Reviewerly/exHarmony huggingface dataset:
Explaining geospatial variation in mobile phone ownership among rural women of Bangladesh: A multi-level and multidimensional approach

Despite significant progress in Information and communication technologies (ICTs), rural dwellers of Bangladesh are still less fortunate when it comes to availing the improved ICT facilities compared to their urban counterparts, and this digital divide is more evident in the case of women. Mobile phone ownership (MPO) can play a key role in bridging this digital divide and achieving the Sustainable Development Goals of “End poverty in all its forms everywhere”, “Achieve gender equality and empower all women and girls”, and “Ensure healthy lives and promote well-being for all at all ages”. Administrative district-level infrastructural and societal readiness to women's MPO in rural settings of Bangladesh may also influence the geospatial variation in their MPO status, which is still unexplained. In the context of rural Bangladesh, household heads substantially exert influence over women's decision-making process. However, the role of household heads' age and education on women's MPO is mostly unexplored. As in developing countries, women's MPO does not ensure its usage, investigating the possibilities of women's MPO on its usage has immense importance. Therefore, this study aims to revisit the correlates of rural women's MPO in Bangladesh and explain the administrative district-level geospatial variation in their MPO by controlling the effect of individual and household-level sociodemographic correlates of MPO. Further, this study attempts to investigate the possibilities of MPO on the extent of its usage. This study used the latest nationally representative cross-sectional data from Bangladesh Multiple Indicator Cluster Survey 2019. This study reveals that the district-level readiness was a potential source of geospatial variation in the prevalence of rural women's MPO in Bangladesh. The lowest level of readiness was noticed in north-western Bangladesh. Comparatively elderly women with better education and media exposure had a considerably higher chance of MPO. Elderly household heads, especially male and less educated heads, hindered the MPO of women. This study identified MPO as a key determinant of its extent of usage. Moreover, to increase the MPO by a faster pace, strategies should target less empowered women, particularly those who lived in districts of lower readiness.
Language cognition and language computation &amp;mdash; human and machine language understanding

Language understanding is the key scientific issue in cognitive science and computer science. However, the two disciplines are quite different in selecting specific research questions. Research in cognitive science focuses on analyzing the working mechanism of the brain and describing its response to language, while only a few research studies investigate the brain's language system as a whole. In contrast, computer scientists focus on the efficiency of practical applications when choosing research questions and often ignore essential laws of language. Thus, the research question becomes: “Can we combine these two disciplines to develop new insights into building intelligent language models and studying cognitive mechanisms?" This paper first reviews the research question, history, and methods of language understanding in cognitive science and computer science, focusing on the current research status and challenges. Then, it compares the commonalities and differences in language understanding between the two fields. Finally, it analyzes the existing work that combines the insights from language cognition and computation and prospects the future development trend.
Improving performance of Recurrent Neural Networks for Question-Answering with Attention-based Context Reduction

In this paper a method is introduced to reduce the context for a context-based question-answering model. Natural Language Processing (NLP) has progressed significantly in last few years. Question-Answering (QA) is a task of Text Analysis which in turn is a large field of NLP. There are many QA models produced till date. Though these models were successful in performing various QA tasks with significant accuracy, these models are based on key-value memory networks and have large external memory requirement. The main motivation of the research is to overcome the limitations of the previous models of excessive memory usage. The proposed algorithm uses attention for context reduction. By focusing on reduced context, only a limited set of sentences have to be processed which reduces the external memory requirement and speeds up the inference time. The method can be used with any existing end-to-end training model thus requiring no additional supervision. When used with an RNN model, it reduces the memory requirement of the model and also decreases the training time. The proposed method also provides evidences in support of the answer.
Artifact Analysis of Smell Evolution and Maintenance Tasks in Simulink Models

