Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the (...) F1-score 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types (...) of credit risk default models in this regard: structural and reduced-form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

In this note, we consider a Merton model for default risk, where the firm’s value is driven by a Brownian motion and a compound Poisson process.

Credit risk, also known as default risk, is the likelihood of a corporation losing money if a business partner defaults. If the liabilities are not met under the terms of the contract, the firm may default, resulting in the loss of the company. There is no clear way to distinguish between organizations that will default and those that will not prior to default. We can only make probabilistic estimations of the risk of default at best. There are two types (...) of credit risk default models in this regard: structural and reduced form models. Structural models are used to calculate the likelihood of a company defaulting based on its assets and liabilities. If the market worth of a company's assets is less than the debt it owes, it will default. Reduced form models often assume an external cause of default, such as a Poisson jump process, which is driven by a stochastic process. They model default as a random event with no regard for the balance sheet of the company. This paper provides a Review of credit risk default models. (shrink)

This paper argues that the reduced-form jump diffusion model may not be appropriate for credit risk modeling. To correctly value hybrid defaultable financial instruments, e.g., convertible bonds, we present a new framework that relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage (...) is mainly due to convertible underpricing. Empirically, however, we do not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

Jim Joyce argues for two amendments to probabilism. The first is the doctrine that credences are rational, or not, in virtue of their accuracy or “closeness to the truth” (1998). The second is a shift from a numerically precise model of belief to an imprecise model represented by a set of probability functions (2010). We argue that both amendments cannot be satisfied simultaneously. To do so, we employ a (slightly-generalized) impossibility theorem of Seidenfeld, Schervish, and Kadane (2012), who (...) show that there is no strictly proper scoring rule for imprecise probabilities. -/- The question then is what should give way. Joyce, who is well aware of this no-go result, thinks that a quantifiability constraint on epistemic accuracy should be relaxed to accommodate imprecision. We argue instead that another Joycean assumption — called strict immodesty— should be rejected, and we prove a representation theorem that characterizes all “mildly” immodest measures of inaccuracy. (shrink)

There is a commonly made distinction between two types of scientists: risk-taking, trailblazing mavericks and detail-oriented followers. A number of recent papers have discussed the question what a desirable mixture of mavericks and followers looks like. Answering this question is most useful if a scientific community can be steered toward such a desirable mixture. One attractive route is through credit incentives: manipulating rewards so that reward-seeking scientists are likely to form the desired mixture of their own accord. Here I (...) argue that this idea is less straightforward than it may seem. Interpreting mavericks as scientists who prioritize rewards over speed and risk, I show in a deliberatively simple model that there is a fixed mixture which is not particularly likely to be desirable and which credit incentives cannot alter. I consider a way around this result, but this has some major drawbacks. I conclude that credit incentives are not as promising a way to create a desirable mixture of mavericks and followers as one might have thought. (shrink)

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their (...) payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. (shrink)

This article presents a comprehensive framework for valuing financial instruments subject to credit risk. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats in financial markets. We analyze how swap rates are affected by bilateral counterparty credit risk, and how CDS spreads depend on the trilateral credit risk of the buyer, seller, and reference entity in a contract. Moreover, we study the effect (...) of collateralization on valuation, since the majority of OTC derivatives are collateralized. The model shows that a fully collateralized swap is risk-free, whereas a fully collateralized CDS is not equivalent to a risk-free one. (shrink)

This article presents a new model for valuing a credit default swap (CDS) contract that is affected by multiple credit risks of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty (...) risk completely in the CDS market. (shrink)

As the ongoing literature on the paradoxes of the Lottery and the Preface reminds us, the nature of the relation between probability and rational acceptability remains far from settled. This article provides a novel perspective on the matter by exploiting a recently noted structural parallel with the problem of judgment aggregation. After offering a number of general desiderata on the relation between finite probability models and sets of accepted sentences in a Boolean sentential language, it is noted that a number (...) of these constraints will be satisfied if and only if acceptable sentences are true under all valuations in a distinguished non-empty set W. Drawing inspiration from distance-based aggregation procedures, various scoring rule based membership conditions for W are discussed and a possible point of contact with ranking theory is considered. The paper closes with various suggestions for further research. (shrink)

