In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user (...) interaction module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data (...) analysis techniques, researchers can produce results that are locally robust. Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (shrink)

Data analysis has become most widespread field of research and it has extended in almost every field of study. Considering the recent trends and developments in the field of communication and information technology, there is a scope of combining the monitoring of substation equipment with big data analysis technology. That will result in an improved data analysis ability, information sharing and utilization rate of monitoring data. In the proposed work, the authors have (...) introduced the big data analysis and its corresponding application in the monitoring of substations. Basic concepts and the procedures of the typical data analysis for general problems are also discussed. As a main part of the paper, different types of distributed data analysis techniques have been proposed, in which two relational online analysis, namely Hive and Impala and one H Base multidimensional online analysis are important. These data analysis techniques are proposed considering the analysis efficiency, storage performance from the business development requirements point of view of the substation. The result obtained depicts that the proposed model has an advantage in storage overhead and roll-up performance, when compared with the traditional method, although the data loading speed is approximately 1.7–1.9 times of the traditional model. Some experiments are carried out in order to verify the validity of the model. (shrink)

Insight is an important and repeated component of most qualitative research studies. Yet insight is often a vague concept that is not well articulated in textbooks and research reports. The late James Loder of Princeton University posited a theologically-based process he termed ‘the transforming moment’ that identifies predictable phases in a wide variety of transformations, including those of a psychological, scientific, and spiritual nature. This process corresponds at many levels with the role of insight in qualitative research. As a (...) class='Hi'>result, careful examination of Loder's outline of the transformative process can help clarify what is meant by insight and how it informs research activities. (shrink)

Teachers’ teaching psychological behavior and classroom development are the current research hotspots in the field of educational psychology. How to realize the data analysis of teachers’ teaching psychological behavior and classroom development is a problem that researchers urgently need to solve. Based on the theory of data correlation analysis, this paper uses modern Internet technology and big data analysis teacher teaching system to quantitatively and qualitatively analyze the potential of students, and build a corresponding (...) model. Through rule correlation technology, the article studies various internal correlations between teachers’ teaching psychological behavior, extracts valuable information from various daily data of students through big data analysis technology, and the WEB teacher’s teaching psychological behavior analysis system based on B/S structure solves the problem that the traditional model cannot measure. In the simulation process, the system is implemented by MVC three-tier architecture, the database uses MYSQL 5.0, the prediction questionnaire is formulated on the basis of the literature method and interviews, and the scale is compiled and tested after repeated revisions. Project analysis and factor analysis are performed on the data obtained from the table test to construct and screen indicators. The experimental results show that the teacher’s classroom teaching behavior index system adopted by the system is practical and feasible, including three first-level indicators, 10 s-level indicators, and 21 third-level indicators. The system has 87.1% completeness, which effectively improves teachers’ teaching psychology. (shrink)

In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.

College student entrepreneurship is a complex and dynamic process, in which the potential risks faced by entrepreneurial enterprises are interactive and diverse. The changes in risk assessment for college student entrepreneurship are also dynamic and nonlinear and are affected by many factors, which make the risk assessment process for college student entrepreneurship quite complicated. Big data analysis technology is a new product formed under the background of cloud computing and Internet technology, which has the characteristics of large (...) class='Hi'>data scale, multiple data types, and strong data value and provides more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. On the basis of summarizing and analyzing previous research results, this article expounded the research status and significance of the risk assessment algorithm for college student entrepreneurship, elaborated the development background, current status, and future challenges of big data analysis technology, introduced the basic principles of support vector machine and hierarchical analytic process, constructed a risk assessment model for college student entrepreneurship based on big data analysis, analyzed the risk factors and assessment indicators of the entrepreneurial model, proposed a risk assessment algorithm for college student entrepreneurship based on big data analysis, performed the discrimination coefficient calculation and comprehensive correlation optimization, and finally conducted a case experiment and its result analysis. The study results show that the risk assessment algorithm for college student entrepreneurship based on big data analysis can effectively realize the comprehensive management of risk factors, make full use of the value of assessment parameter data, and significantly improve the accuracy and efficiency of the risk assessment for college student entrepreneurship, providing more technical support for the researches on the risk assessment algorithm for college student entrepreneurship. The study results of this article provide a reference for further researches on the risk assessment algorithm of college student entrepreneurship based on big data analysis. (shrink)

