The marketization of interest rate is an inevitable requirement for China’s financial reform and joining the WTO to connect with the international financial market. It is also an important link to improve the marketization degree of China’s financial system. The marketization of interest rate in China is gradually advancing according to its preset mode. In the process of interest rate marketization, an unavoidable problem is that while the interest rate marketization gives the commercial banks the autonomy of capital pricing, the (...) fluctuation of interest rate is more and more frequent. However, due to the fluctuation of interest rate, the loan as the main assets of commercial banks will be prepayed by borrowers, and the time deposit as the main liabilities of commercial banks will be withdrawn by depositors in advance; that is, embedded options are implied in asset liability items, which makes it difficult for commercial banks to accurately calculate the actual interest margin of deposits and loans and manage the interest rate risk. Therefore, it is of great significance to identify and price such embedded option value. On the basis of identifying and decomposing the embedded options in deposit and loan of commercial banks, according to the change characteristics of deposit and loan interest rate of Chinese commercial banks, this paper chooses jump-diffusion interest rate model to describe the change of benchmark interest rate of deposit and loan in China and demonstrates the advantages of this model compared with other models. Based on Monte Carlo simulation technology, the embedded options of five-year fixed deposit and ten-year prepayable loan in China are priced. On this basis, it points out that the real interest margin of commercial bank’s deposit and loan should be the nominal interest margin minus the value of deposit and loan’s embedded options. In the process of interest rate risk management, we should pay attention to the existence of embedded options and carry out effective management. (shrink)

Recently, there has been growing interest in the use of ontology as a fundamental tool for representing domain-specific conceptual models to improve the semantics, accuracy, and relevance of domain users’ query results. Although the amount of data has grown steadily over the past decade, much data shares similar characteristics that can be captured by a foundational ontology. In this paper, we show how queries based on a foundational ontology can be evaluated and their performance measured. We also present a (...) Foundational Patterns benchmark to help select the most efficient triple memory and its layout. We evaluate the foundational benchmark with both generated and real datasets for state-of-the-art triple stores. (shrink)

This paper analyzes the impacts of index inclusions and exclusions on corporate sustainable firms by studying a sample of US stocks that are added to or deleted from the Dow Jones Sustainability World Index over the period 2002-2008. The impacts are measured in terms of stock return, risk and liquidity. We cannot find any strong evidence that announcement per se has any significant impact on stock return and risk. However, on the day of change, index inclusion (exclusion) stocks experience a (...) significant but temporary increase (decrease) in stock return. Liquidity deteriorates after the announcement day and bounces back significantly near the day of change. Systematic risk shows little change after announcements. But, idiosyncratic risk is higher after announcements. The overall results support Harris and Eitan's (The Journal of Finance 41(4), 815-829, 1986) price pressure hypothesis, which posits that event announcement does not carry information and any shift in demand (and hence the corresponding price change and liquidity change) is temporary. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm (...) accuracy is tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

This paper considers ethical decision making by blending three streams of related research: cognitive moral development of the decision maker, rational choice theory and a subjective expected utility model. Ethical dilemmas can be defined as situations where moral certainty is compromised by rational cognition. In this paper, the authors assume that some people use a morality-first perspective and others a rationality-first perspective. Ethical scenarios were written and used to test hypotheses derived from this perspective. The instrument developed was shown (...) to be in need of further refinement. Results are discussed in terms of relationships between participant-characteristics variables overall and subscale responses to the ethical scenarios. (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)

Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep learning has brought a turnaround for (...) this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm. (shrink)

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words more easily than unrelated words; this advantage increased linearly with repeated co-occurrence. Second, we developed a computational model, SEMANT, suggesting a possible mechanism for this semantic-episodic interaction. In SEMANT, episodic associations are implemented (...) through lateral connections between nodes in a pre-existent self-organized map of word semantics. These connections are strengthened at each instance of concomitant activation, proportionally with the amount of the overlapping activity waves of activated nodes. In computer simulations SEMANT replicated the dynamics of associative learning in humans and led to testable predictions concerning normal associative learning as well as impaired learning in a diffuse semantic system like that characteristic of schizophrenia. (shrink)