Issues of academic authorship pose few problems for philosophers or those in the humanities, yet raise a host of issues for medical researchers, engineers and scientists, where multiple authors is the norm and journal articles sometimes list hundreds of authors. At issue here are abstract questions about desert, as well as practical problems regarding the distribution of goods attached to authorship—tenure, prestige, research grants, etc. This paper defends a version of the author/contributor model, where the specific contributions of authors (...) are described in a footnote, against other models of authorial attribution. Such a model offers the best guarantee that authors will get their due, as well as providing the most reliable protection against misconduct and fraud. The paper also arguesthat it is important for this model to be institutionalized across disciplinary boundaries as the increasingly interdisciplinary nature of research will inevitably bring discipline-specific authorial norms into conflict. (shrink)

Simulation models of the Reiterated Prisoner's Dilemma (in the following: RPD-models) are since 30 years considered as one of the standard tools to study the evolution of cooperation (Rangoni 2013; Hoffmann 2000). A considerable number of such simulation models has been produced by scientists. Unfortunately, though, none of these models has empirically been verified and there exists no example of empirical research where any of the RPD-models has successfully been employed to a particular instance of cooperation. Surprisingly, this has not (...) kept scientists from continuing to produce simulation models in the same tradition and from writing their own history as a history of success. In a recent simulation study -- which does not make use of the RPD but otherwise follows the same pattern of research -- Robert Axelrod's (1984) original role model for this kind of simulation studies is praised as ``an extremely effective means for investigating the evolution of cooperation'' and considered as ``widely credited with invigorating that field'' (Rendell et al. 2010). (shrink)

The study examined constraints to accessing micro credit loan scheme of Bank of Agriculture BOA among farmers in Enugu State, Nigeria Implications for extension service delivery. Purposive and simple random sampling techniques were used in selecting one hundred 100 respondents for the study. Data were collected using structured interview schedule questionnaire and analyzed using frequency, percentage, mean scores and standard deviation. The study revealed that micro credit loan scheme 88.6 were the most patronized among the rural farmers. Others (...) such as ECOWAS, IFAD project and ATHP programme were not patronized at all, probably because the type of projects sponsored by these credit loan scheme are not important to the respondents. The respondents farmers were highly constrained by late release of funds M= 1.27 , grace period too short M= 1. 17 , excessive bureaucracy M= 1. 14 , too short payback period M= 1. 13 , services not regular M= 1. 13 , among others. The Bank of Agriculture staff also noted that they were constrained by late release of approved funds by head quarters of the Bank of Agriculture M= 1.43 , loan diversion M= 1.27 , poor funding of field officers M= 1.20 , lack of awareness by borrowers M= 1.10 and insufficient staff M= 0.93 . The study recommends that adequate awareness campaign on the availability of micro credit loan scheme by Bank of Agriculture should be created in order for the beneficiaries to be knowledgeable about it. It highlights the need for approval of more funds under the scheme and timely release of funds when needed especially during planting season in order to enable the farmers to make judicious use of it for optimum productivity. Mbah Evangeline N | Jiriko, R | Agada, M.O. "Constraints to Accessing Micro-Credit and Loan Scheme of Bank of Agriculture among Farmers in Enugu State, Nigeria: Implications for Extension Service Delivery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017 . (shrink)

The one-side defaultable financial derivatives valuation problems have been studied extensively, but the valuation of bilateral derivatives with asymmetric credit qualities is still lacking convincing mechanism. This paper presents an analytical model for valuing derivatives subject to default by both counterparties. The default-free interest rates are modeled by the Market Models, while the default time is modeled by the reduced-form model as the first jump of a time-inhomogeneous Poisson process. All quantities modeled are market-observable. The closed-form solution (...) gives us a better understanding of the impact of the credit asymmetry on swap value, credit value adjustment, swap rate and swap spread. (shrink)

