Reviewerly/exHarmony huggingface dataset:
Cloud Computing: Concepts and Techniques with Service Provider Analysis

Cloud computing conveys IT-related capacities as associate degree administration through the net to various shoppers and these administrations are charged enthusiastic about utilization. Several distributed computing suppliers, for instance, Google, Microsoft, Yahoo, IBM, and Amazon are moving towards the reception of cloud innovation prompting an intensive intensifying within the use of various cloud administrations. Amazon is that the pioneer during this field as a result of its progressive variety of style highlights contrasted with others. To handle the problems of cloud specialist co-ops and shoppers totally different open supply devices and business apparatuses are being created. Despite the very fact that heaps a lot of advancements have occurred in distributed computing zone, various difficulties, for instance, security, ability, plus coming up with, virtualization, so forth are however to be adjusted. This paper surveys distributed computing the worldview as way as its verifiable advancement, ideas, innovation, apparatuses, and totally different difficulties.
An Explainable Predictive Model for the Geolocation of English Tweets

With the growing impact of social networks in our daily lives, current trends towards reducing the gap between them and the physical world become more prominent. In this context, some geolocation solutions have recently been developed in order to determine the geographical appurtenance data on those networks. The majority of proposed geolocation strategies is composed of machine learning and neural network-based models. The latter are considered as black boxes, which means the complexity of understanding their structures and their behaviors by humans. This limitation is emphasized by the need to generate interpretable and explainable outputs. In this paper, we demonstrate explainability on the predictions made by an advanced neural-network-based model for tweets geolocation using LIME, a state of the art open source explainability technique. Experiences performed on a set of geotagged tweets showed the aggregation of LIME’s explanations with the geolocation model’s results by 78% when dealing with two English variants.
User Experiences Derived from Mass-Distribution Virtual Products are Integrated by the Spatio-Temporal Component

The mass-distribution virtual products affect in various dimensions to the users from the activation of the sensory mechanisms, anatomical responses until various cognitive processes. Through the observation of the inter-action processes made by a group of normal vision users in three mass-distribution virtual products with the assignment of a task was detected a constant in the processes of interaction: the spatio-temporal component. This component was present not only in the process of interaction with the virtual product but also in the process of interaction with the device’s work system. In turn the spatio-temporal component seems to have variations related in the activation of sensory mechanisms which depends on the characteristics of the object. The intention of these studies is to describe -with the use of networks- the spatio-temporal component during the interaction processes with the mass-distribution virtual products and to reflect on how this component affect the human behavior and user experiences.
Contractual Liability for the Use of AI under Dutch Law and EU Legislative Proposals

In this chapter, the contractual liabilityLiability of a company (the ‘user’) using an AI system to perform its contractual obligations is analysed from a Dutch law and EU law perspective. In particular, we discuss three defences which, in the event of a breach, the user can put forward against the attributionAttribution of that breach to such user and which relate to the characteristics of AI systems, especially their capacity for autonomous activity and self-learning: (1) the AI system was state-of-the-artState-of-the-art when deployed, (2) the user had no controlControl over the AI system, and (3) an AI system is not a tangibleTangible object and its use in the performance of contractual obligations can thus not give rise to strict liabilityLiability under Article 6:77 of the Dutch Civil Code. Following a classical legal analysis of these defences under Dutch law and in light of EU legislative proposals, the following conclusions are reached. Firstly, the user is strictly liable, subject to an exception based on unreasonableness, if the AI system was unsuitable for the purpose for which it was deployed as at the time of deployment. Advancements in scientific knowledge play no role in determining suitability. Secondly, a legislative proposal by the European ParliamentEuropean Parliament liability proposal allows the user to escape liabilityLiability for damage caused by a non-high-risk AI system if the user took due care with respect to the selection, monitoring and maintenance of that system. Thirdly, the defence that the user is not liable because an AI system is not a tangibleTangible object is unlikely to hold.
A Decision Support System for Rheumatoid Arthritis (RA) Treatment Selection and Factor Prioritization by Using Spherical Fuzzy Sets

Rheumatoid Arthritis, also abbreviated as RA, is an inflammatory and autoimmune disease that can be seen both in females and males. It is known that rheumatoid arthritis, which is an autoimmune disorder, is widely seen in joints and occurs when your immune system mistakenly attacks the body's tissues and organs including lungs, kidneys, eyes and skin. As there exist many factors causing Rheumatoid Arthritis, it is not certain which factor is the most effecting factor and it is generally difficult to organize a treatment process. Also, because of many factors causing RA and many treatment methods, it is not easy to select the best treatment alternative based on the effecting factors such as obesity, family history, age and sex. In this paper, it is aimed to prioritize the factors causing Rheumatoid Arthritis by using Spherical Fuzzy AHP and to select the best treatment alternative based on Spherical Fuzzy Sets. Furthermore, this study will provide a general and an analytical view to medical doctors for how to plan a treatment process for the patients having RA.
Non-Integral Depth Measurement of High-Aspect-Ratio Multi-Layer Microstructures Using Numerical-Aperture Shaped Beams

