Ethical banking, microfinance institutions or certain credit cooperatives, among others, grant socially responsible loans. This paper presents a credit score system for them. The model evaluates social and financial aspects of the borrower. The financial aspects are evaluated under the conventional banking framework, by analysing accounting statements and financial projections. The social aspects try to quantify the loan impact on the achievement of Millennium Development Goals such as employment, education, environment, health or community impact. The social credit score model should (...) incorporate the lender’s know-how and should also be coherent with its mission. This is done using Multi-Criteria Decision Making. The paper illustrates a real case: a loan application by a social entrepreneur presented to a socially responsible lender. The decision support system not only produces a score, but also reveals strengths and weaknesses of the application. (shrink)

In the United States, at least 6% of all hospital beds are in the intensive care unit or coronary care unit. The cost of treating a patient in an intensive care unit averages from $2,000 to $3,500 per day. At least 10–40% of intensive care patients will not survive to hospital discharge. Today, every major category of disease may be found in the modern ICU; common diagnoses are septicemia, postsurgical complications, cerebrovascular accidents, gastrointestinal bleeding, neoplasia, and respiratory failure. ICUs employ (...) some of the most sophisticated medical technology, routinely monitoring the cardiopulmonary performance of patients and often providing assisted ventilation. ICUs are high intensity in terms of their staffing, involving 24-hour physician supervision and nurse:patient ratios from 1:3 to 1:1. (shrink)

To reduce the workload of paper evaluation and improve the fairness and accuracy of the evaluation process, a writing assistant scoring system for English as a Foreign Language (EFL) learners is designed based on the principle of machine learning. According to the characteristics of the data processing process and the advantages and disadvantages of the Browser/server (B/s) structure, the equipment structure design of the project online evaluation teaching auxiliary system is further optimized. The panda method is used to read (...) the data, the clean method is used to realize the data preprocessing, the model test is carried out, the cross validation method is selected, the data is divided in advance, and the process of programming the problem scoring system is further optimized, the automatic scoring technology is constructed by English teaching recognition module, feature extraction module and scoring module, the table structure of programming problems is designed, the auxiliary evaluation program of English writing is designed, and the design of writing auxiliary scoring system is completed. The analysis of the experimental results shows that the accuracy of the system is close to 90%, and the total average difference is 0.56. The system can normally take out a variety of test papers. Considering the subjectivity of manual scoring and the impact of key code setting on scoring, the carefully set key code can effectively improve the scoring accuracy of the system. The scoring strategy of the automatic scoring system is effective and the scoring effect is good, and it can be used in practical application. (shrink)

Within the banking community, the argument about sustainability and profitability tends to be inversely related. Our research suggests this does not need to be strictly the case. We present a credit score system based on sustainability issues, which is used as criteria to improve financial institutions’ lending policies. The Sustainability Credit Score System is based on the analytic hierarchy process methodology. Its first implementation is on the agricultural industry in Brazil. Three different firm development paths are identified: business as usual, (...) sustainable business, and future sustainable business. The following six dimensions are present in the SCSS: economic growth, environmental protection, social progress, socio-economic development, eco-efficiency, and socio-environmental development. The results suggest that sustainability is not inversely related to profit either from a short- or long-term perspective. The SCSS is related to the Equator Principles, but its application is not driven to project financing. It also deals with short- and long-term risks and opportunities, instead of short-term sustainability impacts. (shrink)

On the Lockean thesis one ought to believe a proposition if and only if one assigns it a credence at or above a threshold (Foley in Am Philos Q 29(2):111–124, 1992). The Lockean thesis, thus, provides a way of characterizing sets of all-or-nothing beliefs. Here we give two independent characterizations of the sets of beliefs satisfying the Lockean thesis. One is in terms of betting dispositions associated with full beliefs and one is in terms of an accuracy scoring system (...) for full beliefs. These characterizations are parallel to, but not merely derivative from, the more familiar Dutch Book (de Finetti in Theory of probability, vol 1, Wiley, London, 1974) and accuracy (Joyce in Philos Sci 65(4):575–603, 1998) arguments for probabilism. (shrink)