This article presents a new model for valuing financial contracts subject to credit risk and collateralization. Examples include the valuation of a credit default swap (CDS) contract that is affected by the trilateral credit risk of the buyer, seller and reference entity. We show that default dependency has a significant impact on asset pricing. In fact, correlated default risk is one of the most pervasive threats in financial markets. We also show that a fully collateralized CDS (...) is not equivalent to a risk-free one. In other words, full collateralization cannot eliminate counterparty risk completely in the CDS market. (shrink)

Psychometric g—a statistical factor capturing intercorrelations between scores on different IQ tests—is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. Psychometric g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and it idealizes away from those varieties of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge (...) class='Hi'>model of the relationship between its two targets and, thereby, helps demystify the relationship between folk and scientific psychology. (shrink)

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and (...) ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics. (shrink)

This paper presents a new model for pricing OTC derivatives subject to collateralization. It allows for collateral posting adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral arrangement and credit risk can sufficiently (...) explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk. (shrink)

A method of identifying banks’ business models and studying the features of their risk profile, considering the system of indicators featuring the structure of assets, liabilities, income, expenses, and other qualitative indicators based on monthly statistical reporting. Kohonen's self-organizing maps (SOM) are used to process large data sets, revealing objects’ hidden features by forming homogeneous groups according to similar values of a large system of indicators. The choice of the system of indicators that play the most significant role in describing (...) the business models of modern banks is substantiated. The proposed method makes it possible to group banks with homogeneous characteristics into so-called structural-functional groups and studies the change in the characteristics of groups of banks over time to compare their behavior during periods of active development of the system and during a crisis. That approach is useful for studying the banking system at the macro level, as it provides a quantitative measure of its financial stability. The more banks are in groups with negative values of parameters, increased risks, and unprofitable performance, the worse the general state of the system. The method also allows studying the features of each structural and functional group and the business models’ features at the meso-level. The number and composition of banks inherent in any group change dynamically, which characterizes the features of the relevant business model in a particular period. The averages of each group reflect the objective changes in the banking system structure. In addition, the SOM trajectory can be built for each individual bank determining the development of its strategy, features of a particular business model, and risk profile. At the micro-level, it allows comparing the features of individual banks within the SFGB and models ways to improve efficiency and financial stability by forecast values for SOM. An extensive system of indicators used to form structural and functional groups of banks allows to quickly respond to changes in the banking system, identify areas of increased risk and explore the adequacy and effectiveness of banks’ business models. (shrink)

This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a risk-free value. Credit (...) value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level. (shrink)

The article investigates the main characteristics of the financial stability of Ukrainian banks, their risk profile, structure of assets, liabilities, income, expenses based on the monthly reporting data for 2004-2020 (number of observations – 3 813). The Kohonen self-organizing map (SOM) toolkit is used to form homogeneous groups of banks based on a large number of financial indicators. The selected toolkit provides a convenient visualization of the results. Each bank occupies a specific place on the SOM. The close location of (...) banks on the map indicates that these banks have many common characteristics. This study is called the method of structural and functional groups of banks. The study formed stable groups of banks with the same characteristics of business models and risk profile. The formation of consecutive maps for each reporting period allows to obtain the characteristics of each bank separately and the banking system as a whole. The trajectory of an individual bank’s activity during the observation period characterizes the development of its business strategy. The migration of a large number of banks between groups reflects the objective changes in the conditions of banking. Increasing groups of high-risk banks indicate systemic problems. The results show that after a significant reduction in the banking system of Ukraine in 2014-2016, the business models of banks have changed significantly. The resource base relies on unstable sources of current funds with no set term of use. In the assets of banks, a significant portion is invested in government securities – government bonds. Bank loan portfolios have declined and have a higher proportion of non-performing assets (NPL). Many banks have increased currency risk. Concerns are raised about the state of large state-owned banks that hold a separate high-risk group. Thus, the conducted research allows to distinguish groups of banks with specific characteristics of business models and to observe changes of risk profile inherent to these groups. For each structural-functional group, changes in the values of financial indicators are investigated and migration between groups is studied. The proposed methodology is useful for studying both macro and macro levels. (shrink)