This work presents a new optical metrology technique for accurate depth measurement of individual high-aspect-ratio (HAR) multi-layer microstructures with high spatial resolution and signal-to-noise ratio (SNR). To resolve the limitations of existing optical metrology techniques, light shaping with high light efficiency is optimized using numerical-aperture controlled laser beams. Experimental tests evidenced a 28-fold improvement in SNR for measuring large-depth structures when compared with measurements obtained using traditional broadband incoherent illumination. More importantly, instead of integral measurement characteristics currently limited by conventional spectral reflectometry or scatterometry, non-integral depth measurement of an individual microstructure from densely spaced microstructures such as through-silicon vias (TSV) and redistribution layers (RDL) can be realized using the developed optical measuring system with desired numerical aperture and field of view achieved simultaneously. As demonstrated by the measurement of a single submicron structure with linewidth as small as 0.6 μm and an aspect ratio of 5, the precision of depth measurement can be kept within a few nanometers.
User-Friendly Embedding of Vehicle Fast Charging in Primary Vehicle Use with Persona Models

In addition to the reduction of charging times, the optimization of the charging process and its integration into the primary vehicle use is a central approach to minimize the loss of comfort for the user. In a first step, design-critical vehicle users were identified and modelled in typical user scenarios. Then, on this basis and taking into account an analysis of the strengths and weaknesses of fast-charging technology currently available on the market, the requirements definition for the development of a system for "user-friendly support in the planning of a transport task for EVs" is carried out. The focus will be on the development of a multi input device operating system that is based on a different degree of connec-tivity and automation depending on the different user types. The focus of the evaluation lies in the discussion of the correlation between the degree of connec-tivity and automation and the user types. Systematically derived operating con-cepts are evaluated in the context of subject studies.
New Discrete Chaotic Cipher Key Generation for Digital Embedded Crypto-systems

To benefit of embedded systems which are highly customized providing architectures to suit real-time computing, optimized unit size and low power consumption require highest levels of data communication security which are very useful advantage for telecommunications and networks and Internet of Things (IoT) communication applications that handle sensitive information. This paper presents an extracted new 5-dimensional (5-D) discrete time chaos system to generate a robust chaotic cipher data stream to ensure encryption application for secure communication. Dynamical behaviors and security analysis are investigated and compared to current discrete chaotic maps proving its suitability for embedded data encryption systems. Field-Programmable Gate Array (FPGA) implementation design shows better performance and good security robustness compared with previous works while proving the performance improvement of the proposed cipher block in terms of throughput, used hardware logic resources, and resistance against most cryptanalysis attacks.
Communication Between Students and Teachers in a Distance Learning Environment During the COVID-19 Pandemic in Serbia

The COVID-19 pandemic affected the work of educational institutions, which had to adapt to new circumstances. The authors investigated the impact of online learning on student-teacher communication at the Department of Business Studies in Blace in Serbia. The aim of the authors is to analyze how online learning affects the communication between students and teachers and investigate the level of productivity and creativity of students in the new environment. The research study was conducted through a structured online survey using a random sampling technique. In the traditional way of organizing classes, face-to-face classes, students are more motivated and engaged in learning, and deprived of a sense of social isolation. The results of the research prove that students need more frequent interactive dialogue with teachers through formal and informal communication channels. Also, the authors will suggest possible ways to improve the productivity and creativity of students in the online environment.
An Elaborative Approach for the Histopathological Classification of the Breast Cancer using Residual Neural Networks

Breast cancer is the most conventional type of cancer that takes place predominantly in women. Whenever a transition is made in DNA, then it is confirmed that it is caused by cancer. Nowadays, the rate at which this disease is spreading is highly exponential. Currently, it has become a number one morbidity that can happen to women. Tumor in the human body can be detected in early stages using mammography, ultrasound, biopsy, etc., by performing necessary procedures and can be prevented in the early stages. Breast cancer detection in early stages is very crucial, otherwise there is chance of spreading of cancer cells all over the body. The proposed solution is used to identify whether the tumor present in the body is benign which means harmless or malignant. In this paper, the histopathological images are used as the dataset for the classification and detection of breast cancer. The images can be easily downloaded from the public domain. Transfer learning techniques are used to train the model, and later, a machine learning classifier is applied to achieve better results. The proposed model adapts usage of the residual neural network along with the linear support vector machine classifier. Linear SVM is used for binary class classification as it improves the performance of the model by rewarding with substantial accuracy. The final accuracy achieved by implementing this model is 96.92%.
Classification Methods Based on Fitting Logistic Regression to Positive and Unlabeled Data