The quality of early childhood education and care (ECEC) is important for children’s development. One instrument that was developed to assess an aspect of ECEC quality is the Classroom Assessment Scoring System for pre-kindergarten children (CLASS Pre-K). We examined the factorial validity of the instrument using data from 177 German preschool classrooms. The three-factor teaching through interaction model (Hamre et al., 2013) was contrasted to a one-factor, a two-factor, and a bifactor model as proposed by Hamre et al. (2014). (...) Our results indicated that the three-factor structure with the domains of emotional support, classroom organization, and instructional support fit the data best. The fit of the teaching through interaction model was satisfying after adding a cross-loading of the dimension language modeling on emotional support, and two correlated residuals. Evidence of convergent and discriminant validity are provided. In terms of factor structure and pattern score comparisons, the results were similar to previous U.S. and German studies. The discussion concerns the justifiability of the factor model revisions and draws directions for further research. We concluded that our study offers further evidence of the applicability of the CLASS Pre-K for the assessment of teacher-child interaction quality in the German context. (shrink)

There are few examples in the literature of objective measures for the assessment of donor willingness. The author describes the scoring system in use at his own renal transplant unit which has brought objectivity to the process of determining the willingness of living related donors. In this system, a total score to determine the degree of willingness or unwillingness is calculated based on responses to a series of questions. The author believes that with minor modifications this system could be (...) implemented by transplant units in different countries and cultures to screen out donors who are acting under duress. (shrink)

Consider an election in which each of the n voters casts a vote consisting of a strict preference ranking of the three candidates A, B, and C. In the limit as n→∞, which scoring rule maximizes, under the assumption of Impartial Anonymous Culture (uniform probability distribution over profiles), the probability that the Condorcet candidate wins the election, given that a Condorcet candidate exists? We produce an analytic solution, which is not the Borda Count. Our result agrees with recent numerical (...) results from two independent studies, and contradicts a published result of Van Newenhizen (Economic Theory 2, 69–83. (1992)). (shrink)

In many voting systems, voters’ preferences on a set of candidates are represented by linear orderings. In this context, scoring rules are well-known procedures to aggregate the preferences of the voters. Under these rules, each candidate obtains a fixed number of points, sk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s_k$$\end{document}, each time he/she is ranked k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}th by one voter and the candidates are ordered according to the total (...) number of points they receive. In order to identify the best scoring rule to use in each situation, we need to know which properties are met by each of these procedures. Although some properties have been analyzed extensively, there are other properties that have not been studied for all scoring rules. In this paper, we consider two desirable social choice properties, the Pareto-optimality and the immunity to the absolute loser paradox, and establish characterizations of the scoring rules that satisfy each of these specific axioms. Moreover, we also provide a proof of a result given by Saari and Barney, where the scoring rules meeting reversal symmetry are characterized. From the results of characterization, we establish some relationships among these properties. Finally, we give a characterization of the scoring rules satisfying the three properties. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ (...) likelihood of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

This essay examines the scratch as it relates to garbage, scores, and the event. Garbage is that which is cast aside as social systems form themselves, and, as such, is always destined to return. Scores are both methodological maps and experimental artistic methods. And the event, in this context, is the opening that enables both the determination of form and the emergence of the unexpected.

Part-of-speech tagging for English composition is the basis for automatic correction of English composition. The performance of the part-of-speech tagging system directly affects the performance of the marking and analysis of the correction system. Therefore, this paper proposes an automatic scoring model for English composition based on article part-of-speech tagging. First, use the convolutional neural network to extract the word information from the character level and use this part of the information in the coarse-grained learning layer. Secondly, the word-level (...) vector is introduced, and the residual network is used to establish an information path to integrate the coarse-grained annotation and word vector information. Then, the model relies on the recurrent neural network to extract the overall information of the sequence data to obtain accurate annotation results. Then, the features of the text content are extracted, and the automatic scoring model of English composition is constructed by means of model fusion. Finally, this paper uses the English composition scoring competition data set on the international data mining competition platform Kaggle to verify the effect of the model. (shrink)

