Reviewerly/exHarmony huggingface dataset:
Exact meaning of the ambiguous word for improving sentiment analysis

Sentiment Analysis is very important for many applications and tasks. It became very useful tool in market trends predictions, social media monitoring, sufficient predictor for election and products. Many approaches, by many researchers, were used for this task starting from rules-based to machine learning and deep learning approaches on different types of data. All of these methods suffer from a lack of accuracy as a result of not achieving the correct understanding of the texts. In this work, an approach for extracting and using the exact meaning of the word within the context is used as part of initialization data representation. Three well-known classifiers were used (Narve Bayes, Support Vector Machine, and Maximum entropy) for testing this approach on two languages (Arabic and English). The system was evaluated using precision, recall, f-measure, and accuracy on two different datasets (Stanford Sentiment Treebank for English language and LABR-V2 for Arabic language). The results showed that the exact meaning of the words is very useful and the accuracy was increased by range 7.3% for best cases and increased by range of 4% in worst cases. The contributions of this work can be summarized by: (i) using exact meaning of the word for two languages, (ii) using new approach for adding synonyms and antonyms for exact meaning of the word in context, and (iii) better accuracy was received compared with the baseline classifiers.
Query Expansion based on Word Embeddings and Ontologies for Efficient Information Retrieval

Information retrieval has been an ever-going process for end users to fetch relevant data at one go.The problem intensifies more with unstructured data in a semantic web environment.It is also a promising area for researchers to dive in and refine it from time to time.Expanding the user query and reformulating it is one probable solution to increase the efficiency of the information retrieval system.In this paper we propose "WeOnto", a novel two-level query expansion algorithm that utilizes the combination of web ontologies and word embeddings for similarity calculation.In the first level, the Real estate Ontology (REO) is created using Protégé and Sparql queries are passed to retrieve probable semantic words from the given ontology for each inputted user query.The first level gave significant results and improved the information retrieval by 18%.The second level of algorithm uses word embedding enhanced with the domain knowledge that helps to retrieve similar meaningful words based on cosine similarity for the same user query.Word embeddings are implemented using Word2Vec method that follows two architectures namely CBOW or Skip Gram.Most similar semantic words are retrieved using the CBOW word embeddings method in the proposed algorithm and concatenated with the semantic keywords generated from the real estate ontology to form a powerful reformulated query that gives promising relevant results.Finally, two topmost words as per their similarity index are taken to reformulate the original user query.Experimental results depict that proposed algorithm has given distinct results and has showcased significant improvement of 93% over the initial user query.
A Semi-supervised Deep Learning-Based Approach with Multiphase Active Contour Loss for Left Ventricle Segmentation from CMR Images

Along with the widespread achievement of machine learning in computer vision in recent years, plenty of deep learning models for medical image segmentation have been published, with impressive results. However, the majority of those model just leverages supervised technics while others additionally utilize semi-supervised and unsupervised technics, with the results are not as good as supervised ones though. Inspired by the efficiency of Mumford-Shah functional for unsupervised and Active Contour functional for supervised tasks, in this work, we proposed the new loss functional which integrates those two with some modifications and extension to the case of multiphase segmentation. It allows our deep learning model to segment multi-class simultaneously with high accuracy instead of binary segmentation. The proposed approach is applied to the segmentation of left ventricle from cardiac MR images, in which both endocardium and epicardium are simultaneously segmented. Our proposed method is assessed on 2017 ACDCA dataset. The experiment demonstrates that our new loss achieves the promising results in term of Dice coefficient and Jaccard index. This illuminates the efficiency of our method in multi-class segmentation for medical images.
Measurement of Small-Slope Free-Form Optical Surfaces with the Modified Phase Retrieval

In this paper, we demonstrate the use of the modified phase retrieval as a method for application in the measurement of small-slope free-form optical surfaces. This technique is a solution for the measurement of small-slope free-form optical surfaces, based on the modified phase retrieval algorithm, whose essence is that only two defocused images are needed to estimate the wave front with an accuracy similar to that of the traditional phase retrieval but with less image capturing and computation time. An experimental arrangement used to measure the small-slope free-form optical surfaces using the modified phase retrieval is described. The results of these experiments demonstrate that the modified phase retrieval method can achieve measurements comparable to those of the standard interferometer.
Using the project methodology to improve environmental education for students of technical specialties

