Search

Purpose: The purpose of this study was to investigate associations between digital urine color and paper urine color with other urine indices to assess hydration status. Methods: Twelve male subjects (mean ± standard deviation; age, 26 ± 8 years; body mass, 57.8 ± 5.3 kg; height, 177.5 ± 8.9 cm; VO2max, 57.8 ± 5.8 ml·kg−1·min−1) performed four exercise trials in the heat. Before and following exercise trials, subjects provide urine samples. Urine samples were measured using a digital urine color chart on a portable device screen. Urine samples were also assessed with urine specific gravity (USG), urine osmolality (UOsmo), and a validated paper urine color chart. Results: There were extremely large associations found between digital urine color and paper urine color (r = 0.926, p < 0.001). Correlation coefficients showing associations with USG and UOsmo were similar between digital urine color (USG, r = 0.695, p < 0.001; UOsmo, r = 0.555, p < 0.001) and paper urine color (USG, r = 0.713, p < 0.001; UOsmo, r = 0.570, p < 0.001). Bland–Altman analysis indicated that no proportional bias was observed between digital and paper urine colors (bias, − 0.148; SD of bias, 0.492; 95% LOA, − 1.11, 0.817; p = 0.094). Conclusions: Strong associations were found between digital and paper urine colors with no proportional bias. Furthermore, the degree of associations with USG and UOsmo was similar between digital and paper urine color. These results indicate that digital urine color is a useful tool to assess hydration status and this method could be used as an alternative method to using paper urine color. [ABSTRACT FROM AUTHOR]

This paper examines commonly applied methods of data analysis. Predicated on these methods, the main issue pertains to the plausibility of the studies' end products, that is, their conclusions. I argue that the methods chosen often lead to unwarranted conclusions: the data analyses chosen tend to produce looked-for null rejections even though the null may be much more plausible on prior grounds. Two aspects of data analyses applied cause obvious problems. First, researchers tend to dismiss "preliminary" findings when the findings contradict the expected outcome of the research question (the "screen-picking" issue). Second, researchers rarely acknowledge that small p-values should be expected when the number of observations runs into the tens of thousands (the "large N" issue). This obviously enhances the chance for a null rejection even if the null hypothesis holds for all practical purposes. The discussion elaborates on these two aspects to explain why researchers generally avoid trying to mitigate false positives via supplementary data analyses. In particular, for no apparent good reasons, most research studiously avoids the use of hold-out samples. An additional topic in this paper concerns the dysfunctional consequences of the standard ("A-journal") publication process, which tends to buttress the use of research methods prone to false or unwarranted null-rejections. [ABSTRACT FROM AUTHOR]

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research, but is rarely explicitly used as a way to present planned or achieved research contributions in software engineering. Applying the design science lens to software engineering research may improve the assessment and communication of research contributions. Aim: The aim of this study is 1) to understand whether the design science lens helps summarize and assess software engineering research contributions, and 2) to characterize different types of design science contributions in the software engineering literature. Method: In previous research, we developed a visual abstract template, summarizing the core constructs of the design science paradigm. In this study, we use this template in a review of a set of 38 award winning software engineering publications to extract, analyze and characterize their design science contributions. Results: We identified five clusters of papers, classifying them according to their different types of design science contributions. Conclusions: The design science lens helps emphasize the theoretical contribution of research output—in terms of technological rules—and reflect on the practical relevance, novelty and rigor of the rules proposed by the research. [ABSTRACT FROM AUTHOR]

This paper is the first of a series whose aim is to respond the paper 'Scientific integrity in the digital age: data fabrication, published in Research on Chemical Intermediates, DOI ' by Khaled, and four related papers by the same author. The validity of the experimental findings with typical straight lines passing through the origin is discussed. This part presents clear-cut evidence from the literature that the variation of weight loss with time, constructed during corrosion and corrosion control research studies, yields typical straight lines passing through the origin, depending on the solubility of the corrosion products. This part also raises, and at the same time answers, the question: 'are experimental results with typical straight lines passing through the origin necessarily referring to data fabrication?' [ABSTRACT FROM AUTHOR]

Purpose: To compare (1) fluid, food and nutrient intake obtained with a paper versus an online version of a 7-day food record and (2) user's acceptability of both versions of the food record. Methods: A cross-over study was carried out in 2010 in France. A total of 246 participants aged 18-60 years reported their food and fluid intake using both versions of the 7-day food record, separated by a 7- to 14-day washout period. To help participants in estimating consumed portions, both versions of the food record were supported by a photographic booklet of standard portions and containers. At the end of the study protocol, participants completed a questionnaire designed to assess the acceptability of the two questionnaires. Results: The reported water intake of fluids was significantly higher with the online version compared with the paper version (respectively 1348 ± 36 and 1219 ± 34 mL/day, p < 0.0001). No difference was found between methods in terms of energy intake and the consumption of most food categories, macro- and micronutrients. Furthermore, 77 % of the participants preferred the online method to the paper version. Conclusions: Fluid intake, but not food intake, reported with the online 7-day food record was higher in comparison with the paper version. In addition, the online version was preferred by users. In population surveys, the online record is therefore a relevant alternative, and even a preferred alternative in the case of fluid intake, to the paper record. [ABSTRACT FROM AUTHOR]