Bad smells often indicate a possible concern in a software design that may present challenges related to comprehension and maintenance. As a system evolves through a series of changes and maintenance activities, the bad smells embedded in the system may also evolve with the potential for introducing additional new smells. Existing bad smells research often targets textual code-based implementations. We found very little research on bad smells in systems designed with graphical languages that are used often in industry. This paper presents our analysis on the evolution of four bad smells in 575 Simulink models across 31 open-source repositories. We conducted our analysis by creating a chain of model-driven tools that could assist with various analysis needs. Our first step was to extract the evolution history of Simulink models in GitHub. Next, we manually classified each version to a maintenance category (i.e., adaptive, preventive, corrective, or perfective). Then, we developed queries to detect instances of four selected bad smells. Finally, we analysed the evolution of each of the smells across the version history of the repositories, the relationships between the smells and the size of the models, and the impact of maintenance activities on the evolution of the identified bad smells. The results suggest that: 1) larger models tend to contain more types of smells, 2) an increase in the instances of smells is usually associated with an increase in model size, but an increase in model size does not necessarily imply an increase in the number of smells, 3) the majority of bad smells are introduced during the initial construction of the models, although a significant portion of the smells are introduced at later stages, and 4) adaptive maintenance tasks often lead to an increase in the number of smells in Simulink models, but corrective maintenance tasks often correlate with a decrease in the number of smells.
Composer's Philosophy: The Interdependence Between Worldview and Writing Technique

The composer?s philosophical thinking category is actually significant; a stable approach to the phe- nomenon under study has not developed to this day. This phenomenon is a complex socially conditioned mechanism of creativity, which entails understanding and clarifying several philosophical and cultural cat- egories, such as thinking, consciousness, worldview, style, writing technique, concept, cultural values. This research reveals the essence of such a phenomenon as the composer?s philosophy of thinking in the interdependence?s aspect of the worldview and writing technique. The fundamental approaches, methods and principles of research in composer?s philosophical thinking area formed the methodological basis of this scientific article. The manifestations of the composer?s thinking as an aspect of writing technique (thinking-writing) are considered. The philosophical type of worldview in the composer?s work has been substantiated, and the primary forms of their manifestation in the musical text have been determined.
The prediction algorithm of credit risk of science and technology finance based on cloud computing

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.
Mathematical model of gas consumption process in the form of cyclic random process

In the article, a new mathematical model of gas consumption process is considered. This model is presented as the sum of three components: a cyclic random process, a trend component and a stochastic balance. Two methods of stochastic processing of the cyclic process of gas consumption based on the known model of stochastically periodic random process and cyclic random process as models of the cyclic component of the gas consumption process are used in the research. Application of the mathematical model of the cyclic component in the form of cyclic random process allowed to obtain an estimate of the variance in the cycle of the gas consumption process, provided the cyclic component is segmented by depressions, which is much smaller than the obtained variance estimate for the application of the mathematical model as a stochastically periodic random process. This indicates more accurate processing of the gas consumption process. The obtained stochastic estimates can be used in computer and simulation modeling, as well as in analysis and forecasting of gas consumption process in further research.
Introduction to the Special Issue on value and waste in software engineering

In the context of software engineering, “value” and “waste” can mean different things to different stakeholders. While traditionally value and waste have been considered from a business or economic point of view, there has been a trend in recent years towards a broader perspective that also includes wider human and societal values. This Special Issue explores value and waste aspects in all areas of software engineering, including identifying, quantifying, reasoning about, and representing value and waste, driving value and avoiding waste, and managing value and waste. In this editorial we provide an introduction to the topic and provide an overview of the contributions included in this Special Issue.
Development of a Multiple-Choice Writing Examination Validated with a Cognitive Diagnosis Model

The aim of this study was to explore the possibility of diagnosing second language (L2) learners’ writing skills through a multiple-choice examination by applying a CDM (cognitive diagnosis model). L2 learners’ writing abilities have mostly been assessed using direct methods such as essay tests. However, this method not only requires a large amount of human raters’ labor, but also faces several issues such as intra-rater and inter-rater reliabilities. Essay examinations are also hard to validate because they are limited in checking the internal structure (i.e., conducting factor analysis). To compensate for these limitations, this study developed a multiple-choice examination and validated it with factor analysis and a CDM. The examination consisted of 20 items on key L2 writing components: content, organization, grammar, vocabulary, and mechanics. It was conducted on 109 college students. The CDM analysis revealed that the test was valid based on response processes. It provided substantial information about each test-taker’s various skills. These results imply that L2 learners’ writing abilities can be assessed in an indirect way and that the test can be conducted with a large body of students to provide useful information regarding their writing abilities at the same time.
Blockchain Technology