In our work, we consider the classification methods based on the model of logistic regression for positive and unlabeled data. We examine the following four methods of the posterior probability estimation, where the risk of logistic loss function is optimized, namely: the naive approach, the weighted likelihood approach, as well as the quite recently proposed methods - the joint approach, and the LassoJoint method. The objective of our study is to evaluate the accuracy, the recall, the precision and the F1-score of the considered classification methods. The corresponding assessments have been carried out on 13 machine learning model schemes by conducting some numerical experiments on selected real datasets.
DIGITALIZATION OF EDUCATION: PROBLEMS OF MANAGEMENT AND SECURITY

The general process of globalization is being replaced by the process of digitalization, which is increasingly gaining the scale of its influence on all spheres of society. The purpose of the article is to analyze the problems of digital security as a consequence of the use of information and communication technologies in the field of education and science. To identify the main problems and trends related to the issues of digitalization of the educational system and ensuring digital security, methods were used: document analysis, comparative and system analysis. In May 2021, the authors conducted a pilot study with cluster sampling parameters, in which 320 students from Moscow universities took part, using a survey on the Google Form platform. As a result, it was revealed that the theory of digital development of society focuses on the increasing value of information and digital technologies, mostly due to the introduction of artificial intelligence technologies in all spheres of society. The need to teach the basics of digital security from school is becoming urgent, and professional education should not only fully meet the current challenges of the digital age, but also fulfill its traditional functions in society.
The City Information Visual Based on ArcGIS Technology

The 3D visual of urban information is the prerequisite and foundation for building a digital city, and the consistency of the second and 3D GIS platforms makes the digital city application system have a wider application prospect. Analyze various 3D visual software and take ArcGIS as an example to discuss the 3D visual of city information and the corresponding spatial analysis, and study the corresponding 3D modeling process and related technical issues.
Investigating Users’ Perception, Security Awareness and Cyber-Hygiene Behaviour Concerning QR Code as an Attack Vector

A quick response (QR) code is a two-dimensional barcode that is used for displaying or scanning information. Nowadays, it is exploited for cyber-fraud due to its mass adoption and non-textual nature. In this survey paper, we conducted an anonymous survey of 132 people with basic familiarity with QR codes to investigate and understand users’ awareness, perception, and cyber-hygiene behaviour towards QR codes and its malicious use. Analysis of the survey data indicates that more than half of the participants have security concerns about using a QR code, however they still use it. More than one third of the total participants have no such concerns. A similar percentage of the participants were unaware of any security risk associated with QR codes. Moreover, we found bias towards certain factors by the participants for classifying a QR code as benign or malicious, for example, presence of brand logo, higher pixel density and larger QR size are assumed to indicate a trustworthy QR code. Our findings indicate that there is a noticeable gap between how users perceive QR code-based threats and their actual security awareness related to them, and thus require technology-based assistance.
Digital Signature Schemes

In this chapter we show how public key cryptosystems can be used to create “digital” signatures.
Enhancing sentiment analysis performance with blend of features and deep learning algorithms

In Sentiment analysis, writer&apos;s view about products, events, government policies, services, topics, organizations, and individuals is determined through the text written by them on social media platforms like Twitter, and Facebook. Previous researchers focused on getting sentiment either from syntactic and lexical features or by using pre-trained word embeddings. We have combined GloVe pre-trained word embedding,lexicon based features, and Bag of words to form an ensemble feature(FE). We have considered two Twitter datasets namely, US Airlines Twitter, and Stanford Twitter Sentiment Gold (STSGd). We have applied three machine learning algorithms on TF-IDF features, three deep learning algorithms on GloVe word embedding feature. We have proposed a hybrid model which is a combination of LSTM and CNN model with ensemble feature. The proposed model has performed around 2% better on US Airlines Twitter dataset and around 8% better on STS-Gold dataset than state-of-the-art-results. It is observed that ensemble feature improved the performance of algorithms.
A Study on Audio Quality of Experience for WebRTC-Based Communication Applications