By using the traditional active E2E (end-to-end) detection method to locate the congested link in IP network, we can only judge whether the network is congestion or not, and the congested cause cannot be effectively known. However, the passive detection method needs to acquire the syslog of all routers in the managed network, which requires a large amount of data. In this paper, a kind of multimodal spatiotemporal correlation fuzzy inference method for network health monitoring method was proposed through combining with the method of active E2E detection and passive detection. First, the set of network-congested links is obtained based on the Bayesian theorem through E2E path performance detection; then, the router syslog associated with the congested links will be acquired for a period of time, and the abnormal events in the syslog are detected based on the principle of mutual information (MI), so as to find the causes of network congestion. Based on the multimodal spatiotemporal correlation fuzzy paradigms combining with the active end-to-end detection and passive detection technology, the amount of data analysis will be greatly reduced and the operation speed will be improved. Simulation experiments and actual experiments verified the accuracy of the method this paper proposed. [ABSTRACT FROM AUTHOR]

As one of the most popular topics currently, big data has played an important role in both academic research and practical applications. However, in the manufacturing industry, it is difficult to make full use of the research results for production optimization and/or management due to the low quality of real workshop data. Typical quality problems of real workshop data include the information match degree, missing recessive data, and false error identification. The conventional data analysis methods cannot handle most such issues because these methods fail to consider professional insights into and domain knowledge about the data. The main motivation of this paper is to explore methods for analyzing and evaluating big data with domain knowledge. For this purpose, real production data from a semiconductor manufacturing workshop are adopted as the data object. First, a series of data analysis techniques with domain knowledge are developed for diagnosing the imperfections. Then, corresponding data processing techniques with domain knowledge are proposed for solving those data quality problems according to specific flaws in the data. Furthermore, this paper proposes quantitative calculation methods of data value density to determine the extent to which data quality can be improved by the proposed data processing techniques. Case studies are conducted to demonstrate that data analysis and processing techniques with domain knowledge can effectively handle data quality problems of real workshop data in terms of the information match degree, missing recessive data, and false error identification. The work in this paper has the potential to be further extended and applied to other big data applications beyond the manufacturing industry. [ABSTRACT FROM AUTHOR]

The Psychonomic Society (PS) adopted New Statistical Guidelines for Journals of the Psychonomic Society in November 2012. To evaluate changes in statistical reporting within and outside PS journals, we examined all empirical papers published in PS journals and in the Experimental Psychology Society journal, The Quarterly Journal of Experimental Psychology (QJEP), in 2013 and 2015, to describe these populations before and after effects of the Guidelines. Comparisons of the 2013 and 2015 PS papers reveal differences associated with the Guidelines, and QJEP provides a baseline of papers to reflect changes in reporting that are not directly influenced by the Guidelines. A priori power analyses increased from 5% to 11% in PS papers, but not in QJEP papers (2%). The reporting of effect sizes in PS papers increased from 61% to 70%, similar to the increase for QJEP from 58% to 71%. Only 18% of papers reported confidence intervals (CIs) for means; only two PS papers in 2015 reported CIs for effect sizes. Although variability statistics are important to understanding data, and to further analysis, they were only reported as numbers in just over half of the PS journal papers. Almost all PS and QJEP papers relied exclusively on null hypothesis significance testing to guide interpretation of the data. Changes associated with the Guidelines are in the desired direction with respect to reporting effect sizes and power analyses but are not yet reflected in researchers' practices in describing their data, addressing data assumptions, and thinking beyond the p value when interpreting their data. [ABSTRACT FROM AUTHOR]

This paper considers theoretical foundations and mathematical methods of data analysis under the conditions of the Rice statistical distribution. The problem involves joint estimation of the signal and noise parameters. It is shown that this estimation requires the solution of a complex system of essentially nonlinear equations with two unknown variables, which implies significant computational costs. This study is aimed at mathematical optimization of computer algebra methods for numerical solution of the problem of Rician data analysis. As a result of the optimization, the solution of the system of two nonlinear equations is reduced to the solution of one equation with one unknown variable, which significantly simplifies algorithms for the numerical solution of the problem, reduces the amount of necessary computational resources, and enables the use of advanced methods for parameter estimation in information systems with priority of real-time operation. Results of numerical experiments carried out using Wolfram Mathematica confirm the effectiveness of the developed methods for two-parameter analysis of Rician data. The data analysis methods considered in this paper are useful for solving many scientific and applied problems that involve analysis of data described by the Rice statistical model. [ABSTRACT FROM AUTHOR]

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

This bibliographic study presents a historical perspective on Korea's construction engineering and management (CEM) research and reports a 27-year analysis of papers written by Korean CEM researchers between 1993 and 2019. Frequency analysis of the collected papers was performed according to three main perspectives: the knowledge area, research topics, and methods. Knowledge area analysis shows that research fields have become more diversified and subdivided. Construction technology, cost management, and planning and schedule management have consistently maintained the top three positions for the past three decades. In addition, quality management, resource management, and risk management were also ranked high in the 2000s. In the 2010s, safety management, environmental management, quality management, and construction industry and business were ranked high. These changes reveal how Korean society's and industry's demands have changed over the years. Furthermore, the findings from keyword frequency analysis revealed that the focus of CEM research was mostly on cost and scheduling in the 1990s and on scheduling, risk, cost, and construction automation in the 2000s. The advent of Building Information Modeling (BIM) in the 2010s was an epochal change. This study found that the research methods frequently used by CEM researchers were simulation, optimization, regression analysis, computer vision and image processing, 3D modeling, case-based reasoning, genetic algorithm, case study, economic analysis, and artificial neural network. [ABSTRACT FROM AUTHOR]