Agent-based models (ABMs) are one type of simulation model used in the context of the COVID-19 pandemic. In contrast to equation-based models, ABMs are algorithms that use individual agents and attribute changing characteristics to each one, multiple times during multiple iterations over time. This paper focuses on three philosophical aspects of ABMs as models of causal mechanisms, as generators of emergent phenomena, and as providers of explanation. Based on my discussion, I conclude that while ABMs cannot (...) help much with causal inference, they can be viewed as etio-prognostic explanations of illness occurrence and outcome. (shrink)

Base of the pyramid ventures seek to create “mutual value” for themselves and poor communities, but often use business models unadapted for the BoP context, and have been less successful than hoped. Sustainable business models’ multi-stakeholder lens offers a promising alternative path to mutual value, but BoP-based SBM studies are scarce. This single case study explores whether and how SBM characteristics manifest in the business model and value outcomes of Habi, a Manila footwear company successfully creating mutual value (...) with BoP suppliers. We find SBM characteristics underpin Habi’s dual-structure business model and success in four ways: viewing profits as a tool for community development resulted in designing both product and business model around community strengths; understanding communities as systems helped Habi address the complexities of poverty; balancing short-term business needs with a long-term, slow-growth approach led to their choice of investors; and implementing community value capture mechanisms ensured enduring community benefit. (shrink)

Modelling cannot be characterized as isolating, nor models as isolations. This article presents three arguments to that effect, against Uskali Mäki's account of models. First, while isolation proceeds through a process of manipulation and control, modelling typically does not proceed through such a process. Rather, modellers postulate assumptions, without seeking to justify them by reference to a process of isolation. Second, while isolation identifies an isolation base—a concrete environment it seeks to control and manipulate—modelling typically does not identify such a (...) base. Rather, modellers construct their models without reference to concrete environments, and only later seek to connect their models to concrete situations of the real world. Third, Mäki argues that isolation employs idealization to control for disturbing factors, but does not affect the factors or mechanisms that are supposed to be isolated. However, models typically make idealizing assumptions about the factors and mechanisms that are the focus of investigation. Thus, even the product of modelling often cannot be characterized as isolation. (shrink)

This paper studies the problems of tip position regulation and vibration suppression of flexible manipulators without using the model. Because of the two-timescale characteristics of flexible manipulators, applying the existing model-free control methods may lead to ill-conditioned numerical problems. In this paper, the dynamics of a flexible manipulator is decomposed into two subsystems which are linear and controllable at different timescales by singular perturbation theory and a model-free composite controller is designed to alleviate the ill-conditioned numerical problems. (...) To do this, a model-free composite control strategy is constructed which facilitates in designing the controller in slow and fast timescales. In the slow timescale, the slow subsystem controller is designed by adaptive dynamic programming based on the measurements of the slow inputs and the position, while the vibration in the slow timescale is estimated by the least square method. In the fast timescale, the vibration is reconstructed based on the measurements of vibration and its estimate in the slow timescale, by which the fast controller is designed using ADP. Stability of the closed-loop system is proved by SP theory. Finally, simulations are given to show the feasibility and effectiveness of the proposed methods. (shrink)

Hospitality industry plays a crucial role in the development of tourism. Predicting the future demand of a hotel is a key step in the process of hotel revenue management. Hotel passenger flow prediction plays an important role in guiding the formulation of hotel pricing and operating strategies. On the one hand, hotel passenger flow prediction can provide decision support for hotel managers and effectively avoid the waste of hotel resources and loss of revenue caused by the loss of customers. On (...) the other hand, it is the guarantee of the priority occupation of business opportunities by hotel enterprises, which can help hotel enterprises adjust their operation strategies reasonably to better adapt to the market situation. In addition, hotel passenger flow prediction is helpful to judge the overall operating condition of the hotel industry and assess the risk level of the hotel project to be built. Hotel passenger flow is affected by many factors, such as weather, environment, season, holidays, economy, and emergencies, and has the characteristics of complex nonlinear fluctuation. The existing demand predicting methods include linear methods and nonlinear methods. The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. Traditional nonlinear prediction methods need to improve the prediction accuracy, and they are difficult to deal with the increasing data of hotel passenger flow. Based on the above analysis, this paper constructs a deep learning prediction model based on Long Short-Term Memory to predict the number of actual monthly arrival bookings. The number of actual monthly arrival bookings can reflect the actual monthly passenger flow of a hotel. The prediction model can effectively reduce the loss caused by cancellation or nonarrival of bookings due to various reasons and improve the hotel revenue. The experimental part of this paper is based on the booking demand dataset of a resort hotel in Portugal from July 1, 2015, to August 31, 2017. Artificial neural network and support vector regression are built as benchmark models to predict the number of actual monthly arrival bookings of this hotel. The experimental results show that, compared with the benchmark models, the LSTM model can effectively improve the prediction ability and provide necessary reference for the hotel's future pricing decision and operation mode arrangement. (shrink)