Many decades ago Patrick Suppes argued rather convincingly that theoretical hypotheses are not confronted with the direct, raw results of an experiment, rather, they are typically compared with models of data. What exactly is a data model however? And how do the interactions of particles at the subatomic scale give rise to the huge volumes of data that are then moulded into a polished data model? The aim of this paper is to answer these questions by (...) presenting a detailed case study of the construction of data models at the LHCb for testing Lepton Flavour Universality in rare decays of B-mesons. The close examination of the scientific practice at the LHCb leads to the following four main conclusions: raw data in their pure form are practically useless for the comparison of experimental results with theory, and processed data are in some cases epistemically more reliable, real and simulated data are involved in the co-production of the final data model and cannot be easily distinguished, theory-ladenness emerges at three different levels depending on the scope and the purpose for which background theory guides the overall experimental process and the overall process of acquiring and analysing data in high energy physics is too complicated to be fully captured by a generic methodological description of the experimental practice. (shrink)

The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...) results of the main empirical research carried out in recent years, in particular those conducted with a promising methodology: Digital Trace Data Analysis. (shrink)

As a special occupational group, the working and living environments faced by seafarers are greatly different from those of land. It is easy to affect the psychological and physiological activities of seafarers, which inevitably lead to changes in the brain functional activities of seafarers. Therefore, it is of great significance to study the neural activity rules of seafarers’ brain. In view of this, this paper studied the seafarers’ brain alteration at the activated voxel level based on functional magnetic resonance imaging (...) technology by comparing the differences in functional connectivities between seafarers and non-seafarers. Firstly, the activated voxels of each group were obtained by independence component analysis, and then the distribution of these voxels in the brain and the common activated voxels between the two groups were statistically analyzed. Next, the FCs between the common activated voxels of the two groups were calculated and obtained the FCs that had significant differences between them through two-sample T-test. Finally, all FCs and FCs with significant differences between the common activated voxels were used as the features for the support vector machine to classify seafarers and non-seafarers. The results showed that DFCs between the activated voxels had better recognition ability for seafarers, especially for Precuneus_L and Precuneus_R, which may play an important role in the classification prediction of seafarers and non-seafarers, so that provided a new perspective for studying the specificity of neurological activities of seafarers. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

In the current research, sleep duration, sedentary behaviour, physical activity, and their relationship with several anxiety symptoms among college students were examined. This study was a cross-sectional study, and study respondents were recruited from college students. A total of 1,475 of college students were included for analysis. Sedentary behaviours and physical activity were assessed by the International Physical Activity Questionnaire Short Form, while sleep duration was assessed by the Chinese version Pittsburgh Sleep Quality Index. To assess the anxiety symptoms (...) of study respondents, the Generalised Anxiety Disorder-7 was used. The compositional data was analysed in R to estimate the associations between replacements among physical activities, sedentary behaviours, and sleep and anxiety symptoms. Results indicated a greater impact was imposed on the alleviation of anxiety symptoms by substituting sedentary behaviours with physical activity at moderate to vigorous intensity. In the current research, the significance of moderate to vigorous physical activity should be highlighted in preventing anxiety among Chinese college students. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and (...) approval. There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

This work is devoted to the testing, research and comparative analysis of the most well-known and widely used methods and algorithms for clustering numerical data sets. Multidimensional scaling was applied to evaluate the results of solving the clustering problem by visualizing datasets at all stages of the implementation of the studied algorithms. All algorithms were tested for artificial and real data sets. As a result, for each of the investigated algorithms, the main characteristics were formulated in (...) the form of their relative strengths and weaknesses. Based on the test results, conclusions and recommendations for using these algorithms are formulated. (shrink)

In terms of scenic marketing, big data research also plays an important role in the precise marketing of scenic spots. This paper has focused on the big data related to scenic spots as the research object, explores the relationship between various subdivision big data and the number of tourists in scenic spots, and investigates the difference and influence of the consumption behavior of the secondary consumption items in the scenic area, to find the potential of the scenic (...) area’s business growth and to promote the continuous and stable growth of the scenic area’s sales and tourism economy. Using the relevant theories and analysis methods, such as consumer behavior, big data, and tourism consumer behavior, the content mainly focuses on the establishment of the analysis model of the number of tourists in the scenic spot, the data collection, the estimation of the model parameters, the various types of big data, the calculation of the contribution rate of the data to the number of tourists in the scenic spot, and the difference analysis of the secondary consumption items of different types of tourists in the scenic spot. Results show that a multi-objective analysis model is established based on the relevant econometric theories, and an optimization plan is proposed after the multicollinearity diagnosis of the model; to establish a data envelopment analysis model of the difference and influence of different types of tourists’ consumption behavior in scenic spots and study the consumption behavior characteristics of different types of tourists when they purchase secondary consumption items in scenic spots; the econometric model is used to analyze the big data, adjust the linear relationship of some variables, then adopt the method of gradually adding variables combined with the consumer theory, and finally determine the number of daily tourists as the explained variable, the number of internet protocol, Baidu index, and the virtual value of the weekend, dummy variables for variables, bounce rate, and air pollution as explanatory variables. (shrink)

