Reviewerly/exHarmony huggingface dataset:
Time and feature specific sentiment analysis of product reviews

Customer reviews are a great source of firsthand experience–related information about the product. These can be usefully exploited to gain an insight about the product, its features, its improvements, its loopholes, etc. Such reviews are important for both companies and for the customers. For companies, these reviews act as feedback about their products, and for the customers, they are useful for analyzing whether or not to purchase the product. However, it becomes very difficult to go through all the reviews and analyze the product. Moreover, if one is interested only in few features of the product, it becomes a double-fold task for him. Also, it is very difficult to say that how good the product is as per the current market. Hence, sentiment analysis has been explored to analyze the review comments considering time- as well as feature-specific sentiments of product reviews. In this work an "aging factor" to assign time-specific weights to the reviews has been introduced. All the weighted reviews about the specific features sum up to give an overall idea about that feature. Customer reviews about a mobile phone, collected from Kaggle, have been used to perform the experiment. Various machine-learning algorithms, for example, Naïve Bayes, support vector machine, random forest, and maximum entropy classifiers have been experimented upon, and the best working algorithm was chosen for further steps. At last an insight about the future work in this direction has been given.
Comparative analysis of VM consolidation algorithms for cloud computing

Virtual machine consolidation is a major solution for addressing the issue of increasing energy consumption by cloud computing data centers. A lot of work is done on developing algorithms for detecting underloaded, overloaded hosts, selection of virtual machines and their placement to perform the consolidation. These algorithms are usually tested on publicly available Planet lab workload. There is a need to know how benchmarks algorithms used in consolidation of virtual machines respond to other workloads. This paper is an attempt to evaluate these algorithms on Google workload trace. An importer is made to use this dataset by extending the CloudSim toolkit. The comparison of results using Planet lab and Google workload traces is made which shows the difference of 46.41%, 14.84%, 12.86% and 44.83% respectively in terms of number of virtual machine migrations, service level agreement violation time per active host, number of hosts shutdown, and energy consumption. The objective comparison of results illustrated that there is a need to test the proposed algorithms on multiple datasets in order to be assessed as optimal.
PET Image Super Resolution using Convolutional Neural Networks

Positron Emission Tomography (PET) is a nuclear, in-vivo medical imaging technology which can make 3D images of tissue metabolic act, in which high dose of tracer is needed to obtain a high quality PET image, which affects patients' health. Due to the limitations of using high dose tracer and limitations of physical imaging systems, it is not easy to get an image in desired resolution. Simplest approach to generate a high resolution image is by post processing. Single Image Super Resolution (SISR) is a post processing procedure to retrieve a high resolution image from a low resolution input. We propose a convolutional neural network trained on PET images which can estimate a high resolution PET image from its input low resolution image.
Analysis and Prioritization of Design Metrics

Clones at domain level during the design phase plays an important role to identify the risk at an early stage. The authors have analyzed the Design Metrics (DM) for its contribution towards the structural properties in domain models of the software. Then prioritization of these DM leads to classification of the majorly contributing DM elements. These Design Metrics relates to classes within the class diagram at design level that is useful for the identification of cross cutting concerns, origin analysis of software, software quality prediction and maintenance at the class level granularity. The authors have empirically evaluated the results for structural model clone detection.
Adaptation of the educational process to the requirements of the global nuclear market according the concept of «economic cross» through its digitalization

The article proposes the directions of adaptation of the educational process in Russian universities to the requirements of the global educational market through the modernization of their IT-component. The results achieved in the digitalization of the educational process and ensuring its competitiveness are considered. Reserves of the use of digitalization tools have been identified as part of the adaptation of the educational process in Russia to the requirements of the global educational market. Based on the results of five-year time-lapse observations of the authors, the directions of adaptation of the digitalization tools of the educational process are proposed taking into account the current economic needs of Russian education. In this study both traditional technologies for Russian universities and technologies used mainly by foreign educational organizations were considered as tools that can be used to improve the competitiveness of domestic education in the global education market.
Framework of Semantic Annotation of Arabic Document using Deep Learning

Semantic Web vision is to have machines interpret and understand the content of Web documents. There is a need to convert the existing Web of documents into an understandable format, which could be done by automatic semantic annotation. Annotation could be performed using a set of tools provided with general and domain-specific ontologies. The aim of this paper is to present a generic semantic annotation framework of Arabic text using deep learning models. The framework produces annotations using different output formats for a given set of Arabic documents and ontologies. With a prototype of the framework, the initial evaluation shows a promising performance using different public Arabic word embedding models with different vectorization and matching techniques.
Using machine learning to assist with the selection of security controls during security assessment