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy (...) of the NEGM model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM. Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM model has the best prediction performance among these competitive models. (shrink)

Writing in the Business and Politics, Santos and Laczniak (Business and Politics 14(1) 2012) formulated a normative, ethical approach to be followed when marketers e ngage impoverished market segments. It is labeled the integrative justice model (IJM). As noted below, that approach called for authentic engagement, co-creation, and customer interest representation, among other elements, when transacting with vulnerable market segments. Basically, the IJM derived certain operational virtues, implied by moral philosophy, to be used when marketing to the poor. But (...) this well-intentioned approach raises a significant “So what?” question. Are such sentiments anything but lofty aspirations for idealists or are there steps to be taken by society and business managers of goodwill to make the adaptation of the IJM by corporations more likely and pragmatic? This paper begins to layout a roadmap that shows “how and why” the IJM might more likely be vitalized. The crux, as described below, is found in the transformational justice dimensions that are embedded in institutions (and supporting institutional arrangements); such external institutions provide a “power” impetus to assure the ethical rights claims that impoverished consumers have owed to them. In this way, the ideal exchange characteristics for bottom (or base) of the pyramid (BoP) markets argued for in the IJM can become actively transformational. The main contribution of this paper is that it begins to chart out the institutional system elements that need to exercise power in order to deliver a “fairer” marketplace for BoP consumers. (shrink)

Solvency assessment is the core content of insurance supervision. In this paper, from the perspective of capital flow, the insurance company’s capital flow is regarded as a dynamic system, the stochastic differential equations model is established to describe its flow characteristics, and the existence of positive equilibrium point of the system is proved, as well as the conditions of stability at equilibrium point, that is, the requirements of the insurance company’s solvency. Furthermore, by using the numerical simulation method, we (...) get the strategy of insurance companies to deal with the solvency shortage when facing the change of external environment, and the strategy of insurance company to deal with solvency shortage is obtained. (shrink)

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which (...) has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task. (shrink)

Johnson -Laird has argued that spatial reasoning is based on the construction and manipulation of mental models in memory. The present article addresses the question of whether reasoning about time relations is constrained by the same factors as reasoning about spatial relations. An experiment is reported that explored the similarities and the differences in the performance of subjects in comparable spatial and temporal reasoning tasks. The results indicated that, in both the temporal and the spatial content domains, the data (...) were in agreement with the view that subjects solved problems by constructing models in memory rather than with a logical rule conceptualisation of reasoning. An analysis of the premise-reading times on the basis of premise-linking order provided support for an on-line process of mental models construction, and offered an explanation for the finding that spatial problems that did require an inference of transitivity were easier than problems that did not. No essential differences in processing and performance were observed across the two content domains, although in the time domain the correctness data were in agreement with both the mental models theory and the logical rules view. The results are discussed with respect to the mental models theory and the structural characteristics of the problems. (shrink)

The semantic method of extension and intension presented by Rudolf Carnap may have some problems from the current perspective in cognitive science. For example, that method is strongly linked to logic, and it is hard to determine where its state-descriptions come from, how many they are, and what the relation of accessibility between them is. This paper tries to solve difficulties such as those by updating the method resorting to the theses and results of a contemporary reasoning theory: the theory (...) of mental models. Basically, the update consists of replacing the state-descriptions of the original method with the possibilities of the theory of mental models. That allows reformulating several relevant concepts in Carnap’s method in order to better adjust them to the real characteristics of human language. (shrink)