To explore the impact of academic burden on the physical and mental health of primary school students, combined with the results of the Programme for International Student Assessment report in 2018, the relationship among the development of mathematical literacy, mathematics academic burden, and the physical and mental health of primary school students is studied. First, the relationship between mathematical literacy and mathematics anxiety is analyzed, and related influencing factors and measurement methods of mathematics anxiety are introduced. A questionnaire is then (...) designed for primary school students’ mathematical stress, and the reliability and validity of the designed questionnaire are tested. Finally, a questionnaire survey is conducted on students, parents, and teachers in the third, fourth, and fifth grades of three standardized public primary schools. The results of the questionnaire survey show that students, teachers, and parents have a general understanding of the mathematics academic burden of primary school students at this stage. A total of 70% of teachers believe that primary school students have a heavy mathematics burden; 50% of parents think that primary school students are under heavy academic stress; 70% of primary school students believe that the heavy mathematics burden leads to reduced sleep time and extracurricular activities, which has a serious impact on the physical and mental health of primary school students. This research provides a reference for improving the current balance between education and students’ physical and mental health in China. (shrink)

The quality of the products coming from different lines of production requires some tests called comparative life tests. For lines having the same facility, the lifetime of the product is distributed by Burr XII, the lifetime distribution, and units are tested under type-I generalized hybrid censoring scheme. The observed censoring data are used under maximum likelihood and the Bayes method to estimate the model parameters. The theoretical results are discussed and assessed through data analysis and Monte Carlo (...) simulation study. Finally, we reported some brief comments obtained from numerical computation. (shrink)

Specific classes of cyberspaces emphasize different types of digital transactions given the user’s context, thus making it essential to take into account what these environments can afford. In this way, we can portray the niches of technological use as ecologies of particular possibilities and compare how they differ between distinct spheres of human life. The present research is focused on describing the conceptual integration of a taxonomic crossover between Virtual Learning Environments and Educational Affordances of Technology, while also performing empirical (...) testing and determining the psychometric properties in a scale regarding the aforementioned taxonomy. The study sample consisted of 320 students in the departments of foreign languages from three different universities in Sonora. Students were given a questionnaire of 21 items organized into four subscales with a Likert-type response option to measure the notions concerning their usage of Virtual Learning Environments. Internal consistency procedures and confirmatory factor analysis by means of Cronbach’s alpha and Structural modeling support the derived factorial structure, which contains Cyber-Communications, Virtual Behavior Settings, Virtual Communities, and Availability and Access to Connectivity. This structure traces the environmental properties perceived by learners in a virtual environment. Results sustain the initial conceptual construction regarding the proposed taxonomy, conclude that the ‘Virtual Learning Environments Questionnaire’ demonstrates adequate psychometric properties, and validate it as a fitting measure to assess the perceived psychological experience of students in a digital educational setting. (shrink)

I am working as both a TEFL teacher and an SLA researcher in China, doing SLA research. Recently, I have been working on new approaches to data analysis and I’ve found that a book titled “Data Visualization and Analysis in Second Language Research” by Dr. Guilherme D. Garcia is of great significance in empirical research in the field of SLA. This book serves only as a practical and user-friendly guide to beginners involved in SLA research, but (...) also navigation to veteran SLA researchers devoted to new perspectives in data analysis. So far, the author has made his first attempt to connect data visualization under R to SLA research. The author reviews the previous research results and suggests some modifications through running R. From my perspective, it fills a gap in the SLA research practice—to lead readers into a new track of visualizing their reported results. Overall, to make an increasingly higher demand for SLA research, this book is particularly designated for quantitative data analysis in SLA. It is strongly recommended to write a book review to introduce readers to the realm of data visualization and help most SLA researchers better understand the potential value of R and visualization in SLA research. (shrink)

Psychologists are directed by ethical guidelines in most areas of their practice. However, there are very few guidelines for conducting data analysis in research. The aim of this article is to address the need for more extensive ethical guidelines for researchers who are post–data collection and beginning their data analyses. Improper data analysis is an ethical issue because it can result in publishing false or misleading conclusions. This article includes a review of ethical (...) implications of improper data analysis and potential causes of unethical practices. In addition, current guidelines in psychology and other areas (e.g., American Psychological Association and American Statistical Association Ethics Codes) were used to inspire a list of recommendations for ethical conduct in data analysis that is appropriate for researchers in psychology. (shrink)