Abstract Environmental education of students of technical specialties should take into account the features of their future specialty. Within the framework of economic activity, humanity has a significant impact on the state of the environment. Natural resources, which took millions of years to form, are used in the production of various material goods. This production process leads to negative phenomena associated with the destruction of the fragile natural balance. In addition to the impact of biocenosis, economic activity also worsens the environmental situation and has a negative impact on the existence of human civilization itself. For effective public control and monitoring of the state of the natural environment, the necessary conditions are the availability of environmental education among the population and the training of technical specialists capable of solving various environmental problems. The project form of training allows to increase the efficiency of specialists of technical specialties. And the use of projects related to environmental problems in the learning process gives students the opportunity to supplement their environmental knowledge. The poor environmental situation is the main reason for the decrease in the level of immune status and the increase in morbidity in the population. The sustainable development of human society can be achieved only if a dynamic balance is established between the needs of man and the ability of nature to self-repair.
An MILP approach for persistent coverage tasks with multiple robots and performance guarantees

Multiple robots are increasingly being considered in a variety of tasks requiring continuous surveillance of a dynamic area, examples of which are environmental monitoring, and search and rescue missions. Motivated by these applications, in this paper we consider the multi-robot persistent coverage control problem over a grid environment. The goal is to ensure a desired lower bound on the coverage level of each cell in the grid, that is decreasing at a given rate for unoccupied cells. We consider a finite set of candidate poses for the agents and introduce a directed graph with nodes representing their admissible poses. We formulate a persistent coverage control problem as a MILP problem that aims to maximize the coverage level of the cells over a finite horizon. To solve the problem, we design a receding horizon scheme (RHS) and prove its recursive feasibility property by introducing a set of time-varying terminal constraints to the problem. These terminal constraints ensure that the agents are always able to terminate their plans in pre-determined closed trajectories. A two-step method is proposed for the construction of the closed trajectories, guaranteeing the satisfaction of the coverage level lower bound constraint, when the resulting closed trajectories are followed repeatedly. Due to the special structure of the problem, agents are able to visit every cell in the grid repeatedly within a worst-case visitation period. Finally, we provide a computational time analysis of the problem for different simulated scenarios and demonstrate the performance of the RHS problem by an illustrative example.
Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e. , SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.
Cross-Domain Limitations of Neural Models on Biomedical Relation Classification

Relation extraction (RE) aims to extract relational facts from plain text, which is essential to the biomedical research field with the rapid growth of biomedical literature and generally large volumes of biomedicine-related text coming from various sources. Numerous annotated corpora and state-of-the-art models have been introduced in the past five years. However, there are no general guidelines about evaluating models on these corpora in single- and cross-domain settings with diverse entities and relation types. We aim to fill this gap for the task of detecting whether a relation holds between two biomedical entities given a text span. In this work, we present a fine-grained evaluation intended to perform a comparative evaluation of four biomedical benchmarks and understand the efficiency of state-of-the-art neural architectures based on Long Short-Term Memory (LSTM) with cross-attention and Bidirectional Encoder Representations from Transformers (BERT) for relation extraction across two main domains, namely scientific abstracts and electronic health records. We present a comparative evaluation of biomedical RE datasets, including the PHAEDRA, i2b2/VA, BC5CDR, and MADE corpora. Our evaluation of BioBERT and LSTM for binary classification shows significant divergence in in-domain and out-of-domain performance, finding an average drop in F1-measure of 34.2% for BioBERT. The cross-attention LSTM model developed in this work exhibits better cross-domain performance, with a drop of only 27.6% in F-measure.
Comparative analysis of Spark and Hadoop through Imputation of Data on Big Datasets