Chronic pain is one of the most disabling medical conditions globally, yet, to date, we lack a satisfying theoretical framework for research and clinical practice. Over the prior decades, several frameworks have been presented with biopsychosocial models as the most promising. However, in translation to clinical practice, these models are often applied in an overly reductionist manner, leaving much to be desired. In particular, they often fail to characterize the complexities and dynamics of the lived experience of chronic pain. Recently, (...) an enactive, affordance-based approach has been proposed, opening up new ways to view chronic pain. This model characterizes how the persistence of pain alters a person’s field of affordances: the unfolding set of action possibilities that a person perceives as available to them. The affordance-based model provides a promising perspective on chronic pain as it allows for a systematic investigation of the interactive relation between patients and their environment, including characteristic alterations in the experience of their bodies and the space they inhabit. To help bridge the gap from philosophy to clinical practice, we unpack in this paper the core concepts of an affordance-based approach to chronic pain and their clinical implications, highlighting aspects that have so far received insufficient attention. We do so with an analogy to playing video games, as we consider such comparative illustration a useful tool to convey the complex concepts in an affordance-based model and further explore central aspects of the lived experience of chronic pain. (shrink)

Intelligent Guided Learning System is a computer system that uses computers to imitate the experience and methods of teaching experts to assist in teaching; ITS provides learners with personalized learning resources and adaptive teaching methods, thus reducing students’ dependence on teachers and realizing independent learning. After years of research and development, and with the help of artificial intelligence technology, ITS has basically formed a stable structure and unified implementation specifications and has given birth to many excellent products in disciplines such (...) as basic computer, language, medicine, and mathematics. Based on the above background, this paper takes the system structure of universal ITS as the basis, combines the characteristics of music sight-singing subject in teaching contents and teaching methods, researches the intelligent guidance system model of music sight-singing, and completes the design of the overall system architecture. The specific strategies and implementation methods of the teaching methods, resource recommendation, and ability assessment components of the teaching model are studied. The research includes the definition of the difficulty characteristics of the score, the design of the score recommendation algorithm, the design of the sight-singing scoring algorithm, and the experimental analysis of each algorithm. It is proposed that the difficulty feature-based score recommendation algorithm is the core component of resource recommendation in the teacher model, and the sight-singing scoring algorithm is the basis for ability assessment and learner model update. (shrink)

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production, heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, (...) and sustainability in the industry. This paper presents an economic model of a microgrid system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression model was optimized by the gravitational search algorithm. The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic, CCHP system, fuel cell, and energy storage devices are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model. (shrink)

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners’ knowledge status over time by analyzing the learners’ exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the (...) knowledge components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners’ knowledge status and questions. A context-aware attentive knowledge query network model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners’ interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners’ knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners’ knowledge status like other models but also model learners’ forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms. (shrink)

Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this (...) concern, philosophers of science have started analyzing scientific representations in terms of fictionalization strategies. Many arguments center on a dyadic relation between the model and its target system, focusing on structural resemblances and “as if” scenarios. This chapter provides a different approach. It looks more closely at model building to analyze the interpretative strategies dealing with the representational limits of models. How do we interpret ambiguous elements in models? Moreover, how do we determine the validity of model-based inferences to information that is not an explicit part of a representational structure? I argue that the problem of ambiguous inference emerges from two features of representations, namely their hybridity and incompleteness. To distinguish between fictional and non-fictional elements in scientific models my suggestion is to look at the integrative strategies that link a particular model to other methods in an ongoing research context. To exemplify this idea, I examine protein modeling through X-ray crystallography as a pivotal method in biochemistry. (shrink)

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. (...) The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR (...) application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit layer configurations were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters. (shrink)

Writing in the Business and Politics, Santos and Laczniak 2012) formulated a normative, ethical approach to be followed when marketers e ngage impoverished market segments. It is labeled the integrative justice model. As noted below, that approach called for authentic engagement, co-creation, and customer interest representation, among other elements, when transacting with vulnerable market segments. Basically, the IJM derived certain operational virtues, implied by moral philosophy, to be used when marketing to the poor. But this well-intentioned approach raises a (...) significant “So what?” question. Are such sentiments anything but lofty aspirations for idealists or are there steps to be taken by society and business managers of goodwill to make the adaptation of the IJM by corporations more likely and pragmatic? This paper begins to layout a roadmap that shows “how and why” the IJM might more likely be vitalized. The crux, as described below, is found in the transformational justice dimensions that are embedded in institutions ; such external institutions provide a “power” impetus to assure the ethical rights claims that impoverished consumers have owed to them. In this way, the ideal exchange characteristics for bottom of the pyramid markets argued for in the IJM can become actively transformational. The main contribution of this paper is that it begins to chart out the institutional system elements that need to exercise power in order to deliver a “fairer” marketplace for BoP consumers. (shrink)