The paper discusses the main scientific results of the XXIV International Conference "Data Analytics and Management in Data Intensive Domains" DAMDID/RCDL-2022, held on October 4–7, 2022, in St. Petersburg, Russia. A historical review of the development of the conference was carried out. Motivation, goals of the conference, main areas of the subject, related conferences, and held associated events are considered. The conference program of the current year is characterized, including the topics of invited and sectional reports. Conclusions are drawn on the basis of the results of the analysis of the scientific contribution of the conference. Attached is a list of papers selected by the conference program committee for a special issue of the journal. The papers relate to areas of data integration, data analysis in astronomy and biomedicine, machine learning applications, image analysis and processing, and information extraction from texts. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

During the epidemic, online teaching became the mainstream. Online teaching evaluation aims to systematically test teachers' teaching process according to certain teaching objectives and standards, and evaluate its value, advantages and disadvantages, so as to improve the quality of teaching. It is not only an important part of the teaching process, but also the basis of all effective and successful teaching. In this paper, we propose an online teaching evaluation method based on Epistemic Neural Network (ENN), which is an evolutionary intelligence method. In terms of uncertainty modeling, ENN's design innovation provides the improvement effect of geometric progression in terms of statistical quality and calculation cost. Therefore, it is very suitable for teaching evaluation, which is an evaluation process guided by a variety of uncertain factors. Specifically, this paper considers the content and grade standards of online teaching evaluation from five aspects. (1) Teachers' syllabus, teaching progress, teaching plan, courseware and other teaching documents and teaching materials; (2) Abide by teaching discipline, the implementation of teaching plan and the completion of teaching tasks; (3) Teaching attitude, teaching investment, teaching and educating people, and the comprehensive quality of teachers; (4) Whether the concepts taught in the course are accurate, the expression is clear, whether the key points are prominent and whether the difficulties are clearly explained; (5) The depth, breadth and frontier of teaching content, and the amount of classroom information. According to the above five evaluation indexes which involves the big data analysis, we train ENN to get an evaluation score that can evaluate the teacher's online teaching process. In addition, we also test the average evaluation time to verify the effectiveness. [ABSTRACT FROM AUTHOR]

Context: Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes. Objectives: Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology. Methods: We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included. Results: The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years. Conclusions: The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously. [ABSTRACT FROM AUTHOR]

This special issue contains substantially revised and extended versions of some of the best papers presented at the 22nd International Conference on Fundamental Approaches to Software Engineering in 2019. All papers share the common theme that they are either concerned with model-based analysis of systems or they develop methods in its service. [ABSTRACT FROM AUTHOR]

Massive teaching resources will cause serious teaching efficiency problems for online teaching, and traditional online teaching models are even inferior to traditional classroom teaching in terms of teaching effects. Based on this, this paper analyzes massive educational resources and builds a scalable computer interactive education system based on large-scale multimedia data analysis. Moreover, this paper sets the role of the system according to the actual teaching situation, and constructs the functional module of the system structure. In addition, this paper uses computer simulation technology to analyze interactive technology and make technical improvements to make interactive technology the core technology of the computer interactive education system, and get an extensible interactive education system based on the characteristics of network teaching. Then helps to monitor and access the performance of an interactive educational system. Furthermore, this paper designs an experiment to evaluate the performance of the computer interactive education system, which is mainly carried out from two aspects: interactive evaluation and teaching evaluation. From the experimental research results, we can see that this system can effectively improve the quality of teaching. [ABSTRACT FROM AUTHOR]

Global cities play a significant role in world economy as they serve as key hubs of economic activity and trade. These cities are centers of innovation, finance, culture, and commerce, attracting businesses and entrepreneurs from all over the world. They are characterized by their openness, diversity, and their ability to attract and retain talent. This paper includes a bibliometric analysis of the structure of global cities through examining the literature on global cities, including the document type, country/territory distribution, institution distribution, geographical distribution of authors, specially most active authors and their interests or research areas, relationships between principal authors and more relevant journals, and the research hot spots. The input data consists of journal articles archived by the Web of Science from 1991 to 2023, and the analysis is performed using SciMAT and VOS Viewer. The result of this paper would provide valuable insights into the state of research on this topic, including who is conducting research, where it is being conducted, what types of publications are being produced, and which themes are having the most impact on the field. Such an analysis would be useful for researchers, policymakers, and other stakeholders interested in understanding the role of global cities in the world economy. [ABSTRACT FROM AUTHOR]

The analysis of collaborative contribution networks and hot spots in earthquake research from a global perspective will reveal some rules and trends in this field that will provide references and macroguidance for scientific researchers in the field. With the help of data analysis software Excel and visualization software VOSviewer, this study mainly uses two methods of bibliometric and social network analysis to analyze 66,296 papers on the subject of "earthquake" collecting in Web of Science in 2009–2018 from multiple perspectives. In the past 10 years, scientific research output in the earthquake field has steadily increased year by year with an evident cross subject. The contribution of co-publishing is much higher than that of single publishing, and it has a higher number of citations and displays. Half of the 50 keywords with the highest frequency of co-occurrence are the continuation of hot spots in 1900–2010. The evolution of hot spots shows that in the average publishing year of 2013.5–2015.0, hot spots have changed from old to new, but few new hot spots have appeared in the past 2 years. [ABSTRACT FROM AUTHOR]

