Reviewerly/exHarmony huggingface dataset:
FinTech: Deep Learning-Based Sentiment Classification of User Reviews from Various Bangladeshi Mobile Financial Services

Banking has become an integral part of our lives. Fintech (Financial Technology) skyrocketed the number of people willing to use Mobile Financial Services (MFS) for their daily financial transactions. The banks are providing their services via mobile applications, which can be found on the Google Play Store. These Mobile Financial Services (MFS) provide mobility and increase efficiency by 10-fold. With an astonishing number of users came an abundant number of reviews for these apps. User reviews are the backbone of an application's success. They provide information about hands-on experience. This study mainly focuses on the reactions of the users of such apps. Sentiment analysis is being used to draw out emotions from the users based on their written reviews. The primary goal of this paper is to examine the points of view of such application users. A total of 5414 pieces of data were collected from the Google Play Store and classified as negative, neutral, or positive. The data model has been evaluated using CNN, LSTM, and BiLSTM algorithms. Compared to CNN and LSTM, the BiLSTM algorithm produced the best model with an accuracy of 97.07%.
Cloud Modernization and Migration

The objective of this chapter is to introduce the reader to the fundamentals of cloud migrationCloud migration. It describes cloud migrationCloud migration process and different migration strategiesMigration strategies. At the end of this chapter a reader can understand different phases of migration processMigration process and methods.
Edge Detection and Huffman Encoding Based Image Steganography with High Data Embedding Capacity

In the present study, an edge detector and Huffman encoding based image-steganography approach for high data embedding capacity is presented. High frequency pixel region is exploited to preserve the imperceptibility characteristics of the stego-image and cover-image. Confidential data is encoded with Huffman encoding based technique. Finally, 2n correction is applied to maintain the visual quality. A security key enhances the robustness of embedding and extraction process of the confidential message. The proficiency of the method is also collated with already proposed techniques with various evaluation parameters.
Synonym-Substitution Algorithms for Enriching the Agrovoc Vocabulary

Background: Synonym-substitution algorithms analyze the relationships between words and phrases and identify synonyms and related terms. They consider factors like context and subject area to enhance the clarity and accessibility of ontologies. These algorithms provide a wider range of vocabulary options to reduce ambiguity and improve search accuracy, facilitate cross-referencing, and integrate information across different domains. Results: The objective of this study is to enhance the Agrovoc ontology by identifying new names for existing concepts and discovering additional relevant concepts not yet included in the ontology. The study will utilize the lexical properties, hierarchical structure, and logical organization of ontology terms to discover fresh names for established concepts. Meanwhile, unsupervised keyphrase extraction methods will be utilized to identify new concepts. In both cases, the newly learned names will be checked against the AGRIS database to eliminate any illogical candidates. To validate the results, the study generated sentences using both the keywords and new synonyms and utilized BERT for the Agricultural Domain to calculate vector encodings for each sentence. Cosine similarity was then applied to compare the encoding vectors between the keywords and new synonyms. After testing 2426 new synonym-substitution keywords, 2009 was found to be a match. Conclusion: According to the findings, utilizing the synonym-substitution technique to create new synonyms has a favorable effect on the enhancement and expansion of AGROVOC by acquiring fresh and relevant ideas that were previously missing from the ontology.
Towards desirable decision boundary by Moderate-Margin Adversarial Training

The previous adversarial training methods tended to use a larger uniform perturbation budget to obtain an inclusive decision boundary, which improved robustness. However, this large uniform perturbation budget will bring an unnecessary increase in the margin along adversarial directions, causing heavy cross-over between natural and adversarial examples. It is not conducive to balancing the trade-off between robustness and natural accuracy. In this paper, we propose a novel adversarial training scheme, namely Moderate-Margin Adversarial Training (MMAT), to achieve a better trade-off. Specifically, we generate finer-grained adversarial examples to mitigate the cross-over between them and natural examples of neighboring classes. Meanwhile, we design a hybrid loss to learn adversarial examples and natural examples respectively to further obtain a moderate decision boundary. Extensive experiments show MMAT achieves high natural accuracy and robustness under both black-box and white-box attacks. Especially, state-of-the-art robustness and natural accuracy are achieved on SVHN.
User Scheduling and Clustering for Distributed Antenna Network Using Quantum Computing

In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.
Impact of digital skills of mathematics teachers to promote students’ communication behavior in the classroom

The purpose of this study was to measure the effect of classroom practices of using communication tools, collaboration skills, digital skills, and software skills of teachers on the communication behaviors of students during mathematics instruction. A cross-sectional online survey was conducted among 466 mathematics teachers in Nepal. The primary statistical techniques applied in the data analysis were mean, standard deviation, one-sample t-test, and structural equation modeling (SEM). The results showed that the level of skill transformations of mathematics teachers in digital skills was found to be significantly low. Moreover, practices of using communication tools, collaborative skills of teachers, digital skills enhancement of teachers, and software skills enhancement of teachers were found to be significant predictors of the communication behavior of students. The results of this study suggested that teachers’ technological empowerment is essential for developing digitally competent teachers who can transform the traditional mathematics classrooms into an online mode that is more constructive, collaborative, engaging, and supportive to the learners in a flexible and joyful learning environment. The study contributes to providing the knowledge of digital instructional skills of mathematics teachers to the communication behavior of the students. Moreover, the study gives an insight into using multi-group SEM in studying teachers’ technological skills on students’ learning of soft skills, such as communication behavior.
Сценарій змішаного навчання світової літератури у процесі вивчення теми «Tворчість Aдама Mіцкевича»