Blockchain is a distributed decentralized peer-to-peer network aiming to facilitate the immutability and security of data. Towards the service orientation, blockchain is a collection of distributed blocks having unique hash codes without any point of failure. Each block is stored on distributed ledgers, and transactions with them are secure, transparent, immutable, and traceable. To create a new block and allow a transaction to complete, an agreement between all parties is required. To reach an agreement in a blockchain network, consensus algorithms are used. In this chapter, fundamental principles and algorithms of blockchain networks have been discussed, and a detailed review of the blockchain consensus algorithms PoW, PoS, DPoS, PoET, PoWeight, PoB, PoA, and PoC have been provided including the merits and demerits of consensus algorithms with analysis to provide a deep understanding of the current research trends and future challenges.
Sample Representativeness Estimation as a Preliminary Stage of Statistical Steganalysis

The criterion of the test sample representativeness under the condition of a determined reliability and accuracy is described. It based on the proof of the independence of the proposed distribution from the distribution of the mathematical expectation and variance. The mathematical apparatus of matrix theory and probability theory is used.
Virtualizing Human Conversation: Two Case Studies

Collectively Sharing Human Conversation is the name of a digital platform that can be employed to capture sounds from human conversation. In this paper, we present two case studies on the use of this platform. The case studies are focused on services that investigate the opportunities created by virtualizing human conversation. The first case study is Talking Daily Thing, in which objects commonly found in a living room offer various forms of talk. The second case study examines GosoGoso GachaGacha, which offers onomatopoeic sounds as a type of music. The case studies reveal possible applications of virtualized human conversation in the form of digital services and thus new opportunities in the development of such innovative digital services.
Potential Applications of Blockchain Technology in the Construction Sector

Blockchain technology is a new digital technology that has become widespread in various industrial, social and business sectors in recent years. The basic principles specific to the functional attributes of blockchain technology and secure digital infrastructure can be applied in almost every industry. Blockchain aims to create a system that is stable, self-regulating and self-monitoring and provides the facilitation for and protection of really efficient data exchange. This research work surveys blockchain technology and its potential applications in the construction sector. The chapter examines the basics of blockchain, its current advances and categorization. In addition, the study explores the potential use of blockchain to improve the automation of construction processes and discusses future research areas. The applied approach of the research is content analysis of scientific publications, practical examples and case studies, concerning the present applications of blockchain technology in the construction sector. The findings show that blockchain has tremendous potential to be a highly positive force for change in construction. The main conclusion of the study is that using blockchain can address many current concerns in the sector such as data security, privacy and speed of transactions, and provide better construction project management, ensuring a more efficient experience for all participating professionals and clients.
Utilization of Multi-Criteria Decision-Making for Emergency Management

When emergencies or disasters strike, decision-making is a critical component in emergency management. One area of emergency management is ensuring that vulnerable communities are identified and can get the aid they need before, during, and after emergency events. Artificial Intelligence (AI) can be leveraged to improve decision-making in dynamic and complex situations. We propose that Multi-Criteria Decision-Making (MCDM), specifically a hybrid methodology of AHP-TOPSIS, is an approach that can be utilized in AI that can help evaluate, prioritize, and select the most favorable alternative based on computation of the criteria. A study was conducted considering the positive COVID-19 cases in randomly selected counties in three states – Texas, California, and Oklahoma – that have historically experienced the most declared emergencies. The empirical results from the three cases (one case for each state) demonstrate the superiority of the AHP-TOPSIS approach.
Classification of chain rings