In the age of big data, machine learning models are globally used to execute default risk prediction. Imbalanced datasets and redundant features are two main problems that can reduce the performance of machine learning models. To address these issues, this study conducts an analysis from the viewpoint of different balance ratios as well as the selection order of feature selection. Accordingly, we first use data rebalancing and feature selection to obtain 32 derived datasets with varying ratios of (...) balance and feature combinations for each dataset. Second, we propose a comprehensive metric model based on multimachine learning algorithms to select the best-derived dataset with the optimal balance ratio and feature combination. Finally, the convolutional neural network is trained on the selected derived dataset to evaluate the performance of our approach in terms of type-II error, accuracy, G-mean, and AUC. There are two contributions in this study. First, the optimal balance ratio is found through the classification accuracy, which changes the deficiency of the existing research that samples are imbalanced or the balance ratio is 1 : 1 and ensures the accuracy of the classification model. Second, a comprehensive metric model based on the machine learning algorithm is proposed, which can simultaneously find the best balance ratio and the optimal feature selection. The experimental results show that our method can noticeably improve the performance of CNN, and CNN outperforms the other four commonly used machine learning models in the task of default risk prediction on four benchmark datasets. (shrink)

Abstract:For Dewey, growth in the educative process means education that enriches and expands one’s experience as it prepares students for not only a vocation but also entry into and transaction with the world. In few places can we see growth, generally understood, to be occurring as fast as in big data technology. This essay begins with an overview of what big data is, specifically what big data looks like in education as understood through learning management system platforms (...) but also data as understood generally on a college campus through dataveillance, or data surveillance understood as the monitoring or possibility of monitoring of one’s data or online activity. In so doing, this essay acknowledges a selection of privacy concerns and educational challenges resulting from big data. I then proceed to the employment of intelligent technology in education with particular emphasis on algorithms and “algorithmic nudging.” I conclude with a Deweyan analysis of technology as continuous with culture and a discussion about the educational needs for technological environments to aid in the proper cultivation of individuals. (shrink)

Recommending the identity of bidders in public procurement auctions has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest (...) classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset which has 102,087 tenders from 2014 to 2020 and a company dataset which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios. (shrink)

COVID-19 has made it difficult to adopt traditional face-to-face psychological intervention under this situation because of the blocked down and social distancing, which brings big psychological crisis to the public among the global. To explore the emotional change of the public in China at the outburst of the pandemic at different phases, to establish an online working platform and create a new model of an online intervention to hold public emotions under pandemic, and test its effectiveness, so to give advisement (...) for government emergency management system. We established an online organization to work for this program ad innovated a model of online group counseling with online emotional support accompany group right after the outburst of a pandemic. We analyzed 53 OESAGs from February 10 to April 9, including 555 application forms, 253 feedback from members, and 139 feedback from group leaders by using NVivo and SPSS to explore the evolution and characteristics of public emotion during COVID-19 and the effectiveness of OESAG. Our results showed that the emotional changes of members ranged from shock to depression to positive. The public's emotions swiftly changed from stress, anxiety, and isolation, to the hope of returning to work or finding a job during the pandemic with the help of OESAG. OESAG has effectively regulated the negative emotions of members by conducting psychological crisis intervention to provide members a space to communicate with each other, especially the female and frontline staff. Policy makers can set up an online systematic psychological crisis intervention system as soon as possible to make up for the lack of psychological assistance in the emergency management system. (shrink)

Infertility is a serious social problem. Very often the only treatment possibility are IVF methods. This study explores the possibility of outcome prediction in the early stages of treatment. The data, collected from the previous treatment cycles, were divided into four subsets, which corresponded to the selected stages of treatment. On each such subset, sophisticated data mining analysis was carried out, with appropriate imputations and classification procedures. The obtained results indicate that there is a possibility of predicting (...) the final outcome at the beginning of treatment. (shrink)

This study endorses the use of data envelopment analysis, which uses benefit-of-the-doubt weighting to evaluate the social, economic and overall performance of social enterprises. This methodology is especially useful for creating composite indicators based on multiple outputs expressed in different measurement units, and allows for enterprise-specific weighting of the different objectives. Applying this methodology on a unique longitudinal dataset of Flemish sheltered workshops suggests that social enterprises may face different types of mission drift. Further, our results show that (...) top-performing social enterprises are more economically and socially efficient than low performers. These top performers also have a stronger economic orientation, which sheds new light on the balance between social and economic orientations in social enterprises. (shrink)

Does direct democracy enhance political efficacy? This article examines the effect of direct democracy on political efficacy. Normative theorists have suggested that direct democracy has educative effects on citizens, such as promoting political efficacy. While a number of studies have examined the corresponding hypothesis, their empirical findings are not clear-cut. This study attributes the inconsistent results to two problems of the existing studies: the employment of cross-sectional data and the heterogeneity of popular vote issues. This study closes this gap (...) by examining the effect of direct democracy in local politics on political efficacy in a more systematic and controlled way. More concretely, it utilizes the Japanese case: In the first decade of this century, more than 400 Japanese municipalities held a popular vote for the first time because the Japanese national government promoted municipal merger. Therefore, the Japanese case provides multiple popular votes on comparable substantive topics that can be conceived as an homogeneous treatment. By applying multilevel modeling to panel survey data, this study demonstrates the causal effect that the popular vote increases the level of internal political efficacy. (shrink)

This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`.