This paper presents a new model for valuing hybrid defaultable financial instruments, such as, convertible bonds. In contrast to previous studies, the model relies on the probability distribution of a default jump rather than the default jump itself, as the default jump is usually inaccessible. As such, the model can back out the market prices of convertible bonds. A prevailing belief in the market is that convertible arbitrage is mainly due to convertible underpricing. Empirically, however, we do (...) not find evidence supporting the underpricing hypothesis. Instead, we find that convertibles have relatively large positive gammas. As a typical convertible arbitrage strategy employs delta-neutral hedging, a large positive gamma can make the portfolio highly profitable, especially for a large movement in the underlying stock price. (shrink)

In this article we raise a problem, and we offer two practical contributions to its solution. The problem is that academic communities interested in digital publishing do not have adequate tools to help them in choosing a publishing model that suits their needs. We believe that excessive focus on Open Access (OA) has obscured some important issues; moreover exclusive emphasis on increasing openness has contributed to an agenda and to policies that show clear practical shortcomings. We believe that academic (...) communities have different needs and priorities; therefore there cannot be a ranking of publishing models that fits all and is based on only one criterion or value. We thus believe that two things are needed. First, communities need help in working out what they want from their digital publications. Their needs and desiderata should be made explicit and their relative importance estimated. This exercise leads to the formulation and ordering of their objectives. Second, available publishing models should be assessed on the basis of these objectives, so as to choose one that satisfies them well. Accordingly we have developed a framework that assists communities in going through these two steps. The framework can be used informally, as a guide to the collection and systematic organization of the information needed to make an informed choice of publishing model. In order to do so it maps the values that should be weighed and the technical features that embed them. Building on our framework, we also offer a method to produce ordinal and cardinal scores of publishing models. When these techniques are applied the framework becomes a formal decision–making tool. Finally, the framework stresses that, while the OA movement tackles important issues in digital publishing, it cannot incorporate the whole range of values and interests that are at the core of academic publishing. Therefore the framework suggests a broader agenda that is relevant in making better policy decisions around academic publishing and OA. (shrink)

Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks (...) are inadequate for AI-intensive companies. To fully account for contemporary technology, the following categories of evaluation will be developed and featured as vital investing criteria: autonomy, dignity, privacy, performance. With these priorities established, the larger goal is a model for humanitarian investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors. (shrink)

Department of Mathematics education curriculum implementation Based KKNI who have not provided the container development of character education for students. This can be seen from the learning process in college that still relies on aspects of increased knowledge. Achievement of learning on aspects of attitudes / values ​​still are administrative without being pushed on the feasibility of value investment education daily life. Applied learning models are still oriented to conventional learning eg discussions, lectures, discussion and assignment. It is necessary to (...) present innovations in the learning model for students. Model-oriented learning in the context of nationalism (educational values ​​Ki Hadjar Dewantara). so that by applying the learning model of value investment Ki Hajar Dewantara education can increase educational purposes at universities. The method used in this study is quasi-experimental design with a Two-Group Test post-only design. The population in this study that students of Department of Mathematics Education. The sample research nonprobability sampling using purposive sampling technique. Experiments in which the class is used as the Class A total of 39 students and the class is a class B control as many as 33 students. Results of research dianataranya that the student response to the application of learning models planting educational values ​​Ki Hadjar Dewantara is quite good. Students learning attitude was quite good with a big achievement average score of 64.41. Effect of application of learning models planting educational values ​​Ki Hadjar Dewantara attitude towards learning in mathematics education majors and significant effect on average there are differences in learning outcomes experimental class is higher than population control class learning outcomes. It states that the application of learning models planting educational values ​​Ki Hadjar Dewantara effectively implemented in the Department of Mathematics Education. (shrink)