In response to the big data era trend, statistics has become an indispensable part of mathematics education in junior high school. In this study, a pre-test and a post-test were developed for the six attributes of the data distribution characteristic. This research then used the cognitive diagnosis model to learn about the poorly mastered attributes and to verify whether cognitive diagnosis can be used for targeted intervention to improve students' abilities effectively. One hundred two eighth graders participated in (...) the experiment and were divided into two groups. Among them, the intervention materials read by the experimental group students only contained attributes that they could not grasp well. In contrast, the reading materials of the control group were non-targeted. The results of the study showed the following: The variance and the weighted average were poorly mastered by students in the pre-test; compared with the control group, the average test score of the experimental group was significantly improved; in terms of attributes, the experimental group students' mastery of variance and the weighted average was significantly improved than the pre-test, while the control group's mastery was not. Based on this, some teaching suggestions were put forward. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ (...) elements of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research (...) on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud. (shrink)

People suffering from Obsessive-Compulsive Disorder (OCD) do things they do not want to do, and/or they think things they do not want to think. In about 10 percent of OCD patients, none of the available treatment options is effective. A small group of these patients is currently being treated with deep brain stimulation (DBS). Deep brain stimulation involves the implantation of electrodes in the brain. These electrodes give a continuous electrical pulse to the brain area in which they are implanted. (...) It turns out that patients may experience profound changes as a result of DBS treatment. It is not just the symptoms that change; patients rather seem to experience a different way of being in the world. These global effects are insufficiently captured by traditional psychiatric scales, which mainly consist of behavioural measures of the severity of the symptoms. In this article we aim to capture the changes in the patients’ phenomenology and make sense of the broad range of changes they report. For that we introduce an enactive, affordance-based model that fleshes out the dynamic interactions between person and world in four aspects. The first aspect is the patients’ experience of the world. We propose to specify the patients’ world in terms of a field of affordances, with the three dimensions of broadness of scope (‘width’ of the field), temporal horizon (‘depth’), and relevance of the perceived affordances (‘height’). The second aspect is the person-side of the interaction, that is, the patients’ self-experience, notably their moods and feelings. Thirdly, we point to the different characteristics of the way in which patients relate to the world. And lastly, the existential stance refers to the stance that patients take towards the changes they experience: the second-order evaluative relation to their interactions and themselves. With our model we intend to specify the notion of being in the world in order to do justice to the phenomenological effects of DBS treatment. (shrink)

This paper outlines the demographic characteristics of the Baiga tribe, one of the most primitive of the aboriginal tribal groups of Central India. The Baiga population has grown steadily since the first anthropological study of the tribe in the 1930s. Age at menarche, age at marriage, breast-feeding, and time interval between marriage and first conception are natural. There are more females than males. Sub-tribe endogamy is common; consanguineous marriage is favoured (34% of marriages are between first cousins) and marital distance (...) is low (mean 7·1 km). Though the mean menarcheal age is high (15·2 years), mean age at first marriage is low (16·6 years), giving a long reproductive period. (shrink)

Adopting fair value measurement may bring more earnings fluctuations and induce irrational psychology and radical financing behavior of managers. Based on behavioral corporate governance theory, using the sample of Chinese A-share nonfinancial listed companies during 2007–2017, this paper empirically examines the regulatory effect of fair value measurement, that is, whether fair value measurement affects the company's financing decisions when managers have irrational psychological characteristics, i.e., overconfidence. The study found that overconfident managers of the company that have fair value measurement (...) assets will be more aggressive for debt decisions, indicating that fair value measurement has a positively regulatory effect on overconfident managers. (shrink)