In many domains such as healthcare and banking, IT systems need to fulfill various requirements related to security. The elaboration of security requirements for a given system is in part guided by the controls envisaged by the applicable security standards and best practices. An important difficulty that analysts have to contend with during security requirements elaboration is sifting through a large number of security controls and determining which ones have a bearing on the security requirements for a given system. This challenge is often exacerbated by the scarce security expertise available in most organizations. In this article, we develop automated decision support for the identification of security controls that are relevant to a specific system in a particular context. Our approach, which is based on machine learning, leverages historical data from security assessments performed over past systems in order to recommend security controls for a new system. We operationalize and empirically evaluate our approach using real historical data from the banking domain. Our results show that, when one excludes security controls that are rare in the historical data, our approach has an average recall of ≈ 94% and average precision of ≈ 63%. We further examine through a survey the perceptions of security analysts about the usefulness of the classification models derived from historical data. The high recall – indicating only a few relevant security controls are missed – combined with the reasonable level of precision – indicating that the effort required to confirm recommendations is not excessive – suggests that our approach is a useful aid to analysts for more efficiently identifying the relevant security controls, and also for decreasing the likelihood that important controls would be overlooked. Further, our survey results suggest that the generated classification models help provide a documented and explicit rationale for choosing the applicable security controls.
Reinforcing Security of Medical Data using Blockchain

Data security and privacy is one of the most crucial design parameter in any kind of modern application. The rate of connected devices is increasing day by day hence enhancing the security becomes a challenge. The healthcare industry faces many losses due to poor security design in data sharing and access management. Today most of the security is ensured leveraging cryptography alone, but blockchain has become another solution for security and privacy. Blockchain can be used as decentralized trust and a provider of universal truth across the application. Blockchain also utilizes cryptography in itself makes it more secure and trust able, hence the proposed solution uses combination of blockchain and cryptography to reinforce patient data permissions and sharing. Blockchain also enables to become compliant of various data protection and privacy rules.
Nonparametric K-means algorithm with applications in economic and functional data

Inspired by the well-known relationship between K-means algorithm and Expectation-Maximization (EM) algorithm for mixture models, we propose nonparametric K-means algorithm for estimation of nonparametric mixture of regressions and mixture of Gaussian processes. The proposed methods are illustrated by extensive numerical simulations, comparisons, and analysis of two real datasets. Simulation studies and applications demonstrate that our method is an effective and competitive procedure for modified EM algorithm in nonparametric mixture settings.
Bayesian inference for mixtures of von Mises distributions using reversible jump MCMC sampler

Circular data are encountered in a variety of fields. A dataset on music listening behaviour throughout the day motivates development of models for multi-modal circular data where the number of modes is not known a priori. To fit a mixture model with an unknown number of modes, the reversible jump Metropolis-Hastings MCMC algorithm is adapted for circular data and presented. The performance of this sampler is investigated in a simulation study. At small-to-medium sample sizes (n≤100), the number of components is uncertain. At larger sample sizes (n≥500) the estimation of the number of components is accurate. Application to the music listening data shows interpretable results that correspond with intuition.
Integration of distributed ledger technology into software electronic signature exchange service

Object of the research: information storage, transmission and processing systems. Subject of the research: approaches to implementing the distributed ledger technology, in particular, the blockchain technology, as well as implementation issues and feasibility. Purpose of the work: enhancing the safety of data storage, transmission and processing by embedding distributed ledgers in the existing information system. Research methods: the theoretical and analytical research is supposed to underlie the study of the experience building upon the current application and possible options for boosting the potential of the distributed ledger technology, in particular, the blockchain technology. Scientific novelty. The results of the research are supposed to be used to issue proposals and recommendations on how to improve the security of data storage and processing through the use of distributed ledger technology. Practical importance. Practical perspectives consist in the fact that the discussed process of developing and implementing a distributed system simplifies further integration of the technology under consideration with the resulting blockchain service enabling data storage inside a non-modifiable registry.
Information Security in M-learning Systems: Challenges and Threats of Using Cookies