In this paper, I argue that one of the arguments usually put forward in defence of universism is in tension with current set theoretic practice. According to universism, there is only one set theoretic universe, V, and when applying the method of forcing we are not producing new universes, but only simulating them inside V. Since the usual interpretation of set generic forcing is used to produce a “simulation” of an extension of V from a countable set inside V itself, (...) the above argument is credited to be a strong defence of universism. However, I claim, such an argument does not take into account current mathematical practice. Indeed, it is possible to find theorems that are available to the multiversists but that the advocate of universism cannot prove. For example, it is possible to prove results on infinite games in non-well-founded set-theories plus the axiom of determinacy (such as ZF + AFA + PD) that are not available in ZFC + PD. These results, I contend, are philosophically problematic on a strict universist approach to forcing. I suggest that the best way to avoid the difficulty is to adopt a pluralist conception of set theory and embrace a set theoretic multiverse. Consequently, the current practice of set generic forcing better supports a multiverse conception of set theory. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate (...) model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

This study aims to (1) describe the application of the roundtable cooperative model on narrative text teaching materials, (2) describe students’ responses to the application of the roundtable cooperative model on narrative text teaching materials, (3) describe the increase in students’ knowledge of narrative text teaching materials. The type of research used was classroom action research. Data collection techniques were observation, interviews, questionnaires, tests, and documentation. The data in this study were in the form of application, response, and (...) increase in students’ knowledge of implementing the roundtable cooperative model on narrative text teaching materials. Data were analyzed using interactive techniques, including data reduction, presentation, and concluding. The data validation technique used data and method triangulation. The study results show that (1) applying the roundtable cooperative model to narrative text teaching materials can improve students’ understanding and writing skills. These improvements include structure, storyline or sequence of events, writing development, and completeness of information according to the theme; (2) students’ responses to the application of the roundtable cooperative model show that 90.3% agree to use the cooperative model in narrative text teaching materials and 9.7% do not agree; (3) there is an increase in student learning outcomes. Before applying the roundtable cooperative model, the students get an average score of 56, and after applying it, they get an average score of 69. The results show that the roundtable cooperative model can be used in the learning process by using media that supports the roundtable cooperative model and teaching materials. (shrink)

Current approaches used in educational research and practice to evaluate the quality of written arguments often rely on structural analysis. In such assessments, credit is awarded for the presence of structural elements of an argument, such as claims, evidence, and rebuttals. In this article, we discuss limitations of such approaches, including the absence of criteria for evaluating the quality of the argument elements. We then present an alternative framework, based on the Rational Force Model (RFM), which originated from (...) the work of a Nordic philosopher Næss. Using an example of an argumentative essay, we demonstrate the potential of the RFM to improve argument analysis by focusing on the acceptability and relevance of argument elements, two criteria widely considered to be fundamental markers of argument strength. We outline possibilities and challenges with using the RFM in educational contexts and conclude by proposing directions for future research. (shrink)

Item analysis is a general term that refers to the specific methods used in education to evaluate test items, typically for the purpose of test construction and revision. Construction and analysis of science examinations in high school curricula needs the integration of integrated process and skill type which help students develop their rational and logical thinking. This exploratory study aimed to investigate the comparative results of item analyses using Rasch Model Analysis and SPSS. It examined the reliability, mean difficulty, (...) mean ability, discrimination index, mean trend scores, infit and misfit, and item-person and fit maps using the results of the Basic & Integrated Science Process and Skill Test (BISPST) administered to secondary science high school teachers in the Division of Nueva Vizcaya. The Rasch Model Analysis and SPSS revealed the same unsatisfactory value of internal consistency or the reliability coefficient Cronbach’s alpha (0.66 and 0.67), mean score (19.52) and standard deviation (4.53) of the BISPST. The mean item difficulty was set at 0 by definition (SD = 2.02) and the mean examinee ability was 1.56 (SD = 0.98), which means that the examinees were able to correctly answer 61% of items on average. 33 items with difficulty lower than the least able examinee. The mean ability using Rasch Model Analysis per item showed that high performers in the test got the correct answers compared to the low performing group. This is the same and consistent with the results of the mean trend scores of the upper, middle and lower groups as generated by the SPSS. Choices in the BISPST were found to be effective distracters. Although most items needs revision as revealed by the values generated by both softwares, no items were found to be misfit in the distribution. (shrink)