On the basis of ecological footprint theory and tourism ecological footprint theory, the sustainable development indexes such as ecological footprint, ecological carrying capacity, ecological deficit, and ecological surplus of the research area were calculated and the long-term change pattern of each index was analyzed. This paper shows that the ecological footprint of the research area increases year by year, but the ecological footprint is always smaller than the ecological carrying capacity, indicating that the area is still in the state of (...) sustainable development. However, the per capita ecological surplus shows a decreasing trend year by year, indicating that the sustainable development of the region is getting worse. This paper proposes a reordering method of tourist attractions based on heterogeneous information fusion, and realizes the retrieval and reordering of tourist attractions based on user query and fusion of heterogeneous information, so as to help users make travel decisions. In view of the shortage of tourism commercial websites to passively provide scenic spot information, this paper puts forward a scenic spot retrieval method based on query words to enable users to obtain scenic spot information according to their needs, and constructs a tourist consumer data analysis system. The preprocessing methods and methods adopted by the data preprocessing module are analyzed in detail, and the algorithms used in the travel route analysis and consumer spending ability analysis are described in detail. The data of tourism consumers are analyzed by this system, and the results are evaluated. (shrink)

Background Since 2010 the People’s Republic of China has been engaged in an effort to reform its system of organ transplantation by developing a voluntary organ donation and allocation infrastructure. This has required a shift in the procurement of organs sourced from China’s prison and security apparatus to hospital-based voluntary donors declared dead by neurological and/or circulatory criteria. Chinese officials announced that from January 1, 2015, hospital-based donors would be the sole source of organs. This paper examines the availability, transparency, (...) integrity, and consistency of China’s official transplant data. Methods Forensic statistical methods were used to examine key deceased organ donation datasets from 2010 to 2018. Two central-level datasets — published by the China Organ Transplant Response System and the Red Cross Society of China — are tested for evidence of manipulation, including conformance to simple mathematical formulae, arbitrary internal ratios, the presence of anomalous data artefacts, and cross-consistency. Provincial-level data in five regions are tested for coherence, consistency, and plausibility, and individual hospital data in those provinces are examined for consistency with provincial-level data. Results COTRS data conforms almost precisely to a mathematical formula while Central Red Cross data mirrors it, albeit imperfectly. The analysis of both datasets suggests human-directed data manufacture and manipulation. Contradictory, implausible, or anomalous data artefacts were found in five provincial datasets, suggesting that these data may have been manipulated to enforce conformity with central quotas. A number of the distinctive features of China’s current organ procurement and allocation system are discussed, including apparent misclassification of nonvoluntary donors as voluntary. Conclusion A variety of evidence points to what the authors believe can only be plausibly explained by systematic falsification and manipulation of official organ transplant datasets in China. Some apparently nonvoluntary donors also appear to be misclassified as voluntary. This takes place alongside genuine voluntary organ transplant activity, which is often incentivized by large cash payments. These findings are relevant for international interactions with China’s organ transplantation system. (shrink)

In this article, a highly effective Bayesian sampling algorithm based on auxiliary variables is proposed to analyze aberrant response and response time data. The new algorithm not only avoids the calculation of multidimensional integrals by the marginal maximum likelihood method but also overcomes the dependence of the traditional Metropolis–Hastings algorithm on the tuning parameter in terms of acceptance probability. A simulation study shows that the new algorithm is accurate for parameter estimation under simulation conditions with different numbers of examinees, (...) items, and speededness levels. Based on the sampling results, the powers of the two proposed Bayesian assessment criteria are tested in the simulation study. Finally, a detailed analysis of a high-state and large-scale computerized adaptive test dataset is carried out to illustrate the proposed methodology. (shrink)

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine algorithm based on travel consumer sentiment analysis and (...) builds an Hadoop Distributed File System system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification. (shrink)