This paper examines how an individual's labor supply responds immediately to her spouse's health shock in an aging household and how she adjusts her labor supply over time after her spouse's health shock. Different from previous work, this paper considers the subsequent health evolution following the spouse's health shock by proposing an adaptation model where the long-term labor supply adjustment of an individual is allowed to depend on her spouse's health evolution after the initial shock. Analysis of the 1996-2012 data from the Health and Retirement Study (HRS) suggests that in the short run, both husbands and wives change their labor supply very little when their spouses become ill, but in the long run, a husband's labor supply adjustment does vary with his wife's current health status after her initial health shock. In contrast, the wife's annual work hours are not affected by her husband's health shock in the long run, regardless of husband's subsequent health status. Households with an ill wife are probably at greater risk than those with an unhealthy husband in the long run, which may be attributed to the role that women have traditionally played in the household. [ABSTRACT FROM AUTHOR]

Purpose: Life cycle assessment (LCA) is a data-intensive methodology; therefore, experts usually focus collection efforts on a few activities, while generic data on remaining activities are taken from databases. Even though increased availability of databases has facilitated LCA takeoff, assuring data quality is fundamental to ensure meaningful results and reliable interpretation.Methods: Ecoinvent has become a global reference for inventory data. Its current version released three impact partition modeling options—the recycled content, "allocation at the point of substitution" (APOS), and consequential models—whose adequate choice is crucial for yielding meaningful assessments. Tutorials and manuals describe the distribution algorithm that backs each system model, to ground decision-making regarding the best fit to a study's goals. We performed a systematic literature review to investigate—within the papers published on the International Journal of LCA (IJLCA)—how transparently authors addressed the system model choices.Results and discussion: About 70% of LCA practitioners continued to use earlier versions of ecoinvent after version 3 was launched in 2013. The number of papers using versions 3.x only showed an increased growth trend 2 years later. Eighty-three papers actually adopted the newest version of the database. From those, only 29 papers clearly mentioned the adopted system model. Our SLR also suggests a trend regarding authorship profile of LCA-related studies: the number of studies conducted by practitioners aware of the intricacies of sound modeling of background and foreground data might have been surpassed by those conducted by non-LCA specialists who use LCA as a supporting tool for investigations in applied fields, and merely scratch the surface.Conclusions: Our results point to a need for a caveat: ecoinvent users must take time to understand the general concept behind each system model and practice one of the most important actions when performing an LCA—state methodological choices clearly. [ABSTRACT FROM AUTHOR]

Purpose: To look at the bibliometric publication trends on 'Urolithiasis' and aspects of treatment and training associated with it over a period of 16 years from 2000 to 2015. To this end, we conducted this study to look at the publication trends associated with urolithiasis, including the use of simulation, laser technology, and all types of interventions for it. Materials and methods: We performed a systematic review of the literature using PubMed over the last 16 years, from January 2000 to December 2015 for all published papers on 'Urolithiasis'. While there were no language restrictions, English language articles and all non-English language papers with published English abstracts were also included. Case reports, animal and laboratory studies, and those studies that did not have a published abstract were excluded from our analysis. We also analyzed the data in two time periods, period-1 (2000-2007) and period-2 (2008-2015). Results: During the last 16 years, a total of 5343 papers were published on 'Urolithiasis', including 4787 in English language and 556 in non-English language. This included papers on URS ( n = 1200), PCNL ( n = 1715), SWL ( n = 887), open stone surgery ( n = 87), laparoscopic stone surgery ( n = 209), pyelolithotomy ( n = 35), simulation in Endourology ( n = 82), and use of laser for stone surgery ( n = 406). When comparing the two time periods, during period 2, the change was +171% ( p = 0.007), +279% ( p < 0.001), and −17% ( p = 0.2) for URS, PCNL, and SWL, respectively. While there was a rise in laparoscopic surgery (+116%), it decreased for open stone surgery (−11%) and pyelolithotomy (−47%). A total of 82 papers have been published on simulation for stone surgery including 48 papers for URS (67% rise in period-2, p = 0.007), and 34 papers for PCNL (480% rise in period-2, p < 0.001). A rising trend for the use of laser was also seen in period 2 (increase of 126%, p < 0.02, from 124 papers to 281 papers). Conclusions: Published papers on intervention for Urolithiasis have risen over the last 16 years. While there has been a steep rise of URS and minimally invasive PCNL techniques, SWL and open surgery have shown a slight decline over this period. A similar increase has also been seen for the use of simulation and lasers in Endourology. [ABSTRACT FROM AUTHOR]