The objective of this study is to Analyze the Financial Position and Performance of the Axis and Kotak Mahindra Bank in India based on their financial characteristics. We have chosen the CAMEL model and t-test which measures the performance of bank from each of the important parameter like capital adequacy, asset quality, management efficiency, earning quality, liquidity and Sensitivity. The present study is conducted analyze the consistency of the profitability of the Axis and Kotak Mahindra bank’s. It is analyses (...) that the ratio of credit deposit is maximum of Kotak Mahindra Bank Ltd and it shows efficient management of the bank. The ratio of earning per share is maximum for Axis Bank Ltd i.e. 50.28 and the ratio of return on Assets is minimum for Axis Bank Ltd. The CAMELS’ analysis and t-test concludes that there is no significance difference between the Axis and Kotak Mahindra bank’s financial performance but the Kotak Mahindra bank performance is slightly less compared with Axis Bank. (shrink)

Imam Hatip schools (religious vocational schools) in Turkey have been taught teaching Arabic for many years. However, the objectives of learning Arabic have not yet been realized. The Education Council of the Ministry of Education prepares educational plans and programs for Arabic lessons in order to increase the quality of Arabic language teaching, the first of these programs was in 1973. This research is a field study carried out in 2016 on how to implement the educational programs prepared in 2011 (...) for Arabic lessons in Imam Hatip schools, and the problems facing the teacher in practical reality. The scope of the research is limited to Tekirdağ Anadolu Religious High School for boys, Süleymanpaşa Anadolu Religious High School for girls, Süleymanpaşa Religious Secondary school, and the teachers working in these schools. The research was based on the views of those educators as to how the educational programmes for Arabic lessons and the problems they faced during their exercise were implemented. The research was formulated through interviews with teachers. The results of the research according to the data received from the teachers were processed in the statistical program SPSS and then converted to frequency values and percentage values. These values were presented in tabular form. As a result of the findings, it was seen that most of the teachers did not read the Arabic lesson teaching program they were practicing, and they were unaware of the program’s vision, approach and goals; and they did not understand the program’s approach, vision, and objectives. Moreover, it was understood that the minds of the teachers were not clear about whether the Arabic language was a foreign language lesson or a religious vocational lesson, that the teaching practices and behaviors of teachers are incompatible with the educational program and they also had one view that the goals of the Arab lesson were largely unfulfilled. After the main results have been revealed and discussed, finally, following the disputed results, some suggestions were made to increase the quality of teaching Arabic such as making Arabic an effective, active and influential element in the lives of students and teachers through various awards and scholarships and the examination of Arabic as a foreign language in the university entrance examinations as an element of motivation and quality. In addition, in order to improve the quality of the Arabic teacher, we have made the Arabic exams as periodic, an increase in salaries according to the teachers’ receivables score, the continuation of in-service trainings, etc. (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)

This book is an edited collection of original research papers on the digital revolution of the public and governance. It covers cyber governance in Finland, and the securitization of cyber security in Finland. It investigates the cases of Brexit, the 2016 US presidential election of Donald Trump, the 2017 presidential election of Volodymyr Zelensky, and Brexit. It examines the environmental concerns of climate change and greenwashing, and the impact of digital communication giving rise to the #MeToo and Incel movements. It (...) considers how digitilization can serve to emancipate women through ride-sharing, and how it leads to the question of robot rights. It considers fake news and algorithmic governance with respect to case studies of the Chinese social credit system, the US FICO credit score, along with Facebook, Twitter, Cambridge Analytica and the European effort to regulate and protect data usage. (shrink)