Student performance prediction aims to evaluate the grade that a student will reach before enrolling in a course or taking an exam. This prediction problem is a kernel task toward personalized education and has attracted increasing attention in the field of artificial intelligence and educational data mining. This paper provides a systematic review of the SPP study from the perspective of machine learning and data mining. This review partitions SPP into five stages, i.e., data collection, problem formalization, (...) model, prediction, and application. To have an intuition on these involved methods, we conducted experiments on a data set from our institute and a public data set. Our educational dataset composed of 1,325 students, and 832 courses was collected from the information system, which represents a typical higher education in China. With the experimental results, discussions on current shortcomings and interesting future works are finally summarized from data collections to practices. This work provides developments and challenges in the study task of SPP and facilitates the progress of personalized education. (shrink)

Context: The use of first-person micro-phenomenological interviews and their productive interaction with third-person physiological data is a challenging and pressing issue in order to offer an effective and fruitful application of Varela’s neurophenomenological hypothesis. Problem: We aim at offering a generative method of analysis of first-person micro-phenomenological interviews using third-person physiological data. Our challenge is to describe this generative first-person analysis with the third-person physiological framework rather than put Varela’s hypothesis into practice in a generative way (...) (as we did in another paper. Method: The present contribution is a first pioneering study as far as the exposition of such an interactive generative methodology is concerned. It is also a new issue insofar as it deals with a case study, surprise in depression, that has not been thoroughly dealt with so far, either in philosophy or in psychopathology. Results: We show that the analysis of first-person data is an intrinsic generative one, insofar as new refined categories and multifarious circular micro- and macro-processes were discovered in the very process of analyzing. They provide the initial structural generic third-person description of surprise inherited both from philosophical phenomenological a priori categories and from the experimental startle setting with a refined micro-segmentation of the dynamic of the experience. Implications: Our article could be of interest to neurophenomenologists looking for an effective application and to researchers in quest of a method of analysis of first-person data. The present limitations are due to the still preliminary data-results we need to complete. Constructivist content: The article is directly linked to Varela’s neurophenomenological program and aims at extending and reforming it with a cardio-phenomenological approach. Keywords: First-person micro-phenomenological interviews, surprise, generative analysis of first-person data, depression, cardio-phenomenology, generative categories. (shrink)

Since the beginning of the 21st century, sentiment analysis has been one of the most active research fields in natural language processing. Now sentiment analysis technology has not only achieved significant results in academia, but also has been widely used in practice. From business services to political campaigns, sentiment analysis is used in more and more fields. Sentiment analysis is essentially to dig out the user’s emotional attitude from the massive emotional natural language text data, (...) and analyze the emotional dynamics of the text author through certain technical means. At present, there is almost no sentiment analysis in cross-media writing content, and it can rarely help cross-media writing vision to advance with the times and comprehensive improvement of writing ability to adapt to the current rapidly developing information society; the commonly used text media in the digital age are not. Then there is the only composition tool. Various new media appearing with the development of the ages continue to intervene in writing, and it is the general trend to cultivate media literacy in writing. The main content of this paper is the research on the emotional intensity of students’ Internet public Aiming at the shortcomings of the topic feature word selection in the sentiment tendency analysis of the students’ Internet public opinion, improvements have been made to facilitate the research on the sentiment intensity of the sentiment analysis of the students’ Internet public opinion. Sentiment analysis of students’ cross-media written text through an improved MapReduce combinator model. (shrink)

At present, a series of economic structural changes created by the network economy have brought challenges to the entire economy and society. Traditional social commerce has also suffered severe tests under the background of network economy and global integration, and the rise and development of network commercial activities lack legal constraints. Based on the Big Data technology, in view of the characteristics of data mining services, this paper expands and changes the traditional model and proposes the Big (...) class='Hi'>Data alliance data mining service process model. Moreover, this paper uses intelligent decision-making theory and knowledge reasoning methods to construct a fast-responding, service-reusable, and intelligent service model to realize the scalability of data mining services. In addition, this paper constructs system function modules according to the requirements of Internet commercial judicial data mining and verifies the performance of the system constructed in this paper through experimental analysis. The research results show that the system constructed in this paper has certain practical effects. (shrink)

With the continuous development of software, it is inevitable that there will be various unpredictable problems in computer software or programs that will damage the normal operation of the software. In the paper, static analysis software is taken as the research object, the errors or failures caused by the potential defects of the software modules are analyzed, and a software analysis method based on big data tendency prediction is proposed to use the software defects of the stacked (...) noise reduction sparse analyzer to predict. This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. Through experimental tests, the performance of the presented method is better than that of the comparison method in correct rate, accuracy rate, recall rate, F1-measurement, AUC value, and running time, which proves that the research results in this paper have more accurate failure prediction effect and can timely eliminate software failures. (shrink)