The latent block model is used to simultaneously rank the rows and columns of a matrix to reveal a block structure. The algorithms used for estimation are often time consuming. However, recent work shows that the log-likelihood ratios are equivalent under the complete and observed (with unknown labels) models and the groups posterior distribution to converge as the size of the data increases to a Dirac mass located at the actual groups configuration. Based on these observations, the algorithm Largest Gaps is proposed in this paper to perform clustering using only the marginals of the matrix, when the number of blocks is very small with respect to the size of the whole matrix in the case of binary data. In addition, a model selection method is incorporated with a proof of its consistency. Thus, this paper shows that studying simplistic configurations (few blocks compared to the size of the matrix or very contrasting blocks) with complex algorithms is useless since the marginals already give very good parameter and classification estimates. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores how Educational Big Data Analytics can enhance student learning. It investigates the role of active learning in improving students' data analysis skills and critical thinking. By actively engaging students in data analysis assessments, the aim is to equip them with the skills to navigate the data-rich educational landscape. Methods: The study uses a teaching strategy that combines structured and unstructured data analysis using Python tools and ChatGPT APIs. It presents five assignments, each highlighting data analysis skills and encouraging critical thinking. Results: The paper offers insights into how the teaching strategy effectively enhances students' data analysis and critical thinking skills. It investigates the specific impact of active learning on students' engagement with educational data. The study reveals that all students can complete a comprehensive project, integrating the skills they have learned in the five assignments related to educational big data while incorporating the educational implications from their respective disciplines. Conclusion: The key lies in instructors being able to design individual assignments that link practical experiences, enabling each teaching session's effectiveness to accumulate in students' personal experiences and practical skills, ultimately empowering them with the abilities necessary to work effectively with Educational Big Data Analytics. The findings of this study make a valuable contribution to the ongoing conversation about enhancing the educational experience for students in this data-rich era. [ABSTRACT FROM AUTHOR]

Survival analysis studies time to event data, also called survival data in biomedical research. The main challenge in the analysis of survival data is to develop inferential methods that take into account the incomplete information contained in censored observations. The seminal paper of Kaplan and Meier (J Am Stat Assoc 53:457–481,1958) gave a boost to the development of statistical methods for time to event data subject to right censoring; methods that have been applied in a broad variety of scientific fields including health, engineering and economy. A basic quantity in survival analysis is the survival distribution: S (t) = P (T > t) , with T the time to event or, in case of a bivariate vector of lifetimes (T 1 , T 2) , S (t 1 , t 2) = P (T 1 > t 1 , T 2 > t 2 ). Nonparametric estimation of these basic quantities received, since Kaplan and Meier (J Am Stat Assoc 53:457–481,1958), considerable attention resulting in many publications scattered over a large period of time and a large field of applications. The purpose of this paper is to review, in a unified way, nonparametric estimation of S(t) and S (t 1 , t 2) for time to event data subject to right censoring. Interesting to realize is that, in the multivariate setting, the form of the nonparametric estimator for S (t 1 , t 2) is determined by the actual censoring scheme. In this survey we focus, for the proposed (implicitly) existing or new nonparametric estimators, on the asymptotic normality. By doing so we fill some gaps in the literature by introducing some new estimators and by providing explicit expressions for the asymptotic variances often not yet available for some of the existing estimators. [ABSTRACT FROM AUTHOR]

In this journal, Sciffer et al. (Large-scale Assessments in Education 10:1–22, 2022), hereafter SP&M, conclude that school socioeconomic compositional (SEC) or school socioeconomic status (school-SES) effects in Australia are substantial and substantively important for research and policy. This paper demonstrates that these claims are unwarranted. Their SEC estimates are much larger than estimates from comparable studies and a metastudy. Despite plausible theoretical reasons and empirical evidence, SP&M do not consider that school academic composition is a significant predictor of student achievement independent of SEC. SEC effects are confounded by academic composition and are typically trivial when considering academic composition. The second part of this paper compares SP&M's estimates with analysis of the same data, from the Australian National Assessments in Performance—Literacy and Numeracy (NAPLAN). In a model corresponding to SP&M analyses comprising demographics, SES, school-SES, and student-level prior achievement, the effects of school-SES are small, with standardized effects mostly less than 0.10. With the addition of academic composition measured by school-level prior achievement, school-SES effects are effectively zero. In contrast, academic composition has significant, albeit small, impacts on student achievement. Therefore, contrary to SP&M's (2022) conclusion, school-SES effects on student achievement in NAPLAN are negligible, whereas school-level prior achievement has small effects. That is not to say that school-SES is always irrelevant, but any assessment of its importance must consider both student- and school-level prior achievement. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network. Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers. [ABSTRACT FROM AUTHOR]

The inverter is considered the core of the PV power plant. The inverter's failure leads to generation loss and decreases plant availability. So, it is required to investigate a clear Root Cause Analysis (RCA) to deduce the failure causes and implement the required corrective action in addition to the preventive action to avoid more inverter failure, hereby maintaining the plant available to a certain value. This paper discusses real-time mode operation data analysis of the PV grid-connected inverter due to real central inverter incidents in Benban solar park located in Egypt.The central inverter plays an important role in the Mega-Scale PV power plant. The main function of this inverter is to convert the DC power produced by the PV modules to AC power to be injected into the utility grid by considering specific characteristics based on the grid code. The availability of any PV power plant directly depends on the healthy inverter's operation. The more increases for the installed inverters, the less availability loss in the case of inverter partial or catastrophic failures. So, it is required to focus on the failure causes of the central inverter by implementing a technical analysis using the available operational data. The monitored data of the central inverter in the PV power plant is classified into two types. The first type is the continuous time data stored in the memory. It represents the waveforms of inverter outputs like voltage, current, frequency, .... etc. Unfortunately, in case of a catastrophic failure, the central inverter is completely charred, and the continuous time data is lost due to storage memory damage. The second type is the operation data that is recorded by the SCADA system (per one-minute interval). Hereby, the operation data is the sole data in the case of the completely charred inverter. The representation of the operational data in curves indicates symptoms that can be used for the RCA processes. The investigation outcomes include three results. The first result is detecting the signature of the IGBT thermal stress on the voltage balance of the DC link capacitor. The second result is verifying a scenario for the cause of the IGBT failure by implementing a technical mathematical model based on the detected symptoms that denote the fault signature which is considered the thread-tip for detecting the failure cause. The third result is the simulating scenario for the interpretation of a DC link capacitors explosion due to the short circuit fault that occurred due to IGBT failure. The investigation in this paper is performed based on operation data analysis of the PV grid-connected inverter (central type) due to a real incident. The analysis methodology is based on mathematical calculation for the IGBT junction temperature using the measured heatsink temperature. The study concludes that after the IGBT failure occurred, it was a short circuit for a while and closed the terminals of the DC link capacitors. So, the DC link capacitors exploded and produced heavy sparks that led to enough fire to burn the inverter container completely. [ABSTRACT FROM AUTHOR]