Manuscript reviews are intended to be objective, empirical assessments of the scientific worth of papers submitted for publication. However, critics have charged that manuscript reviews are unreliable, unconstructive, and biased in a number of ways . A review of the empirical literature in this area indicates: that inter-reviewer reliability in manscript assessments is clearly inadequate, that reviewer bias can sometimes influence manuscript assessments, and that there is a dearth of empirical data supporting the predictive and discriminant validity of manuscript assessment (...) procedures. Based on the available evidence it seems that manuscript reviews are more strongly influenced by chance factors than by systematic reviewer or editorial bias. Nonetheless, our desire to conceptualize manuscript reviews in psychology as objective, empirical assessments has produced a number of undesired results. An alternative approach to manuscript review based on an adversery model rather than a scientific model is presented. Advantages of an adversary model as a method for identifying sound research are discussed. Changes in current publication policies that would allow research findings to be disseminated more efficiently are also described. (shrink)

Food safety risk is becoming a vital issue for public health, and improving public awareness of FSR through social media is necessary. This study aims to explore specific mechanisms of FSR perception; it first categorizes 19 risk characteristics into two variables, dread and efficacy, and then examines how social media use affects perceived FSR through both variables. Additionally, the study explores the moderating effects of source credibility and science literacy on the mechanisms of FSR perception. Based on a nationwide (...) online survey of more than six salient food safety issues in China, the study found that exposure to food safety risk information on social media can help improve perceived FSR based on the proposed “dread–efficacy processing model”, where dread stimulates perceived risk, while efficacy suppresses risk perception. Moreover, source credibility intensifies the effect of social media use on efficacy appraisal, whereas science literacy exerts a “double-weakening” influence on dread appraisal. Theoretical and practical implications of the findings are discussed. (shrink)

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by (...) the LDA topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics; there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda; the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...) derived from subliminal learning, conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks (e.g., artificial grammar learning) is predominantly based on rule abstraction is questionable. When subjects cannot report the rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

Current research considers business model innovation as a series of responses to technological change and market environment change. However, in practice, it is often business model innovation that leads to market innovation and subversion and then promotes a new round of iterative product renewal. This is because business model innovation is a value creation activity based on market demand, rather than a technology-oriented innovation behavior. Moreover, since the degree of demand manifestation varies, the degree of its (...) influence on value creation and thus the driving mechanism for business model innovation vary significantly, and the duality of market-oriented theory is introduced to explore how firms go about achieving business model innovation based on different demand characteristics and how the results of firms’ integration of market information work. Through regression analysis of 230 sample enterprises, the results show that both responsiveness and proactive market orientation have a significant positive impact on business model innovation. In contrast, market information integration has a positive regulatory effect on the two groups of relationships. (shrink)

A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...) conditioning, artificial grammar learning, instrumental learning, and reaction times in sequence learning. We conclude that unconscious learning has not been satisfactorily established in any of these areas. The assumption that learning in some of these tasks is predominantly based on rule abstraction is questionable. When subjects cannot report the “implicitly learned” rules that govern stimulus selection, this is often because their knowledge consists of instances or fragments of the training stimuli rather than rules. In contrast to the distinction between conscious and unconscious learning, the distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning. We discuss various computational models of these two forms of learning. (shrink)

Sports energy consumption is a quantitative reflection of physical exercise effect. Combined with different sports modes and students’ physical characteristics, the calculation model of sports energy consumption is put forward. Firstly, the relationship between students’ age, height, weight, gender, and energy consumption is analyzed by using multiple linear regression method, and a linear acceleration model is proposed by combining different exercise methods. The relationship between the integral value of acceleration and energy consumption is analyzed, and a linear integral (...) model based on different motion modes is proposed. Based on the kinetic energy theorem, the student movement energy expenditure is estimated. This paper proposes a human movement recognition method based on hybrid features, which mostly can represent the curve of the second generation wavelet transform edge thinning, and from the edge and texture features of the optimal said human posture, the statistical characteristic of the second generation wavelet transform is subtly trained as image characteristics, learning and recognition of human movement. Then, the motion recognition algorithm is tested, which can effectively identify the common movement patterns of primary and middle school students. Finally, the linear relationship between the estimation results of the model and the calculation results of Meijer is analyzed. The analysis results show that the linear acceleration model proposed in this paper can estimate the energy consumption of primary and middle school students’ motion relatively accurately. (shrink)

The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current (...) practice. The differences between the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign ‘single event’ probabilities, and [iii] its reliance on ‘random samples’. It is argued that charges [i]–[ii] stem from misidentifying the frequentist ‘long-run’ with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. (shrink)