Background Moral distress has negative effects on physical and mental health. However, there is little information about nurses’ coping strategies reducing moral distress. Aim The purpose of this study was to investigate the coping strategies of intensive care unit nurses reducing moral distress in Iran. Study design This is a qualitative study with a content analysis approach. Participants and research context The research sample consisted of nurses working in intensive care units of teaching hospitals affiliated to Tehran University of (...) Medical Sciences. Samples were selected among eligible nurses by purposive sampling. Data were obtained through 17 in-depth, individual, and semi-structured interviews with 17 nurses. Graneheim and Lundman’s (2004) 6-step content analysis method was used to analyze the data. Data management was also performed by MAXQDA software version 20. The COREQ checklist was used to report the study. Ethical considerations This project was approved by the Ethics Committee of Tehran University of Medical Sciences. All ethical guidelines in research were followed. Finding The data analysis resulted in the formation of two main categories of desirable coping strategies (with two subcategories of compensation and rejuvenation) and less desirable coping strategies (with three subclasses of indifference over time, escape and concealment). Discussion The intensive care unit nurses in dealing with ethical problems first try to solve the problem through discussion, but when they fail to resolve it peacefully, they resort to several coping strategies. Factors, such as increasing experience, lack of support from hospital managers and officials, poor communication between colleagues, the need to maintain hierarchy, fear of reprimand, and a sense of powerlessness, changed the nurses’ preferred strategies. It is important for managers to provide a blame/punishment-free atmosphere for expression of ethical experiences; a supportive atmosphere in which staff can engage in ethical discussions without fear of punishment. They should also provide opportunities for rest, rejuvenation, and adequate training for their employees. (shrink)

Road accidents have increased rapidly in recent years for a variety of reasons. Analyzing and visualizing road accidents through heatmaps can help improve policies for their prevention by informing about areas with a high-risk of road accidents. The purpose of this research is to build a model for the analysis and visualization of road accidents through heatmaps. Information about road accidents is extracted from the news of the main online media portals through scripts in the Python language and Web (...) Scraping techniques. From the extraction of about 30,000 articles from news portals for one year, only 829 were selected in the end that provided information about road accidents. As a result, and contribution of this research, a corpus was built with the geographic coordinates of road accidents and on this data our model was applied for the analysis and visualization of high-risk areas of road accidents using heatmaps. The visualization of heatmaps was done through a Python script, where it was applied to the geographic coordinates of road accidents. (shrink)

In the era of the global village, crowd sourcing as a new model of service outsourcing is increasingly being valued by all walks of life. This study uses the data envelopment analysis method to explain the crowd sourcing competitiveness of service outsourcing base cities by using input-output efficiency. The crowd sourcing competitiveness among crowd sourcing base cities is organized and analyzed by collating and analyzing the data of 21 service outsourcing base cities in China from 2016 to (...) 2019. The results show that there is no significant difference in the competitiveness of 21 service outsourcing, the overall trend is that the competitiveness of crowd sourcing is not strong, the match between input and output is not high. Comparatively speaking, Beijing, Shanghai, Nanjing, Hangzhou, Suzhou and Wuxi are more competitive in crowd sourcing. Combining with the reality of China's economic development, taking the road of crowd sourcing with Chinese characteristics is a good choice for the development of China's crowd sourcing industry. (shrink)

Industry sustainability plays a vital role in shaping the environment for cultural and creative business development. However, considering the influence of the external environment and random factors on the technical efficiency of cultural and creative industries with the inherent defects of the traditional data envelopment analysis model; this manuscript analyzed the operating efficiency of 56 cultural and creative enterprises using the three-stage DEA model from 2012 to 2018. An analysis of the results shows that differences in efficiency (...) exist between stage one and stage three DEA. Furthermore, the environmental elements and statistical noise measured by the stochastic frontier analysis in stage two reveal positive and negative influences on the creative cultural enterprises at different stages. As a result, the overall efficiency of the listed cultural and creative industries was revealed to be low in China. Finally, this study suggested effective countermeasures and recommendations for better-operating efficiency systems for cultural and creative enterprises. (shrink)

Banking firms are becoming increasingly aware that their clients’ management of environmental and social risks may in term threaten their own business as lenders and investors. In addition, stakeholders are requiring banks to improve their social performance. As a result, some banks are developing corporate social responsibility (CSR) policies and management systems to reduce potential risks and improve their performance. In the Spanish financial system, half of the banking firms are savings banks, most of which have always used some (...) Corporate Social Responsibility (CSR) criteria in their management. Private Banks have only recently started to integrate social aspects in their performance. However, no formal analysis has been carried out on the impact of CSR strategies. Various initiatives have been launched nationally and internationally to include the social dimension in management systems. The purpose of this research is to analyse the social performance of the main Spanish financial companies through public data such as social or sustainability reports and media sources. In order to do this, we need to determine which CSR criteria most greatly affect banking firms and to choose the most accurate quantitative and qualitative indicators to measure social performance. (shrink)