The aim of the paper is to develop a scenario of blended learning of World Literature based on studying the topic “Creativity of Adam Mickiewicz”. It uses the popular rotating model of “inverted class” and various forms of work, such as lecture, test, individual tasks, practical session, video conference, and questionnaire. The proposed scenario was integrated into the MOODLE distance learning platform of Donbas State Pedagogical University (Slovyansk, Ukraine). In short, blended learning requires the creation of educational and methodological support that meets modern quality standards for the provision of educational services – accessibility, interactivity, multimediality. Blended learning technologies make it possible to apply a wide range of forms and teaching aids that contribute to the personalization of the educational process, make it possible to implement the entire range of elements of mastering the material (from knowledge to assessment), and activate the search activity of not only students, but also lecturers.
Data Reconstruction Attack with Label Guessing for Federated Learning

&lt;p&gt;In light of recent advancements in deep and machine learning, federated learning has been proposed as a means to prevent privacy invasion. However, a reconstruction attack that exploits gradients to leak learning data has recently been developed. With increasing research into federated learning and the importance of data usage, it is crucial to prepare for such attacks. Specifically, when face data are used in federated learning, the damage caused by privacy infringement can be significant. Therefore, attack studies are necessary to develop effective defense strategies against these attacks. In this study, we propose a new attack method that uses labels to achieve faster and more accurate reconstruction performance than previous reconstruction attacks. We demonstrate the effectiveness of our proposed method on the Yale Face Database B, MNIST, and CIFAR-10 datasets, as well as under non-IID conditions, similar to real federated learning. The results show that our proposed method outperforms random labeling in terms of reconstruction performance in all evaluations for MNIST and CIFAR-10 datasets in round 1.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
A test for the absence of aliasing or white noise in two-dimensional locally stationary wavelet processes

Abstract Either intentionally or unintentionally, sub-sampling is a common occurrence in image processing and can lead to aliasing if the highest frequency in the underlying process is higher than the Nyquist frequency. Several techniques have already been suggest in order to prevent aliasing from occurring (for example applying anti-aliasing filters), however there is little work describing methods to detect for it. Recently, Eckley and Nason (Biometrika 105(4), 833–848, 2018) developed a test for the absence of aliasing and/or white noise in locally stationary wavelet processes. Following Eckley and Nason (Biometrika 105(4), 833–848, 2018), we derive the corresponding theoretical consequences of sub-sampling a two-dimensional locally stationary wavelet process and develop a procedure to test for the absence of aliasing and/or white noise confounding at a fixed point, demonstrating its effectiveness and use through appropriate simulation studies and an example. In addition, we outline some possibilities for extending these methods further, from images to videos.
Convex-hull based robust evolutionary optimization approach for ROC maximization under label noise

Convex-hull based receiver operating characteristic (ROC) maximization has become a hot research topic due to its significance in the study of imbalance binary classification. Recently, a series of multi-objective evolutionary algorithms (MOEAs) have been proposed to maximize ROC convex hull by regarding it as a multi-objective optimization problem. However, in real applications, the ubiquitous label noises degrade the performance of the existing MOEAs. To address this issue, in this paper, we propose a robust evolutionary optimization approach, named REO, to enhance the robustness of the existing MOEAs. In the proposed approach, we firstly design a distance-based samples selection method to extract a “clean” data subset, aiming to obtain an ideal individual. Second, with the ideal individual, a problem-oriented two-stage adaptive updating strategy is designed to guide the population evolution and enhance the robustness of MOEAs. Specifically, in the first stage, based on the achieved ideal individual, a bi-level evolution direction is constructed to provide the guidance for the evolution of population. In the second stage, we utilize the cosine similarity to assign different step sizes to adaptively update the inferior individuals. Experimental results on 19 complicated datasets with different noise levels show that the proposed REO approach can effectively enhance the robustness of the existing MOEAs for ROC convex hull maximization under the label noises.
A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.
Fine-tuning language models to recognize semantic relations