The trend of rapid development of mobile technologies has highlighted new challenges and threats regarding the information security by the using cookies in mobile learning (m-learning) systems. In order to overcome these challenges and threats, this paper has identified two main objectives. First, to give a review of most common types to cookies and second, to consider the challenges and threats regarding cookies with aspects that are directly related to issues of security and privacy. With these objectives is possible to bridge security gaps in m-learning systems. Moreover, the identified potential challenges and threats are discussed with the given proposals of pragmatic solutions for their mitigating or reducing. The findings of this research may help students to rise security awareness and security behavior in m-learning systems, and to better understand on-going security challenges and threats in m-learning systems.
Enhanced Double-Carrier Word Embedding via Phonetics and Writing

Word embeddings, which map words into a unified vector space, capture rich semantic information. From a linguistic point of view, words have two carriers, speech and writing. Yet the most recent word embedding models focus on only the writing carrier and ignore the role of the speech carrier in semantic expressions. However, in the development of language, speech appears before writing and plays an important role in the development of writing. For phonetic language systems, the written forms are secondary symbols of spoken ones. Based on this idea, we carried out our work and proposed double-carrier word embedding (DCWE). We used DCWE to conduct a simulation of the generation order of speech and writing. We trained written embedding based on phonetic embedding. The final word embedding fuses writing and phonetic embedding. To illustrate that our model can be applied to most languages, we selected Chinese, English, and Spanish as examples and evaluated these models through word similarity and text classification experiments.
Attention Based R&amp;CNN Medical Question Answering System in Chinese

This paper proposes a Chinese medical question answering system based on CNN s with self-attention embedded model. The key idea of the proposed framework is first understanding whole sentences via LSTM, obtaining the feature map by the convolution layer of CNN, then further-comprehending of question and answer by self-attention and finally pass through to polling layer of CNN to enhance accuracy. In addition, due to the word segmentation issue in the Chinese especially in medical field, character embedding is applied to enhance the accuracy. The experimental results show that the proposed framework improves the accuracy around 80%.
The Implementation of Teaching Reading Through Genre Based Approach for University Students

Abstract The first aim of this study investigated how the Genre Based Approach (GBA) can be used to build the university students’ motivation in reading procedure text. The second is how GBA can be used to solve the students’ difficulties in reading procedure text. It used a qualitative approach which involved the case study method. The research participants in this research were 10 of first semester students who took the advanced reading at the Informatics Engineering Department, Faculty of Engineering, UMT. The GBA can be used to build the students’ motivation (by using the extrinsic factors of motivation), and it can be used to solve the students’ difficulties. The researchers implicate, the lecturer should apply GBA to increase the university students’ motivation and helps their challenges in reading procedure text. Based on the observation, there were many students uncaring about it until the lecturer commanded them to read a procedure text. Moreover, we hope that other researchers will conduct further exploration of the teaching reading procedure text using GBA.
Secure Cloud e-Voting System Using Fully Homomorphic Elliptical Curve Cryptography

e-Voting system simplifies the traditional voting process from the point of view of both the voters as well as the officials. Voter turnout increases as the voting become more easy and convenient. The expense for human resources is drastically reduced as counting and tallying are all done by the software itself. Homomorphic Encryption (HE) is a cryptographic method which supports the processing of data while it is in encrypted form. In this paper, a cloud e-Voting system is implemented using Fully Homomorphic Elliptical Curve Cryptography (FHECC). This ensures that privacy is preserved and the risk of the vote being tampered or leaked is minimum, even if the votes are stored in the public cloud. This paper also analyses the implementation of FHECC and compares it with older and widely used homomorphic algorithms like ElGamal cryptosystem and Paillier cryptosystem and the study shows that FHECC has better performance and improved security.
On the Fusion of Text Detection Results: A Genetic Programming Approach

Hundreds of text detection methods have been proposed, motivated by their widespread use in several applications.Despite the huge progress in the area, which includes even the use of sophisticated learning schemes, ad-hoc post-processing procedures are often employed to improve the text detection rate, by removing both false positives and negatives.Another issue refers to the lack of the use of the complementary views provided by different text detection methods.This paper aims to fill these gaps.We propose the use of a soft computing framework, based on genetic programming (GP), to guide the definition of suitable post-processing procedures through the combination of basic operators, which may be applied to improve detection results provided by multiple methods at the same time.Performed experiments in the widely used ICDAR 2011, ICDAR 2013, and ICDAR 2015 datasets demonstrate that our GP-based approach leads to F1 effectiveness gains up to 5.1 percentage points, when compared to several baselines.
Document protection technology in the digital economics using cognitive biometric methods