&lt;abstract&gt;&lt;p&gt;An associative Artinian ring with an identity is a chain ring if its lattice of left (right) ideals forms a unique chain. In this article, we first prove that for every chain ring, there exists a certain finite commutative chain subring which characterizes it. Using this fact, we classify chain rings with invariants $ p, n, r, k, k', m $ up to isomorphism by finite commutative chain rings ($ k' = 1 $). Thus the classification of chain rings is reduced to that of finite commutative chain rings.&lt;/p&gt;&lt;/abstract&gt;
Automatic Stylized Plaid Check Pattern Try-on

In ithe nterest of pleasing the needs of customers finding new styles in fashion, we present a novel method that is capable of creating novel plaid and check patterned shirts based on consumer taste integrated with well-known art piece, as well as giving a predicted look of the shirt on the customer. This is done through a combination of three existing deep neural networks [1]–[3]. Since public stores are closing due to the COVID-19, customers are unable to try out apparel before purchasing. Qualitative experiments demonstrate the effectiveness of the proposed method. We believe that our service meets the need of frustrated customers in the pandemic of COVID-19.
Development of Blockchain-Based Traceability System for Fishery Products

Tracking fishery products in large archipelago countries, such as Indonesia, is a challenging task. Data quality and data integration of existing traceability systems are some of the major issues, which may lead to a trustworthiness problem. In the last few years, the Ministry of Marine Affairs and Fisheries of the Republic of Indonesia has developed systems to manage fishery products’ traceability. Although, traceability systems developed using centralized systems may have better performance and lower efforts to build compared to decentralized ones, but they have issues in handling untrustworthy organizations that try to mutate the traceability chain since no consensus is strictly enforced. Better trustworthiness can be achieved by utilizing blockchain technologies, which ensure data immutability, and their decentralized nature enables consensus between organizations participating in the system. In this work, we propose a decentralized traceability system, which allows users to observe the traceability chain and get the aggregated data of fishery products. To build the decentralized fishery traceability system, we employ Hyperledger Fabric, which is popular for its remarkable identity management and its excellent performance relatively compared to other blockchain technologies. Our system has undergone performance tests and achieved high throughput and response time for almost all required features. In addition, the system reduced the probability to have a single point of failure problem since each organization has multi peers deployed.
Data-Driven Approaches to User Interface Design: A Case Study

Abstract The most common reason for software product failure is misunderstanding user needs. Analysing and validating user needs before developing a product can allow to prevent such failures. This paper investigates several data-driven techniques for user research and product design through prototyping, customer validation, and usability testing. The authors implemented a case study software product using the proposed techniques, and analyses how the application of UX/UI research techniques affected the development process. The case study results indicate that preliminary UX/UI research before the development reduces the cost of product changes. Moreover, the paper proposes a set of metrics for testing the effectiveness of UX/UI design.
Bandwidth function matrix-based spectral deconvolution with alternate minimization method

Spectroscopic data often suffer from spectral distortion and random noise. In this paper, a novel bandwidth function matrix-based spectral deconvolution with the alternate minimization method is proposed to recover the degraded spectra. Different from traditional spectral deconvolution methods which only employ or estimate one bandwidth function, the proposed method introduces a bandwidth function matrix composed of multiple bandwidth functions corresponding to the whole wavelength range of the spectrometer, and applies the alternate minimization method to alternately estimates the real spectrum and bandwidth function matrix until convergence. As a result, the correction error caused by the differences in bandwidth functions in the whole spectral band could be effectively eliminated. A series of experiments was carried out for various simulated and real measured spectra, and comparative results demonstrate the superior performance of the proposed method comparing with the traditional methods.
The Use of Information and Communication Technologies to Increase the Motivation of Students When Studying English as a Foreign Language

The research is devoted to the study of the possibilities of using information and communication technologies to increase the motivation of students in the process of learning English. Based on the results of a survey attended by 400 second-year students of the XXXX University being involved in the experimental course, the evolution of motivational behavior in the learning process has been demonstrated. The scientific value of the study relates to the concept outline of the synergistic relationship between the motivational behavior of students and the content of the online learning course. The practical value of the study relates to the demonstration of the educational experience of constructing the concept of an effective training course with a focus on maintaining the appropriate level of motivation.