With the development of world economy, sports development has also become a symbol of national strength. Nowadays, sports competition has become an important activity for all countries to show their strength, and also an important bridge to connect all countries and build friendship. In the process of challenging the limit, human beings gradually master the action essentials of various sports, and the action difference of athletes directly affects the grades of the competition. Therefore, in the process of sports training, athletes of various countries will focus on the standard of action of each athlete. In traditional sports training, veteran athletes with rich experience are usually used as coaches to guide athletes empirically. However, the difference of coaches will also lead to the difference of sports effects. At the same time, coaches' grasp of athletes' action standards is more based on subjective observation, which cannot be accurately measured, so there may be large differences and errors in their grasp of action standards. In this paper, we propose an intelligent action recognition and correction system to assist coaches to measure and evaluate athletes' actions more accurately, and give correction suggestions according to standard actions. Our system uses RGB-D sensors to analyze athletes' skeleton key points in real time. The different between the athlete's action and the standard action is calculated through the connection between the joint key points. In this paper, we also use the timing tracking algorithm to comprehensively evaluate the consistency of the action with the standard. We verified the feasibility of the recognition correction system through the actual movement and measurement of athletes. The experiment shows that our system can accurately measure the movements of athletes, and has more accurate measurement results and correction suggestions than the traditional coach naked eye measurement. [ABSTRACT FROM AUTHOR]

In a world with an increasing population, an alternative for aiding healthcare systems is the use of sensors and wearable devices for monitoring patient physiological data. The analysis of collected data can help guide health services or the self-care of patients. This paper explores literature related to the collection and analysis of physiological data in smart environments by means of a systematic mapping study, organized in three steps: (1) identification of research questions; (2) elaboration of the search process; (3) definition of the criteria for filtering results. Papers were added using the snowball sampling method. This work encompassed 5870 papers published in the past 11 years, up to April 2019. The final selection resulted in 32 papers. Among these, 25 works collected cardiac data, 23 used Wi-Fi, Bluetooth, GSM, or ZigBee technologies, and 14 used techniques for the analysis of physiological data. Mapping verified the more prevalent trends and technologies in the collected and analyzed physiological data from smart environments. The filtering process allowed for a focus on communication technologies and vital sign data types. Three general questions (GQ), two specific questions (SQ), and two statistical questions (STQ) were answered. Similar reviews have already been conducted focusing on sensors, rather than collecting techniques and physiological data analysis. This denotes an opportunity for further studies in the vital sign analysis area. [ABSTRACT FROM AUTHOR]

Although data sharing is one of the primary measures proposed to improve the integrity and quality of published research, studies show it remains the exception not the rule. The current study examines the availability of data in papers reporting the results of analyses of empirical data from original research in high-impact addiction journals. Thirteen high-impact journals with data sharing policies were selected from those included in the substance abuse category of the 2018 Clarivate Analytics' Journal Citation Report. The first 10 full or short original research reports that included empirical data in the most recent complete issue of each journal were electronically searched and reviewed for reference to where their data can be obtained and for a formal data sharing statement. Only eight of the 130 papers contained a data sharing statement in their text or supplementary online materials, and just one contained a direct link to the data analyzed. Data sharing was rare in the 13 high-impact addiction journals reviewed. The nature of the data reported in addiction journals might partly explain this. Currently, data sharing is not a procedure likely to improve the quality and integrity of published addiction research. [ABSTRACT FROM AUTHOR]

The medical industry is constantly improving its own structure with the development of society. However, most of the current drug management systems cannot meet the needs of actual drug management. There are many problems such as incomplete system functions, confusion of drug management, unclear division of modules, loss and waste of human resources. At present, hospitals need a new and perfect hospital drug information management. Drug management is an indispensable part of the hospital management system. This paper completes the design of the target drug rapid retrieval system, which is realized multidimensional data analysis technology, and tests the multidimensional data analysis algorithm model used. The improved multidimensional data analysis algorithm greatly improves the accuracy. The design and improvement of the system can effectively improve the drug processing efficiency of existing pharmacies through the design and simulation experiments, enable the pharmacy department to better cooperate with other departments to work, make the cooperation between different departments more effective, and solve the work efficiency problems of the hospital. By introducing multidimensional data analysis technology into the field of drug information retrieval, this paper designs an effective and fast retrieval method. [ABSTRACT FROM AUTHOR]

