the photogrammetry technique and 3D modeling programs, three-dimensional models were created and a comparative study was done to find possible similarities between the fragment and the statue of Hercules exhibited in the museum of Cherchell which belonged to the same locality (Mauretania Caesariensis).
An Assessment Framework for Distributed Ledger Technology in Financial Application

Distributed Ledger Technology (DLT) currently stands in the limelight. In many people's eyes, DLT has great potential to accelerate the digitalization process and substantially change the roles and services of central banks. On the other hands, as many new applications for DLT spring up, there isn't an assessment framework to evaluate DLT applied in the financial industry, leaving those systems to be easily under attack. This paper presents an initial DLT assessment framework for financial application. Through a suite of benchmarks, we show the evaluation results and explore the factors which affect the performance and security of DLT application.
Image dehazing with uneven illumination prior by dense residual channel attention network

Existing dehazing methods based on convolutional neural networks estimate the transmission map by treating channel-wise features equally, which lacks flexibility in handling different types of haze information, leading to the poor representational ability of the network. Besides, the scene lights are predicted by an even illumination prior which does not work for a real situation. To solve these problems, the authors propose a dense residual channel attention network (DRCAN) for estimating the transmission map and use an image segmentation strategy to predict scene lights. Specifically, DRCAN is built based on the proposed dense residual block (DRB) and dense residual channel attention block (DRCAB). DRB extracts the hierarchical features with increasing receptive fields. DRCAB makes the network focus on the features containing heavy haze information. After the transmission map is estimated, fuzzy partition entropy combined with graph cuts is used to segment the transmission map into scene regions covered with varying scene lights. This strategy not only considers the fuzzy intensities of the low-contrast transmission map but also takes spatial correlation into account. Finally, a clear image is obtained by the transmission map and varying scene lights. Extensive experiments demonstrate that our method is comparable to most of existing methods.
Strategic prospects for the development of human capital in the context of singularity and intellectualization of the Russian economy

Abstract A comparative analysis of the relationship between the development trends of the modern information society and high-tech production in Russia and the world is carried out, the possibilities of intellectualization of the Russian economy in terms of the implementation of the principle of singularity are determined. The authors’ attention is focused on studying the development trends of Russian education in a historical perspective and analyzing the development model of human capital in the context of the transition of the world and Russian economies to a new technological structure, the hallmark of which is the general intellectualization of society. Based on the analysis of empirical data, it is concluded that modern society is very different from the society that was characteristic of the last century, which requires a new structure (construction) and a completely different educational content. The necessity of developing a new model of the education system of the XXI century as the basic basis for the development of human capital is justified, taking into account the key features of modern intellectual society and the principle of singularity.
A Self-Adaptive Preferred Learning Differential Evolution Algorithm for Task Scheduling in Cloud Computing

With the development of cloud computing, there are more and more complex requirements. Swarm intelligence algorithms are widely used in this field because they can solve these complex problems effectively, with good coordination and overall stability. This paper presented a calculation method of self-adaptive preferred learning differential evolution algorithm applied to cloud computing task scheduling, which can be used to control the individual allocation of cloud computing task scheduling by using it to copy and perish the individuals. According to the traditional differential evolution algorithm is the blindness of randomly selected individuals in evolving process, a self-adaptive preferred learning differential evolution algorithm is proposed. The algorithm selectively uses the well-behaved individual's information, based on this, the crossover operator and mutation operator were optimized adaptively to improve high convergence rate and search efficiency. The experimental results show that the proposed algorithm has 0.19-1.02 advantage in make-span tasks and more effective than other algorithms in system load balancing and stability for this scheduling problem.
Encoding Sentences with a Syntax-Aware Self-attention Neural Network for Emotion Distribution Prediction

Emotion distribution prediction aims to simultaneously identify multiple emotions and their intensities in a sentence. Recently, neural network models have been successfully applied in this task. However, most of them have not fully considered the sentence syntactic information. In this paper, we propose a syntax-aware self-attention neural network (SynSAN) that exploits syntactic features for emotion distribution prediction. In particular, we first explore a syntax-level self-attention layer over syntactic tree to learn the syntax-aware vector of each word by incorporating the dependency syntactic information from its parent and child nodes. Then we construct a sentence-level self-attention layer to compress syntax-aware vectors of words to the sentence representation used for emotion prediction. Experimental results on two public datasets show that our model can achieve better performance than the state-of-the-art models by large margins and requires less training parameters.
Comprehensive study of Deep learning based Telugu OCR