A genuine commitment to ethics and compliance (E&C) programs means that top management adopt them for what they represent and not for other purposes. Only then can they truly build socially responsible behavior and a successful and sustainable business, as stated in the latest international standard for compliance management practice (ISO 37301:2021), which we found to be consistent with a new business narrative as conceptualized in Freeman's work. However, it also requires that top managers place a moral value on these (...) practices, rather than simply using them for instrumental reasons. Building on Kohlberg's six stages of moral development as applied to managers' moral thinking, this manuscript offers a model to explain how top managers' moral valuing of these practices varies along a moral reasoning continuum, resulting in four distinct modes. It also theorizes that each mode of moral valuing yields an archetype of E&C programs. It thus offers a new approach that contributes to the business ethics and management literature by incorporating a morally grounded perspective on the adoption and implementation of E&C practices and how it might shape their features and characteristics. It also aims to improve professional practice and business contributions to Freeman's ideas through genuine ethics‐based E&C programs. (shrink)

In order to obtain continuous stratum information during TBM tunneling, using TBM tunneling parameters, stratum recognition is carried out through the K-nearest neighbor model, and the model is improved by the entropy weight method to improve the stratum recognition rate. By analyzing the correlation between TBM tunneling characteristic parameters and stratum, the tunneling characteristic parameter vector which is most sensitive to the stratum is obtained by sensitivity analysis, and the stratum recognition model based on (...) the K-nearest neighbor algorithm is established. Aiming at the problem that the model has a large error in complex formation recognition, a formation recognition model based on the entropy weight K-nearest neighbor algorithm is established, and the wrong data of the K-nearest neighbor model is recalculated. The recognition rate of the stratum in the new model is increased from 90.95% to 98.55%. The results show that the K-nearest neighbor model has a better recognition effect for the interval with single stratum distribution, and the recognition rate of entropy weight K-nearest neighbor model for complex stratum is significantly improved, which provides an effective method to obtain stratum information by using tunneling characteristic parameters. (shrink)

The spatiotemporal agglomeration of industries is the most prominent geographical feature of economic activities. Based on the analysis of the spatiotemporal distribution of China’s construction industry agglomeration, this paper analyzes the characteristics and evolution trend of the spatiotemporal agglomeration of construction industry in 31 provinces and cities of China from 2010 to 2019 by using Moran’s index and the spatiotemporal transition measurement model. The findings are as follows: China’s construction industry has experienced two stages in terms of time: (...) steady rise and turbulent rise. Spatially, China’s construction industry, as a whole, the space takes the shape of one horizontal and two vertical, similar to the letter “H” being crossed. And the difference of “East-West” two ends of the industrial agglomeration level is obvious. The Yangtze River Delta Urban Agglomerations, the Pearl River Delta Urban Agglomerations, Beijing-Tianjin-Hebei Urban Agglomerations, and the western region have significant local features. The four major types of China’s construction industry cluster, which are H-H, H-L, L-H, and L-L, are formed. The time-space transition of China’s construction industry is dominated by the “stable transition” mode. The transition inertia is significant. The regional development has strong path dependence and spatial locking characteristics. (shrink)

In 1973, Nickles identified two senses in which the term `reduction' is used to describe the relationship between physical theories: namely, the sense based on Nagel's seminal account of reduction in the sciences, and the sense that seeks to extract one physical theory as a mathematical limit of another. These two approaches have since been the focus of most literature on the subject, as evidenced by recent work of Batterman and Butterfield, among others. In this paper, I discuss a (...) third sense in which one physical theory may be said to reduce to another. This approach, which I call `dynamical systems reduction,' concerns the reduction of individual models of physical theories rather than the wholesale reduction of entire theories, and specifically reduction between models that can be formulated as dynamical systems. DS reduction is based on the requirement that there exist a function from the state space of the low-level model to that of the high-level model that satisfies certain general constraints and thereby serves to identify quantities in the low-level model that mimic the behavior of those in the high-level model - but typically only when restricted to a certain domain of parameters and states within the low-level model. I discuss the relationship of this account of reduction to the Nagelian and limit-based accounts, arguing that it is distinct from both but exhibits strong parallels with a particular version of Nagelian reduction, and that the domain restrictions employed by the DS approach may, but need not, be specified in a manner characteristic of the limit-based approach. Finally, I consider some limitations of the account of reduction that I propose and suggest ways in which it might be generalised. I offer a simple, idealised example to illustrate application of this approach; a series of more realistic case studies of DS reduction is presented in another paper. (shrink)