The objective of this paper is to examine the role of the banking system in the growth of the Vietnamese economy in the process of the transition that started in the early 1990s. An ARDL approach-based multivariate regression technique is applied to shed light on the impact on the growth of banking development, which is measured by broad money and bank credit. The empirical findings confirm a positive long-term effect of banking development on growth, reflecting the important role of (...) the banking system in a typical bank-based financial system in mobilizing and supplying capital to the economy, thus contributing to growth throughout the process of economic transition. The empirical findings also indicate a nonlinear effect and a diminishing marginal effect of banking development in the sub-period 2007–2020. The thresholds for the two measures of banking development are estimated to be around 107% and 101% of the GDP, respectively. This finding suggests that bank credit expansion needs to be closely controlled to be adaptive to the capital-absorptive capacity of the economy. To a certain extent, this finding is also an indicator of the ongoing extensive growth model adopted in Vietnam, which relies heavily on the quantity of invested capital. (shrink)

Fan culture has grown immensely over the past few years. People are constantly looking up to celebrities and personalities as role models for their fashion, identity, and success. During the stage of adolescence, it is normal for teenagers to admire well- known people and form fan attachments as part of their identity formation. However, this admiration of a specific media figure can be associated with one's personality, cognitive processes, and psychological well-being. Thus, the current study aims to investigate the correlation (...) between celebrity admiration and the self-esteem of 238 Filipino male teenagers. The research instruments, namely Celebrity Attitude Scale (CAS) and Rosenberg Self-Esteem Scale (RSES) were both utilized through google forms. Results have shown high scores on celebrity admiration and the normal range of self-esteem of the participants. Using Pearson’s r correlation coefficient, it was found that celebrity admiration and self-esteem of Filipino male teenagers have a significant relationship that exists in a negative direction. (shrink)

The aim of this paper is to present a revision of international versions of the Utrecht Work Engagement Scale and to describe the psychometric properties of a Portuguese version of the UWES-9 developed simultaneously for Brazil and Portugal, the validity evidence related with the internal structure, namely, Dimensionality, measurement invariance between Brazil and Portugal, and Reliability of the scores. This is the first UWES version developed simultaneously for both countries, and it is an important instrument for understanding employees' work engagement (...) in the organizations, allowing human resources departments to better use workforces, especially when they are migrants. A total of 524 Brazilian workers and 522 Portuguese workers participated in the study. Confirmatory Factor Analysis, group comparisons, and Reliability estimates were used. The use of workers who were primarily professionals or administrative support, according to ISCO-08, reinforced the need to collect data on other professional occupations. Confirmatory factor analysis showed acceptable fit for the UWES-9 original three-factor solution, and a second-order factor structure has been proposed that presented an acceptable fit. Full-scale invariance was obtained between the Portuguese and Brazilian samples, both for the original three-factor first-order and second-order models. Data revealed that Portuguese and Brazilian workers didn't show statistically significant differences in the work engagement dimensions. This version allows for direct comparisons of means and, consequently, for performance of comparative and cross-cultural studies between these two countries. (shrink)

Plants from a handful of species provide the primary source of food for all people, yet this source is vulnerable to multiple stressors, such as disease, drought, and nutrient deficiency. With rapid population growth and climate uncertainty, the need to produce crops that can tolerate or resist plant stressors is more crucial than ever. Traditional plant breeding methods may not be sufficient to overcome this challenge, and methods such as highOthroughput sequencing and automated scoring of phenotypes can provide significant (...) new insights. Ontologies are essential tools for accessing and analysing the large quantities of data that come with these newer methods. As part of a larger project to develop ontologies that describe plant phenotypes and stresses, we are developing a plant disease extension of the Infectious Disease Ontology (IDOPlant). The IDOPlant is envisioned as a reference ontology designed to cover any plant infectious disease. In addition to novel terms for infectious diseases, IDOPlant includes terms imported from other ontologies that describe plants, pathogens, and vectors, the geographic location and ecology of diseases and hosts, and molecular functions and interactions of hosts and pathogens. To encompass this range of data, we are suggesting inOhouse ontology development complemented with reuse of terms from orthogonal ontologies developed as part of the Open Biomedical Ontologies (OBO) Foundry. The study of plant diseases provides an example of how an ontological framework can be used to model complex biological phenomena such as plant disease, and how plant infectious diseases differ from, and are similar to, infectious diseases in other organism. (shrink)