Optical Character Recognition (OCR) is a technology to recognize text in the images. Images in the sense means scanned documents or pictures. With the help of an OCR, it is possible to convert any image text into machine-readable text data. OCR has varied uses in understanding any scripts written in any language. They also convert the text with high accuracy. After any image text has gone through OCR processing, they edited with the help of MS Office. With the advent of OCR, there is no need to digitize the image text. Another advanced technology that is extensively used in OCR is deep earning. Deep learning can be called as a subfield of Machine Learning. This area is inspired by the structure and function of the brain. Deep learning has many algorithms that are used to solve problems. This area offers scalable solutions to problems. Another very important benefit of deep learning is feature extraction from unorganized data. In a broader sense, this area provides automated feature learning. It is called “deep”, as the layers of the network are made to learn deep. As segmentation is a part of OCR, delve into it in this paper. Segmentation is a subpart of image processing; OCR and it goes hand in hand with deep learning. Segmentation helps in easier analysis of the parts of an image. Nowadays segmentation is clubbed with deep learning and artificial intelligence to analyze and understand those features that was not possible attest a decade ago. Deep learning helps to learn the input patterns so that they can predict object classes. Image segmentation divides the input image into segments to help us to do a better analysis of the image.
Adiabatic Logic Based Energy-Efficient Security for Smart Consumer Electronics

Smart consumer electronic devices are mostly area constrained and operate on a limited battery supply and therefore, have tight energy budgets. Lightweight cryptography (LWC) such as PRESENT-80 allows for minimal area usage and low energy for secure operations. However, CMOS implemented LWCs are vulnerable to side-channel attacks such as correlation power analysis (CPA). Adiabatic logic is an emerging circuit design technique that can reduce energy consumption and be CPA resistant. Many existing adiabatic logic families use a four-phase clocking scheme which pays a large area penalty. Thus, in this article, we introduce 2-EE-SPFAL, a two-phase clocking scheme implementation of an existing adiabatic family known as EE-SPFAL. To show the applicability of 2-EE-SPFAL, we construct a two-phase clock generator that remains energy efficient and secure. From 100 kHz to 25 MHz, our results show an average energy saving of 76.5% to 21.3% between CMOS and 2-EE-SPFAL. As a case study, we performed a CPA attack on both the CMOS and 2-EE-SPFAL implementation of PRESENT-80 and determined that the CMOS key could be retrieved while the adiabatic key was kept hidden.
Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information

In the vision task of a self-driving system, the use of visible light images to segment an object often loses its functionality at night or in harsh weather. The far-infrared image shows different pixel values according to the thermal radiation quantity of the object itself, so it can be adapted to perform well at night and in harsh weather conditions. However, at the same time, it has insufficient texture features, blurred object boundaries and temperature inversion, which has a great impact on the segmentation task of traditional algorithms. In response to the above problems, this article proposes a far-infrared object segmentation algorithm using deep learning. In the current popular encoding-decoding structure, multi-scale pooling layers are used to obtain receptive fields of different sizes. This is used to solve the effects caused by the blurring of infrared objects. The feature enhancement module is designed for the multi-receptive field feature map, which can filter out the most versatile and highly semantic feature channels to reduce the effect of temperature inversion on segmentation. The obtained high semantic feature map is guided into the decoding structure and is fused with the features obtained by the encoder and the decoder. This allows richer information to be obtained between different feature maps. Finally, we also release a new low-resolution far-infrared segmentation dataset. Experiments are performed on three datasets, and the segmentation result of the mIoU(mean Intersection over Union) reaches 70.59%, 30.98% and 60.67%. A large number of experiments confirm the effectiveness and robustness of the network in far-infrared images and verify that the dataset released in this article has strong reference significance.
Healthcare Conversational Chatbot for Medical Diagnosis

Medical services are basic needs for human life. There are times when consulting a doctor can be difficult. The proposed idea is an AI-based chatbot that will provide assistance to the users regarding their health-based issues. The state of the art in the aforementioned field includes extractive bots that extract the keywords (i.e., symptoms from the user's input) and suggest its diagnosis. The proposed idea will be a conversational bot, which unlike the QnA bot will take into consideration the context of the user's whole conversation and reply accordingly. Thus, along with symptom extraction, the user will get a better experience conversing with the bot. The user can also normally chat with the chatbot for issues like if the user is not emotionally sound. For example, the bot will console the user if he/she is feeling stressed by recognizing the emotional health of the user.
Digital Payments: Blockchain based Security Concerns and Future

Technological growth has been tremendous in the past few decades and has brought significant changes in the lifestyle of human beings. One such field where the growth of technology brought a radical change is the field of transactions. Introduction of digital payments and digital money brought a whole new dimension in money payments and transactions. Policies of different governments has encouraged and also discouraged the use of various types of digital money. Still digital payments are ever growing in terms of volume. Still digital payments are not widespread as traditional cash transactions due to various security issues, threats, vulnerabilities and mostly the people's fear or doubt on its security aspects. The paper addresses such issues and finally proposes a better enhancement for the existing digital payment technologies by using secured and privacy sensitive technologies like blockchain.
Digital Media in Higher Education: Disruptive or Sustaining Innovation?