The various framework has been developed to deal with the big data in the remote sensing field. A comparison between the two big data processing frameworks known as Hadoop and Spark is presented in this article. The unstructured and structured data is processed efficiently with the spark framework. The Hadoop framework is used two elements Hadoop Distributed File System (HDFS), for storage and MapReduce to process the data. The Hadoop MapReduce system shows computational complexity in terms of big data processing. The execution time of the spark framework is faster than the Hadoop MapReduce platform of big data processing. We tested the PARAFAC tensor factorization-based data imputation task on the Hadoop and Spark platform. The data imputation task outcomes in Spark are much faster than the Hadoop and without an API framework. The execution time during Spark framework is lesser than the Hadoop and without API model of tensor factorization
Enhancing software modularization via semantic outliers filtration and label propagation

Software systems’ modular structure often drifts from the intended design throughout evolution. To improve the modular structure of a software system, the software clustering technology aiming to partition a software system into meaningful modules is demanding. Many clustering approaches rely on semantic information, which cluster software entities that use similar vocabulary. However, the existence of semantic outliers obstructing the clustering process is hardly considered. To overcome the existence of semantic outliers, this paper proposes a two-stage software clustering approach named EVOL (Enhancing Via Outliers filtration and Label propagation). A feature density-based outliers detecting algorithm is used to compute the local outlier factor of each feature. Accordingly, we filter out the semantic outliers and cluster remaining high-quality features to construct a partition skeleton; After that, assign each outlier into a suitable cluster by label propagation. To assess the effectiveness of the proposed approach, this paper conducts experiments on six folders from Mozilla Firefox and other four software systems, referring to the original design concepts and modular structure provided by the developers. The average of the evaluation metric MoJoFM shows significant improvement from 6% to 35% over the other six state-of-art clustering techniques. The results demonstrate that the filtration of the outliers facilitates the clustering results, and label propagation could place the outliers into a suitable cluster. In this paper, we propose EVOL, a new software clustering approach that considers semantic outliers filtration and label propagation. The experiment results show that the proposed approach EVOL can be very useful to enhance the quality of the software modularization.
Blockchain-based secure data sharing scheme using image steganography and encryption techniques for telemedicine applications

Telemedicine is an alternate way to the conventional delivery of healthcare services. Timely communication of the patient records in a secure and private way is essential for telemedicine services, where the poor secrecy affects the quality of the services to a certain extent. Presently, the advent of Blockchain technologies has attracted several researchers to achieve secure data sharing. In this way, this chapter designs a new Blockchain-based secure data-sharing scheme (BBSDSS) using image steganography and encryption techniques for telemedicine applications. The BBSDSS model involves three stage processes namely image steganography, encryption, and secure data sharing. Initially, glowworm swarm optimization algorithm is applied for the image steganography process. The presented model includes signcryption technique for encrypting the stegno image. Finally, the Blockchain technique is applied to enable secure sharing of the patient details. Extensive experimental analysis was taken place and the results are examined in terms of distinct aspects. The simulation values denoted that the presented algorithm has resulted in a higher peak signal to noise ratio (PSNR) of 51.75dB, 51.75dB, 51.75dB, and 51.75dB on the applied images 1–4, respectively.
The concept of “smart village” and rural territories of Russia

The article is dedicated to the problem of identifying strategic groups in the business education market. The authors conducted a systematic review of publications on the topic. The object of the research is the developed markets of business education in the USA and Western Europe, the subject of the research is the groups of relatively homogeneous educational institutions that demonstrate similar strategic behaviour (strategic groups). For researchers, dividing market participants into strategic groups is the basis for identifying efficiency factors in the respective groups, and for heads of educational institutions and market regulators, it is a prerequisite for constructing meaningfully interpreted rankings and business school ratings. The purpose of the work is to identify trends in changes of the business-schools grouping approaches and the factors underlying such groupings. The authors revealed a shift of researchers' the emphasis from the characteristics of educational programs offered by schools to the positioning of schools in a wide social and educational environment. Although the strategic analysis level is growing, the discussion topic remains fragmented and covers only a part of the business education markets.
OAuth 2.0 : Architectural design augmentation for mitigation of common security vulnerabilities