Users of communication applications always want uninterrupted and high-quality sound. It is possible to provide these requests by determining the quality experienced by the end-user. Under different network conditions, it is important to identify the effects of parameters and possible scenarios in order to select the most optimal Opus codec configuration in real-time. In this way, it is possible to establish an adaptive configuration structure. This study focuses on the effects of Opus audio codec within an application that uses the WebRTC library. When the call started, test scenarios were created to investigate the effects of changing Opus parameters, and the results were discussed.
INTEGRATION AS A TREND IN THE DEVELOPMENT OF HIGHER PEDAGOGICAL EDUCATION

The article reveals the main reasons for the decline in the quality of training of future teachers in the Republic of Ka- zakhstan. One of which is the lack of coordination (integrity) of actions between teachers of a pedagogical university who read various cycles of disciplines in it (general education (OOD); basic (DB); profiling (PD)). This provision is stated in the article as the presence of an intra-system contradiction – a contradiction between parts of the whole. The authors of the article stand on the position that outside of the resolution of this contradiction, the system will be destroyed. In accordance with this, the importance of the implementation of integration in the educational process of a pedagogical university is actualized, as an important condition for creating a unified educational space, as the most effective form of implementing tasks aimed at improving the quality of higher pedagogical education.
Improving Relation Classification Using Relation Hierarchy

The relation classification (RC) task classifies a relation present between two target entities in a given context. It is an important task of information extraction and plays a significant role in several NLP applications. Most of the existing studies consider relation classes as a flat list of classes and thus ignore hierarchical relation between classes. This study explores the application of relation hierarchy in improving relation classification performance. In particular, we focus on the following two applications of relation hierarchy: (i) detecting noisy instances; and (ii) modifying the cross-entropy (CE) loss function. We use TACRED, the most widely used RC dataset for this purpose. We build a taxonomical relation hierarchy over the relation classes of TACRED and use it for filtering and relabeling ambiguous or noisy instances of TACRED. For better optimization, we also introduce hierarchical-distance scaled cross-entropy loss (HCE Loss), using the shortest-path distance between ground truth and predicted label for scaling cross-entropy loss. Our extensive empirical analyses indicate that relation hierarchy-inspired filtering, relabeling, and the HCE loss help in improving the relation classification.
Fuzzy Clustering Based Association Rule Mining: A Case Study on Ecommerce

Association rule mining (ARM) refers to a procedure that focuses on finding frequent patterns in various data sources. The most commonly used area is the retail sales data and rules which show an association between sales of two products are investigated. To this end, sales data is preprocessed, and algorithms are used to find the association rules by using the specific threshold values of Support and Confidence parameters. In this study, we investigated the effects of using fuzzy clustering with association rule mining. A case study from the E-commerce area is selected and sales data for a specific period is analyzed. First ARM is applied to the whole data, then the data is segmented by using Fuzzy Clustering, and ARM is applied to all segments. Later the resulting rules are compared and the effects of segmentation on ARM results are analyzed.
Designing a mobile based-application using Apriori algorithm: A Business development strategy during pandemic COVID-19

It is very crucial for retailers to improve their marketing and customer relationship management strategies.Hence, this paper will cover the topic about how to utilize the association rule method to analyse the customers' purchasing behaviour and patterns based on market basket analysis.Association rule method can find the relationships between large data set of secondary files from several e-commerce platforms.This method can be performed by utilizing the cutting-edge data innovation and programming applications like data mining and Apriori algorithm libraries of Python programming language in Anaconda Navigator.By applying this Apriori algorithm, the transaction data obtained in case studies becomes easier to manage.
Selective Word Substitution for Contextualized Data Augmentation

The often observed unavailability of large amounts of training data typically required by deep learning models to perform well in the context of NLP tasks has given rise to the exploration of data augmentation techniques. Originally, such techniques mainly focused on rule-based methods (e.g. random insertion/deletion of words) or synonym replacement with the help of lexicons. More recently, model-based techniques which involve the use of non-contextual (e.g. Word2Vec, GloVe) or contextual (e.g. BERT) embeddings seem to be gaining ground as a more effective way of word replacement. For BERT, in particular, which has been employed successfully in various NLP tasks, data augmentation is typically performed by applying a masking approach where an arbitrary number of word positions is selected to replace words with others of the same meaning. Considering that the words selected for substitution are bound to affect the final outcome, this work examines different ways of selecting the words to be replaced by emphasizing different parts of a sentence, namely specific parts of speech or words that carry more sentiment information. Our goal is to study the effect of selecting the words to be substituted during data augmentation on the final performance of a classification model. Evaluation experiments performed for binary classification tasks on two benchmark datasets indicate improvements in the effectiveness against state-of-the-art baselines.