In this paper, we investigate the model of decentralized groupwise e-commerce through regional chains combined with edge computing algorithms and design and implement it. A blockchain-based architecture for remote sensing resource and service transactions is proposed. The architecture utilizes the characteristics of nontamperable and traceable data on the blockchain to solve the problem of mutual distrust between the transaction parties, uses MongoDB for data retrieval and filtering to solve the problem that it is difficult to query flexibly on (...) the blockchain, uses the file platform to save public resources to solve the problem that it is very expensive or even infeasible to store images or large texts in the blockchain, and ensures remote sensing resources and services related to the effective storage of images and description texts. The Ethernet features are described and analyzed to assess their suitability as a technical basis for the corresponding solutions. To assess the ability of the Ethernet-based system to solve solvable problems, its practical use cases are analyzed. The results of the analysis show that five main issues need to be addressed when applying Ethernet to e-commerce, namely, low scalability, lack of distributed storage, lack of convenience, reliability, and functional integrity. To exploit the idea of using blockchain technology to solve complex problems and to overcome the challenges identified in the existing Ethernet-based implementation solutions, an optimization idea is proposed by creating a multiblockchain architecture based multimodule solution in the Ethernet framework. Finally, the application prospects of the proposed information system are discussed. (shrink)

Uncertainty natural language processing has always been a research focus in the artificial intelligence field. In this paper, we continue to study the linguistic truth-valued concept lattice and apply it to the disease intelligent diagnosis by building an intelligent model to directly handle natural language. The theoretical bases of this model are the classical concept lattice and the lattice implication algebra with natural language. The model includes the case library formed by patients, attributes matching, and the matching (...) degree calculation about the new patient. According to the characteristics of the patients, the disease attributes are firstly divided into intrinsic invariant attributes and extrinsic variable attributes. The calculation algorithm of the linguistic truth-valued formal concepts and the constructing algorithm of the linguistic truth-valued concept lattice based on the extrinsic attributes are proposed. And the disease bases of the different treatments for different patients with the same disease are established. Secondly, the matching algorithms of intrinsic attributes and extrinsic attributes are given, and all the linguistic truth-valued formal concepts that match the new patient’s extrinsic attributes are found. Lastly, by comparing the similarity between the new patients and the matching formal concepts, we calculate the best treatment options to realize the intelligent diagnosis of the disease. (shrink)

In the current social media environment, emotional branding communication has become a common marketing tool for brand owners, and therefore it has become particularly important and urgent to study it. Based on the perspective of brand equity theory, combined with the new characteristics of marketing communication in the social media environment, this paper constructed an emotional branding communication model in the social media environment. The system dynamics method was used to simulate and analyze the new product marketing system (...) to assess whether it could stir the emotional needs of the consumers and resonate within their hearts. This paper discusses the asymmetric communication of different brands regarding the same commodity to determine the impact of this exchange mechanism, that is, only the weak brands in the market initially adopt marketing methods, while the strong brands do not participate in social marketing activities. It was found that the influence of marketing frequency and marketing intensity on symmetric and asymmetric communications was different. In the face of different types of competitors, the marketing strategy of weak brands needs emphasis. Through unit consistency test, structure verification test, effectiveness, and rationality test, it was proven that the emotional branding communication model and new product sales interaction simulation model established in this paper were reasonable and effective. (shrink)

Based on a sample of 54 Islamic indices over the period 2007–2014, we investigate the effect of Shariah board members' educational background on Islamic indices' risk and return characteristics via the screening criteria. Using a capital asset pricing model benchmark analysis, we assess the sensitivity of Islamic indices to their conventional peers in terms of beta and derive a measure of return (Jensen's alpha). First, we observe that the higher the number of members in common among the (...) boards, the higher the risk–return profile of Islamic indices. Second, commonalities among board members lead to standardization of the screening criteria and to similar Islamic indices' performance. Third, we show that different betas across providers depend on the screening criteria, while the economic educational background of board members affects performance in terms of Jensen's alpha. Our study aims at contributing to the governance literature related to board composition and its importance as a possible driver of performance. In addition, given the impressive growth that Islamic finance has experienced during the last decade, this topic is of great interest to the asset management industry. (shrink)