To protect documents from falsifications and counterfeiting in the world of high information technologies, it is proposed to use, in addition to existing security technologies (signature and digital signature), a technology that would connect the author's biometric data with the protection and content of the document in such a way that changes the text would change protection, and change protection without biometric data of a person would be impossible or difficult. This technology is an image of a sonogram of a speech signal by an author with boundaries for recognition. A sonogram stores biometric data about a person's voice.
Effectiveness of Convolutional and Capsule network in Malaria Parasite Detection

Malaria, a life-threatening disease, takes millions of lives every year all over the world. Early detection of malaria can be an effective way to right with this disease. This study investigates the effectiveness of the combination of convolutional and capsule network to detect the malaria parasite in red blood cells in terms of standard evaluation metrics. The proposed architecture achieves higher accuracy in comparison to different existing convolutional neural networks. The proposed method also shows stable and higher accuracy in case of different ratios of train and test set separation. The proposed architecture attains about 95% accuracy when only 10% of the dataset is kept for training purpose. Such performance ensures the superiority of the CNN-Capsule network in malaria parasite detection.
Design of Underwater Humanoid Flexible Manipulator Motion Control System Based on Data Glove

The ocean is rich in resources. In order to facilitate the collection of marine resources, human beings have designed many robots to replace divers to overcome the harsh underwater conditions, most of which use motion rocker to control the movement of the rigid manipulator. It is difficult to accurately operate the underwater rigid manipulator with the moving rocker, and the rigid gripper and tools are also easy to damage and destroy the object being operated, which is not suitable for the work scene with high requirements for operation. Therefore, we design an underwater humanoid flexible manipulator motion control system (SMA controller) based on data glove, which can realize the precise control of complex actions, and reduce the destructive behaviors caused by improper operation.
Tsunami Potential Identification based on Seismic Features using KNN Algorithm

Tsunami is one of the destructive natural disasters in the world. Tectonic earthquake events trigger most of tsunamis around the world. Machine learning has been used widely in various problems in the world, including natural disasters problems. However, the use of machine learning method in specific problems related to tsunami still requires a lot of study and improvement in many aspects. This paper presents the tsunami potential identification based on seismic parameters by utilizing a classification algorithm, namely k-nearest neighbors (k-NN). The algorithm is applied to classify earthquake events in the dataset into two classes, i.e., tsunami potential and no tsunami potential events. The study uses three schemes of features for the experiment to obtain the optimal accuracy. Results indicate that the accuracy of the identification is a promising result for initial research in this area.
Organization and Management of Educational Work in Universities in the Light of National Strategic Guidelines for the Development of Education

Abstract The main issue of the research is the problem of improving the quality of organization and management of educational activities in Russian Universities within the framework of the state youth policy. As part of the comprehensive and effective development of territories, it is necessary to fulfill the requirements to ensure the development of civil initiatives and high moral qualities of future specialists. Taking into account not only national but also international standards, educational activities in Russian Universities are becoming a priority for the strategic development of education in the country. The aim of the study is to develop a system of criteria for improving the quality of educational activities in Russian Universities within the framework of the state youth policy, the development of civic initiatives and high moral qualities of future specialists. The system of criteria should be multilevel, take into account all stages of interaction. The types and principles of social and educational work in the system of vocational education are considered. The article also contains various forms and methods of education currently implemented in Universities. Summary. The project of Interregional public organization of the League of Higher School Teachers aimed at effective organization of educational work in Universities is presented.
Towards a Better Understanding of the Value of Blockchains in Supply Chain Management

Whether or not blockchain technology adds value to supply chains is a highly controversial topic. It has been lauded as the technology that brings transparency and efficiency to supply chains. However, others find this view overly optimistic. In this paper, we aim to add more insight into the issue by applying an action design research approach. We develop and evaluate two artifacts in collaboration with companies and use the results to conclude how blockchain technology can help overcome issues in the current supply chain information flow. It shows that blockchains can provide a shared trust base and a common standard that allows for increased information exchange between supply chain partners. Based on our findings, we propose a research agenda for studying blockchain technology with a stronger focus on governance mechanism and transparency issues.