This article is an attempt to consider the application of digital media in education from the perspective of Christensen’s concept of disruptive and sustaining innovations. At the initial research stage, the hypothesis was formed that digital media as one of the main innovations in the educational sphere of recent decades play an auxiliary role in organizing and realizing the educational process. The authors have reviewed over 500 scientific works published in the period 2019–2020 and devoted to the issue of innovations in the educational environment. The analysis of their content was directed to identification of innovation types used in the higher education of various countries and consideration of digital tools and resources used for educational purposes from the standpoint of the concept on disruptive and sustaining innovations. The methods of synthesis and generalization were applied to build a picture of most popular digital solutions in the sphere of higher education and their innovative nature.
tqix: A toolbox for Quantum in X

We present an open-source computer program written in Python language for quantum measurement and related issues. In our program, quantum states and operators, including quantum gates, can be developed into a quantum-object function represented by a matrix. Build into the program are several measurement schemes, including von Neumann measurement and weak measurement. Various numerical simulation methods are used to mimic the real experiment results. We first provide an overview of the program structure and then discuss the numerical simulation of quantum measurement. We illustrate the program's performance via quantum state tomography and quantum metrology. The program is built in a general language of quantum physics and thus is widely adaptable to various physical platforms, such as quantum optics, ion traps, superconducting circuit devices, and others. It is also ideal to use in classroom guidance with simulation and visualization of various quantum systems.
Extending critical infrastructure element longevity using constellation-based ID verification

This work supports a technical cradle-to-grave protection strategy aimed at extending the useful lifespan of Critical Infrastructure (CI) elements. This is done by improving mid-life operational protection measures through integration of reliable physical (PHY) layer security mechanisms. The goal is to improve existing protection that is heavily reliant on higher-layer mechanisms that are commonly targeted by cyberattack. Relative to prior device ID discrimination works, results herein reinforce the exploitability of constellation-based PHY layer features and the ability for those features to be practically implemented to enhance CI security. Prior work is extended by formalizing a device ID verification process that enables rogue device detection demonstration under physical access attack conditions that include unauthorised devices mimicking bit-level credentials of authorized network devices. The work transitions from distance-based to probability-based measures of similarity derived from empirical Multi-Variate Normal Probability Density Function (MVNPDF) statistics of multiple discriminant analysis radio frequency fingerprint projections. Demonstration results for Constellation-Based Distinct Native Attribute (CB-DNA) fingerprinting of WirelessHART adapters from two manufacturers includes 1) average cross-class percent correct classification of %C > 90% across 28 different networks comprised of six authorized devices, and 2) average rogue rejection rate of 83.4% ≤ RRR ≤ 99.9% based on two held-out devices serving as attacking rogue devices for each network (a total of 120 individual rogue attacks). Using the MVNPDF measure proved most effective and yielded nearly 12% RRR improvement over a Euclidean distance measure.
The Data Sharing Scheme based on Blockchain

Data sharing is a vital method for data intensive and cooperative work-based applications. A central database system, which collects and stores data generated from different parties and provides open access, can not only save costs on infrastructures, but also provide consistent and uptodate data for all the participants. However, privacy and security issues also emerge especially when the shared data is highly sensitive, such as electronic medical records (EMRs). In this paper, we propose a new data sharing scheme with access control based on CP-ABE, searchable encryption, and blockchain. We realize two main goals in our scheme. First, data owners can share their data with multiple users who satisfy the access policy rather than just a single user. Second, the difficulty of the ciphertext query is overcome by means of searchable encryption technology. Most importantly, data owners do not need to interact with the data querier in the process of sharing. Both theoretical analysis and practical experiments show that the proposed scheme is secure and efficient.
A Deep Learning Architecture with Word Embeddings to Classify Sentiment in Twitter

Social Media Networks are one of the main platforms to express our feelings. The emotions we put in text tell a lot about our behavior towards any topic. Therefore, the analysis of text is a need for detecting one’s emotions in many fields. This paper introduces a deep learning model that classify sentiments from tweets using different types of word embeddings. The main component of our model is the Convolutional Neural Network (CNN) and the main used features are word embeddings. Trials are made on randomly initialized word embeddings and pretrained ones. The used pre-trained word embeddings are of different variants such as Word2Vec, Glove and fastText models. The model consists of three CNN streams that are concatenated and followed by a fully-connected layer. Each stream contains only one convolutional layer and one max-pooling layer. The model works on detecting positive and negative emotions from Stanford Twitter Sentiment (STS) dataset. The accuracy achieved is 78.5% when using the randomly initialized word embeddings and achieved a maximum accuracy 84.9% using Word2Vec word embeddings. The model not only proves that randomly initialized word embedding can achieve good accuracy, it also showing the power of the pretrained word embeddings that helps to achieve a higher competitive accuracy in sentiment classification.
Development of magnetic resonance image de-noising methodologies: A comprehensive overview of the state-of-the-art

Denoising of various radiological images is crucial, as a minute loss of a detail result in colossal failure. While numerous frequency domain and spatial denoising approaches are introduced to diminish the threat of data loss, noise removal in medical images without the loss of relevant features such as corners, sharp structures and edges is still a challenging task. Machine learning helps classify, identify, and quantify the patterns of medical images and has been proved as the state – of – the – art enhancement technique. In this paper, the fundamentals of the MR in medical imaging as well as the related noise removal techniques are reviewed. The performance evaluation of various machine learning based approaches is presented. Also included in this paper are the challenges, issues and future research directions.