OAuth 2.0 is a widely used authorization protocol for cloud based web services and is used by many big firms such as Google, Facebook, Microsoft for granting limited access to the data. OAuth 2.0 has a flexible design and can be implemented and configured in a number of ways according to the use case. However, each of these implementation decisions and configuration choices come with their respective security implications. In our research, we carry out a simulation of the OAuth 2.0 protocol and suggest additional features which can augment the architectural design and enhance overall security effectiveness of the protocol. The protocol classifies third-party client apps based on their ability to maintain the confidentiality of the secret keys and the method used by them to register with the authorization servers. The protocol further offers various options to send access tokens to third-party client apps. In our research, we suggest converging these different third-party client app types and implementation options into a single uniform client app type and operational flow, thereby reducing the complexity in decision making, implementation, security misconfigurations and maintenance of different codebases. We also propose incorporating 2FA (Two Factor Authentication) as a feature for authentication of third-party client apps in addition to the existing mechanism of basic authentication through client secret. We also advocate measures to utilize digital signatures and cryptography to mitigate the common risks and vulnerabilities associated with misuse of tokens, which are issued and exchanged at various stages of the OAuth process including access to protected resources.
Privacy-Preserving Query Scheme (PPQS) for Location-Based Services in Outsourced Cloud

Pervasive smartphones boost the prosperity of location-based service (LBS) and the increasing data prompt LBS providers to outsource their LBS datasets to the cloud side. The privacy issues of LBS in the outsourced cloud scenario have attracted considerable interest recently. However, current schemes cannot provide sufficient privacy preservation against practical challenges and are little concerned about the data retrieval efficiency of the cloud side. Therefore, we present an efficient Privacy-Preserving LBS Query Scheme (i.e., <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>PPQS</mtext> </math> ). In our scheme, two cloud entities are employed to store the sensitive information of the outsourced data and provide the query service, which enhances the ability of privacy preservation for sensitive information. Besides, by using the techniques of homomorphic encryption and searchable symmetric encryption, the proposed scheme supports both the type query and the range query, which can significantly improve the data retrieval efficiency of the cloud side and reduce the computation burden on the cloud side and the user side. Through detailed analysis on security and computation cost, we show the enhanced ability of privacy preservation and the lower computation cost compared to previous schemes. Based on a real dataset, extensive simulations are performed to validate the effectiveness and performance of our scheme.
N-SVRG: Stochastic Variance Reduction Gradient with Noise Reduction Ability for Small Batch Samples

The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD.To this end, we propose a noise reduction method for Stochastic Variance Reduction gradient (SVRG), called N-SVRG, which uses small batches samples instead of all samples for the average gradient calculation, while performing an incremental update of the average gradient.In each round of iteration, a small batch of samples is randomly selected for the average gradient calculation, while the average gradient is updated by rounding of the past model gradients during internal iterations.By suitably reducing the batch size B, the memory storage as well as the number of iterations can be reduced.The experiments are compared with the state-of-the-art Mini-Batch SGD, AdaGrad, RMSProp, SVRG and SCSG, and it is demonstrated that N-SVRG outperforms SVRG and SASG, and is on par with SCSG.Finally, by exploring the relationship between the small values of different parameters n.B and k and the effectiveness of the algorithm, we prove that our N-SVRG algorithm has some stability and can achieve sufficient accuracy even in the case of small batch size.The advantages and disadvantages of various methods are experimentally compared, and the stability of N-SVRG is explored by parameter settings.
Learning Experiences in a Full 3D Conference: Relationships with 3D Factors and Innovative Mindsets

This paper aims to investigate relationships factors of 3D environment and innovative mindset with learning experiences. This reports the reflections about our learning experiences of an international conference, IEEE TALE 2020, held in a full 3D platform, based on the results of the post-conference survey. The survey was conducted for two weeks in spring 2021. Total of 30 participants responded the survey and 23 responses with the score of learning experiences were analyzed in this research. The overall score of learning experiences averaged 77.57 over 100.00 with the s.d., 17.12. The significant correlations with the learning experiences in virtual reality ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p &lt; .01$</tex> ) were found on avatar's behavior, natural environment, sponsor and venue booths, virtual banquet, information center, interactions with close and new colleagues, as well as session presentations. The results indicate that unique features of 3D platform may contribute to higher the learning experiences of the full 3D conference. As to innovative mindset, those who valued interaction with others tended to enhance their learning experience.