This paper presents an impact evaluation of the policies for teacher education programs within the framework of the National Development Plan 2014-2018. The programs were compelled to obtain high-quality accreditation, increase the credits in school practices, and the English level, among other requirements. Relying on multivariable regression, it shows that, for the year 2021, there is no association between high-quality accreditation in most modules of the Saber Pro tests, or it is negative in the global score and quantitative reasoning. In (...) a second step, using Differences–in–Differences, it evidences how the changes made by the programs had positive effects on the performance of students compared to other professional programs. The results show significant improvement in most modules of the Saber Pro test, except for the English module. The effects were homogeneous by characteristics of the students and institutions, but heterogeneous by area of knowledge, indicating that the effect is mainly due to the curricular changes of the programs. (shrink)

I explore and develop the idea that (NA) knowledge is non-accidentally true belief. The applicable notion of non-accidentality differs from that of ‘epistemic luck’ discussed by Pritchard. Safety theories may be seen as a refinement of, or substitute for, NA but they are subject to a fundamental difficulty. At the same time, NA needs to be adjusted in order to cope with two counterexamples. The Light Switch Case turns on the ‘directionof-fit’ between a belief and the facts, while the Meson (...) Case concerns knowledge of nomological necessities. A proposed revision to NA is: (ENA) S knows that P when S’s belief that P is non-accidentally true because (i) it is based on good evidence, and (ii) in and of themselves, beliefs based on good evidence tend to be true. ‘knowledge-as-credit’ accounts have been offered as an alternative way of sharpening NA. I argue that such accounts face serious objections that don’t apply to ENA. Finally, I take up Lackey’s worry that the credit-based approach mishandles knowledge via testimony. ENA faces no difficulty on that score, which counts in its favor. (shrink)

This paper attempts to assess the economic significance and implications of collateralization in different financial markets, which is essentially a matter of theoretical justification and empirical verification. We present a comprehensive theoretical framework that allows for collateralization adhering to bankruptcy laws. As such, the model can back out differences in asset prices due to collateralized counterparty risk. This framework is very useful for pricing outstanding defaultable financial contracts. By using a unique data set, we are able to achieve a (...) clean decomposition of prices into their credit risk factors. We find empirical evidence that counterparty risk is not overly important in credit-related spreads. Only the joint effects of collateralization and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of financial contracts. We also analyze the difference between cleared and OTC markets. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

In this paper a Knowledge-Based System (KBS) for determining the appropriate students major according to his/her preferences for sophomore student enrolled in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested. A set of predefined criterions that is taken into consideration before a sophomore student can select a major is outlined. Such criterion as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during (...) the student freshman year, number of credits passed, student cumulative grade point average of freshman year, among others, were then used as input data to KBS. KBS was designed and developed using Simpler Level Five (SL5) Object expert system language. KBS was tested on three generation of sophomore students from the Faculty of Engineering and Information Technology of the Al-Azhar University, Gaza. The results of the evaluation show that the KBS is able to correctly determine the appropriate students major without errors. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and (...) validated, achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset (...) size, and class imbalance, and presents strategies employed to address these issues. Additionally, the paper emphasizes the importance of alternative evaluation metrics, including precision, recall, and F1-score, to assess the model's performance comprehensively. Detailed information regarding the model architecture, data preprocessing, and hyperparameter tuning is provided to enhance the reproducibility of the research. The study also discusses ethical considerations and the implications of false positives and false negatives in smoke detection applications. While achieving 100% accuracy is a notable accomplishment, this paper calls for cautious interpretation and further validation in real-world settings. It concludes by highlighting areas for future research and improvement in smoke detection technology, emphasizing the need for practical deployment and the reduction of false alarms. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training (...) and cross-validating the proposed model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing (...) attacks are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)