In this paper we use the DBLP data to investigate the co-author relationship in a real bibliographic network and predict the interactions between co-authors. We analysis the research trend of authors and conferences based on keywords extracted from paper titles. We can understand research fields and change of research trend to find appropriate co-authors and conferences to submit our work. We also find potential co-authors for an existing author in DBLP data by using a variety of similarity measures and a random walk model. It can be useful for building a recommendation system. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to introduce an algorithm designed for generating random variates in situations characterized by uncertainty. Method: The paper outlines the development of two distinct algorithms for producing both minimum and maximum neutrosophic data based on the Weibull distribution. Results: Through comprehensive simulations, the efficacy of these algorithms has been thoroughly assessed. The paper includes tables presenting neutrosophic random data and an in-depth analysis of how uncertainty impacts these values. Conclusion: The study's findings demonstrate a noteworthy correlation between the degree of uncertainty and the neutrosophic minimum and maximum data. As uncertainty intensifies, these values exhibit a tendency to decrease. [ABSTRACT FROM AUTHOR]

The growing number of patients with skin cancer has highlighted the need for diagnostic expert systems to help detect lesions with high accuracy. The detection of dangerous diseases associated with skin lesions, especially malignant neoplasms, requires the detection of pigmented skin lesions. Methods of image recognition and computerized classification capabilities can improve the accuracy of skin cancer detection. In this paper, an approach based on leverage algorithm has been proposed. Statistical analysis based on feature acquisition from images is used to optimize the lightweight classification methods. Numerical experiments have shown the high efficiency of the approach, and the accuracy is comparable to the use of deep learning neural networks. [ABSTRACT FROM AUTHOR]

This paper aims to broaden the scope of the construct concept image by addressing the mathematically oriented notion of "solution of an algebraic equation." The notion solution of an algebraic equation is not mathematical per se, but it is most prevalent in mathematical communications and in mathematics education. The paper describes five episodes of lesson segments that were collected in a secondary school mathematics teaching certificate program. The specific tasks, the episodes, and the related class discussions, involving the notion of solution of algebraic equation, are described. Several issues, arising from the various concept images of solution of equations, including miscommunication, ambiguity about correctness, ranking correctness, and minimizing errors, are discussed. Along an analysis of these data, 4 suggestions are offered for improving the communication around the notion solution of an algebraic equation: promote awareness, offer instructional guidelines, suggest a vocabulary, and be sensitive. [ABSTRACT FROM AUTHOR]

In the count data set, the frequency of some points may occur more than expected under the standard data analysis models. Indeed, in many situations, the frequencies of zero and of some other points tend to be higher than those of the Poisson. Adapting existing models for analyzing inflated observations has been studied in the literature. A method for modeling the inflated data is the inflated distribution. In this paper, we extend this inflated distribution. Indeed, if inflations occur in three or more of the support point, then the previous models are not suitable. We propose a model based on zero, one, ... , and k inflated points with probabilities w 0 , w 1 , ... , and w k , respectively. By choosing the appropriate values for the weights w 0 , ... , w k , various inflated distributions, such as the zero-inflated, zero–one-inflated, and zero–k-inflated distributions, are derived as special cases of the proposed model in this paper. Various illustrative examples and real data sets are analyzed using the obtained results. [ABSTRACT FROM AUTHOR]

This paper proposes the concept of green institutional environment, and constructs China's green institutional environmental system from the three dimensions of green supervision and public institution, green standardized institution and green accounting institution. Based on the relevant data from 2007 to 2017, this paper simulates China's green institutional environmental index through the Functional Data Analysis method and the functional entropy weight method. The results show that, first, there is a general upward trend in China's green institutional environmental index, with a range of [0.2–0.8]; the minimum value of this index is 0, and there is no maximum value. The larger the value, the higher the level of green institutional environment. Second, at the present stage, China's green standardized institution is relatively well-developed, accounting for the largest weight (i.e., [0.932–0.940]) in the green institutional environmental index system; the green accounting institution plays a supporting role, with a weight of [0.030–0.040], due to the fact that the Chinese government has made great efforts to implement policies at this level; the development of green supervision and public institution is under-developed and has little influence on the green institutional environmental index, with a weight of [0.0285–0.0305]. Finally, the proposed green institutional environmental index remains robust when equalizing the weights of the three dimensions. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment associated with schizophrenia (CIAS) represents a distinct, persistent, and core group of schizophrenia symptoms. Cognitive symptoms have been shown to have an impact on quality of life. There are several published CIAS measures, but none based on direct patient self-report. It is important to capture the patient's perspective to supplement performancebased outcome measures of cognition to provide a complete picture of the patient's experience. This paper describes additional validation work on the Patient-Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS) instrument. Methods: Data from two large, international, pharmaceutical clinical trials in medically and psychiatrically stable English-speaking patients with schizophrenia and 88 healthy controls were analyzed. An exploratory factor analysis (EFA) was conducted in one trial (n = 215), using the original 35-item PRECIS. The factor structure suggested by EFA was further evaluated using item response theory (IRT; Samejima's graded response model), and tested using confirmatory factor analysis (CFA). Both EFA and CFA results were tested in a second trial with similar inclusion/exclusion characteristics (n = 410). Additional statistical properties were evaluated in healthy controls. Results: EFA suggested that the best solution after item reduction suggested a factor structure of 6 factors based on 26 items (memory, communication, self-control, executive function, attention, sharpness of thought), supporting a total score, with an additional 2-item bother score (28 items in all). IRT analysis indicated the items were well-ordered within each domain. The CFA demonstrated excellent model fit, accounting for 69% of the variance. The statistical properties of the 28-item version of the PRECIS were confirmed in the second trial. Evidence for internal consistency and test-retest reliability was robust. Known-groups validity was supported by comparison of healthy controls with patients with schizophrenia. Correlations indicated moderate associations between PRECIS and functioning instruments like the Schizophrenia Cognition Rating Scale (SCoRS), but weak correlations with performance-based outcomes like MATRICS Consensus Cognitive Battery (MCCB). Discussion: Using two clinical trial samples, we identified a robust factor structure for the PRECIS and were able to replicate it in the second sample. Evaluation of the meaningful score difference (MSD) should be repeated in future studies, as these samples did not show enough change for it to be evaluated. Conclusions: This analysis provides strong evidence for the reliability and validity of the PRECIS, a 28-item, patient-reported instrument to assess cognitive impairment associated with schizophrenia. The correlation with functioning and the weak correlation with performance on cognitive tasks suggests that patient reports of cognitive impairment measure a unique aspect of patient experience. [ABSTRACT FROM AUTHOR]