Studies show bilingualism entails an advantage in cognitive control tasks. There is evidence of a bilingual advantage in the context of aphasia, resulting in better cognitive outcomes and recovery in bilingual persons with aphasia compared to monolingual peers. This bilingual advantage also results in structural changes in the right hemisphere gray matter. Very few studies have examined the so-called bilingual advantage by reference to specific anomia therapy efficacy. This study aims to compare the effect of French-Phonological Component Analysis (Fr-PCA) (...) in monolinguals and bilingual persons with aphasia, both at the linguistic and cognitive control level, and to examine the structural impact of left hemisphere lesion location and right hemisphere structural data. Eight participants with chronic aphasia received Fr-PCA for a total of 15 h over 5 weeks. The results showed improved accuracy for treated words and generalization to untreated items and discourse in both groups, and improved Flanker task performance for some participants. Bilingual participants improved more than monolinguals for picture-naming tasks and narrative discourse. Damage to the left postcentral gyrus and the middle frontal gyrus was associated with less therapy-induced improvement. Additionally, left hemisphere damage to the inferior parietal gyrus and postcentral gyrus was associated with reduced cognitive control pre-therapy. Undamaged right hemisphere cortical thicknesses were significantly different between groups; the inferior frontal gyrus and the middle frontal gyrus were greater for the bilingual participants and correlated with cognitive control skills. These results suggest a bilingual advantage in anomia recovery following Fr-PCA, potentially resulting from enhanced cognitive control abilities that could be supported by right hemisphere neural reserve. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship (...) between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)

Doctoral graduates in philosophy are an excellent source of information about the discipline: they are at the cutting edge of research trends, have an inside view of researchfocused departments, and their employment prospects provide early insights on the future health of the discipline. We report on the results of a survey sent to recent PhD graduates and current students, as well as data gathering efforts by Academic Placement Data and Analysis that have taken place over the past (...) ten years. In this report we especially focus on demographic representation, program ratings, academic job placement, and non-academic careers. (shrink)

. Network data analysis helps in capturing node usage behavior. Existing algorithms use reduced feature set to manage high runtime complexity. Ignoring features may increase classification errors. This paper presents a model, allowing classification of network traffic, while considering all the relevant features. Learning phase partitions training sample on values of the respective features. This creates equivalence classes related to m features. During classification, each feature value of the test instance results in picking one set from equivalence class (...) generated during learning. Algorithm captures new behavior in semi-supervised incremental learning mode. For problems having m features and n training samples the model has incremental learning complexity of and average classification complexity is of the order. (shrink)

Recent experimental studies dispute the position that commonsense morality accepts ‘Ought’ Implies ‘Can’, the view that, necessarily, if an agent ought to perform some action, then she can perform that action. This paper considers and supports explanations for the results of these studies on the hypothesis that OIC is intuitive and true.

BackgroundKinarm Standard Tests is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior. We have previously used principal component analysis to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab. Here, we performed PCA using data from the Kinarm Exoskeleton Lab, and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined (...) whether further dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO and EP. Four behavioral tasks were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching ] and three motor tasks [Visually Guided Reaching, Object Hit, and Object Hit and Avoid ]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type was decided on a per-task basis. To assess agreement, we compared principal components across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58–75% while accounting for 76–87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations. (shrink)

Background. The SA DTA was updated to better serve the South African research community by providing clarity on exactly when – at what stage during research – institutions hold rights to the data they generate in collaborative research contexts where raw data are received and integrated with other data. SA DTA 1.1 introduces significant enhancements in data governance, focusing on the explicit definition and management of ‘inferential data’. Objectives. To introduce SA DTA 1.1 and demonstrate (...) its practical application in real-world research contexts. Methods. Using a descriptive design, the study presents two practical case studies: one involving two universities and another a university and a pharmaceutical company. The first case focuses on identifying genetic markers for neurodegenerative diseases, while the second addresses genetic markers for chronic diseases. Results. In the first case study, the entity conducting the analysis generated inferential data independently, thereby gaining sole rights in such data. In the second case study, both collaborating entities contributed to the data analysis, leading to joint rights in the inferential data. These findings demonstrate the flexibility and clarity provided by SA DTA 1.1 in managing data ownership and intellectual property rights. Conclusion. SA DTA 1.1 significantly advances data governance by providing clear and adaptable guidelines for data rights in collaborative research. It supports ethical and efficient data sharing, protecting researchers’ interests and fostering global scientific innovation. (shrink)