Abstract Transformer-based pre-trained Language Models (PLMs) have emerged as the foundations for the current state-of-the-art algorithms in most natural language processing tasks, in particular when applied to context rich data such as sentences or paragraphs. However, their impact on the tasks defined in terms of abstract individual word properties, not necessary tied to their specific use in a particular sentence, has been inadequately explored, which is a notable research gap. Addressing this gap is crucial for advancing our understanding of natural language processing. To fill this void, we concentrate on classification of semantic relations: given a pair of concepts (words or word sequences) the aim is to identify the semantic label to describe their relationship. E.g. in the case of the pair green/colour , “is a” is a suitable relation while “part of”, “property of”, and “opposite of” are not suitable. This classification is independent of a particular sentence in which these concepts might have been used. We are first to incorporate a language model into both existing approaches to this task, namely path-based and distribution-based methods. Our transformer-based approaches exhibit significant improvements over the state-of-the-art and come remarkably close to achieving human-level performance on rigorous benchmarks. We are also first to provide evidence that the standard datasets over-state the performance due to the effect of “lexical memorisation.” We reduce this effect by applying lexical separation. On the new benchmark datasets, the algorithmic performance remains significantly below human-level, highlighting that the task of semantic relation classification is still unresolved, particularly for language models of the sizes commonly used at the time of our study. We also identify additional challenges that PLM-based approaches face and conduct extensive ablation studies and other experiments to investigate the sensitivity of our findings to specific modelling and implementation choices. Furthermore, we examine the specific relations that pose greater challenges and discuss the trade-offs between accuracy and processing time.
Research on Unifying Business and Testing with Cucumber Automation

The synchronization of business goals with software testing procedures is essential for producing high-quality, dependable software solutions in today's quickly changing business environment. A major difficulty today is bridging the gap between testing teams and business stakeholders. This study examines how Cucumber automation can be used to integrate business and testing practices. A well-liked behaviour-driven development (BDD) framework called Cucumber offers a distinctive method that makes it possible to collaborate effectively, improve communication, and guarantee the delivery of software solutions that are in line with company needs. This article discusses the advantages, difficulties, and effective implementation techniques of Cucumber automation in bridging the gap between businesses and testing through an analysis of actual case studies and industry best practices. The results highlight the significance of having a collaborative mind-set, having clear communication, and having a well-defined framework for using Cucumber automation. The research comes to a close with suggestions for future work and potential improvements in leveraging Cucumber automation to further strengthen the fusion of business and testing practices, ultimately resulting in higher software quality and client satisfaction
Kecakapan Soft Skill dan Hard Skill bagi Akuntan Profesional dan Andal

In the 4.0 era, individuals must present new skills in all activities carried out. Forms of skills that are needed at this time are soft skills and hard skills. This study was conducted with the aim of providing an overview and persuasion that an accountant must create a character in his field through mastery of competence and mastery of supporting soft skills and hard skills in order to be able to carry out his profession professionally and reliably. The capability of accountants regarding soft skills and hard skills must be developed in order to help wisely respond to problems that arise suddenly in the world of work and provide a comprehensive solution to these problems armed with the competencies and skills of the soft skills and hard skills they have. To overcome it, an analysis needs to be carried out to find out what and how important a person's capabilities are in the world of work. This study uses literature review methods. The results found that accounting students or an accountant need to have soft skills and hard skills including future skills so they are not eliminated in the world of work and can compete with other workers.
PODCAST STORYTELLING: A NEW WAY OF TOURISM MARKETING IN INDONESIA

This study explores the trend of using podcasts and storytelling approaches as a new way to market regional tourism, in Indonesia. In the last two years, there has been a significant increase in access to audio on demand content in Indonesia. The use of new media such as podcasts in the promotion of the tourism sector is inevitable. Research on podcasts in Indonesia, especially tourism podcasts, is still not prevalent. However, the use of podcast platforms by actors in the tourism sector is starting to emerge. The storytelling approach is also increasingly important for the tourism sector because a good authentic story has the potential to create a better sense of place and connections. This study seeks to explore the opportunities and challenges of tourism podcasts, as well as the importance of the storytelling approach for marketing regional tourism in Indonesia. The method used in this study is a literature review which includes the process of reading and analyzing articles in journals, previous research reports, reference books and news related to podcasts. The results of the study show that podcast is an important marketing tool to enrich information on tourist destinations in Indonesia, and of course help encourage regional tourism marketing. This research is expected to be an input and recommendation to practitioners, government, communities, private sector and researchers to work together to equip tourism activities in various regions with podcasting and storytelling skills as a new way to develop tourism marketing in Indonesia. &#x0D; &#x0D;
Research on time series forecasting method of ion concentration in produced fluid

Abstract In the research on strong alkali‐surfactant‐polymer flooding scaling prediction, the variation characteristics of ion concentration in the produced fluid are obviously consistent with those of scale component content. Consequently, studying the variation tendency of ion concentration in produced fluid is helpful for further revealing the scaling tendency. Given the periodicity and chaos characteristics of the ion concentration data of the produced fluid, an echo state network (ESN) is used to realize the relevant time series forecasting. Simultaneously, to cope with the failure of the ESN in selecting suitable reservoir parameters according to different characteristics of time series, a modified discrete particle swarm optimization algorithm based on objective space decomposition is used to optimize the reservoir parameters. The experimental results indicate that the improved ESN presents the lowest error and is the closest to the target value.