Spectroscopic techniques generate one-dimensional spectra with distinct peaks and specific widths in the frequency domain. These features act as unique identities for material characteristics. Deep neural networks (DNNs) has recently been considered a powerful tool for automatically categorizing experimental spectra data by supervised classification to evaluate material characteristics. However, most existing work assumes balanced spectral data among various classes in the training data, contrary to actual experiments, where the spectral data is usually imbalanced. The imbalanced training data deteriorates the supervised classification performance, hindering understanding of the phase behavior, specifically, sol-gel transition (gelation) of soft materials and glycomaterials. To address this issue, this paper applies a novel data augmentation method based on a generative adversarial network (GAN) proposed by the authors in their prior work. To demonstrate the effectiveness of the proposed method, the actual imbalanced spectral data from Pluronic F-127 hydrogel and Alpha-Cyclodextrin hydrogel are used to classify the phases of data. Specifically, our approach improves 8.8%, 6.4%, and 6.2% of the performance of the existing data augmentation methods regarding the classifier's F-score, Precision, and Recall on average, respectively. Specifically, our method consists of three DNNs: the generator, discriminator, and classifier. The method generates samples that are not only authentic but emphasize the differentiation between material characteristics to provide balanced training data, improving the classification results. Based on these validated results, we expect the method's broader applications in addressing imbalanced measurement data across diverse domains in materials science and chemical engineering. [ABSTRACT FROM AUTHOR]

Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (MRep-DeepInsight), a novel extension of the DeepInsight method designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach is able to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest, XGBoost, LightGBM, FT-Transformer and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By leveraging the power of diverse representations, MRep-DeepInsight offers a promising new avenue for advancing decision-making and scientific discovery across a wide range of fields. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

This paper presents a conceptual framework that accurately represents the current and future perspectives of data-driven companies in tourism by means of an analysis of the data sources used in the data-driven tourism research literature, as well as the research topics to which they are applied. For this purpose, a bibliometric analysis of data-driven tourism research is carried out. The framework of the study is all tourism-related publications whose research was based on data sources during the period 1982–2020. The results show some of the basic bibliometric performance indicators and the maps of science. The main themes of research interest are identified, and the conceptual evolution is obtained based on these maps. Three major thematic areas are identified: tourism research topics, information sources, and data analysis techniques. Based on these three thematic areas, the conceptual model of data architecture and processes of a data-driven organization in the tourism sector are obtained. An additional qualitative analysis of the three thematic areas is performed. [ABSTRACT FROM AUTHOR]

Data releasing and sharing between several fields has became inevitable tendency in the context of big data. Unfortunately, this situation has clearly caused enormous exposure of sensitive and private information. Along with massive privacy breaches, privacy-preservation issues were brought into sharp focus and privacy concerns may prevent people from providing their personal data. To meet the requirements of privacy protection, such a problem has been extensively studied. However, privacy protection of sensitive information should not prevent data users from conducting valid analyses of the released data. We propose a novel algorithm in this paper, named Data Release under Adjustable Privacy-utility Equilibrium (DRAPE), to address this problem. We handle the privacy versus utility tradeoff in the data release problem by breaking sensitive associations among variables while maintaining the correlations of nonsensitive variables. Furthermore, we quantify the impact of the proposed privacy-preserving method in terms of correlation preservation and privacy level, and thereby develop an optimization model to fulfil data privacy and data utility constraints. The proposed approach is not only able to provide a better privacy levels control scheme for data publishers, but also provides personalized service for data requesters with different utility requirements. We conduct experiments on one simulated dataset and two real datasets, and the simulation results show that DRAPE efficiently achieves a guaranteed privacy level while simultaneously effectively preserving data utility. [ABSTRACT FROM AUTHOR]

This paper analyses Twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles and was able to successfully identify the political leniency of the user profiles with 99% confidence level, and also on a synthetic dataset with 2500 tweets, where the predicted accuracy and F1 score were 0.99 and 0.985 respectively, and 0.97 and 0.975 when neutral users were also considered for classification. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in the number of users belonging to the different clusters. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of tourism management classroom teaching, this article intelligentizes the teaching process of tourism management, and combines multimedia technology and big data technology to improve the teaching process. The crisis early warning system based on the image monitoring index system monitors and observes the components of the tourist destination from the perspective of tourists, and calculate the indexes based on the monitored results, so as to discover the possibility of image crisis events in time and better serve tourists. In addition, this article builds a tourism management classroom teaching system based on multimedia and big data technology on this basis, and tests the system. Through research, it can be seen that the crisis early warning system based on the image monitoring index system and the tourism management classroom teaching system based on multimedia and big data technology proposed in this paper have good practical effects. [ABSTRACT FROM AUTHOR]