It is difficult to give a precise definition of hypertext since, in addition to its use as a technical tool, there is the conceptual dimension of a space for organizing memory and mapping connections. People often confuse the hypertext system, which makes it possible, through the digital medium, to link objects of different types, with the products (compositions?) created by means of this technique. Hypertext cannot be limited to either of these aspects. Like ink and paper, it is a medium (...) for composition and expression and should also be seen as such. Whatever its purpose, and even if it involves collecting together pages on the Web in an ad hoc manner, hypertext delineates areas of knowledge and thus helps to produce a dual temporality, one that belongs to documents, the other characterized by instant actualization (which coincides with the eradication of any distance of space or time between the items of information). (shrink)

Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute (...) EEG epochs (8–16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81–100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies. (shrink)

The successful early acquisition of reading literacy represents a crucial learning process determining the further course of academic development (Stanovich, 2009). During this process, interactions between children and their proximal social environment are of utmost importance. Therefore, we introduce a systemic framework for the development of learning potential (e.g., Mudrak et al., 2015, 2019, 2019b; Ziegler & Stoeger, 2017) and explore the interactions between the social and motivational processes associated with reading literacy development in school-age children. We base our analysis (...) on a representative Czech sample of fourth-grade pupils involved in the Progress in International Reading Literacy study (PIRLS, Martin et al., 2016). On the basis of the systemic framework, we hypothesized hierarchical relationships among family socioeconomic status, related developmental resources (including parental support, expectations, and reading resources), children’s reading motivation (including reading engagement and reading confidence), and manifested learning outcomes (including school grades and reading competence). We implemented three structural equation models to test the hypothesized relationships. The first model tested the direct effect of developmental resources on reading competence. The second model included the motivational variables as mediators between resources and competence. The third model included school grades as mediators between resources and motivational variables. Our analyses indicated the good fit of the proposed models. The final model explained 37.8 % of the variance in children’s school grades and 46.5 % of the variance in reading literacy test scores (compared to 34.8 % in the first model). Moreover, parental socioeconomic status was strongly associated with parental expectations, which were associated with reading confidence, partially through the effect of parental expectations on children’s school grades. Reading confidence was the main predictor of reading literacy within the model, followed by the direct effects of parental resources. The results illustrate complex processes through which the family environment affects the development of learning competencies such as reading literacy by providing children with the relevant social and material resources associated with their motivation and school outcomes. We discuss some of the reasons that these relationships may take place and consider their implications for educational practice. (shrink)

Definition of the problem: Even after the new German legislation about organ donors and transplantation (“information solution”), the question of criteria for distributing the organs is still not solved. The various alternatives to solve this problem face different social acceptance and economic efficiency.Arguments: Medical criteria (e.g. HLA compatibility) and non-medical criteria (e.g. willingness to pay of the patients) are valued on the basis of generally accepted objectives (e.g. equal access to health services or low costs). As an innovative form of (...) organ distribution systems the club solution is introduced. It allows the donor to realise preferences regarding use of the organs.Conclusion: Scoring systems may help to increase the transparency of organ allocation. From an economic point of view medical criteria have to be weighted more than non-medical criteria. (shrink)

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make (...) full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given. (shrink)

This article is devoted to examining the ontological foundations of computer-generated music scores. Specifically, we focus on the categorial question, i.e., the inquiry that aims to determine the kind of ontological category that musical works belong to. This task involves considerations concerning the existence and persistence conditions for musical works, and it has consequences for the determination of what it is to compose a musical work. Our contention is that not all the possible answers to the categorial question in the (...) ontology of music are equally compatible with the hypothesis that creative music systems compose musical works. The thesis defended here is that musical Platonism is the proposal that best accommodates this hypothesis. We claim that musical Platonism is the answer to the categorial question that offers the most straightforward explanation for the possibility of considering creative music systems as genuinely composing musical works. Moreover, we uphold that the notion of creative-evaluative discovery as the characterization of what it is to compose a musical work entailed by Platonism is the simplest explanation of the process developed by a computer in producing musical works. For this purpose, we will take as empirical data the features of the Iamus computer, a system that produces musical works autonomously using evolutionary algorithms and following an evo-devo strategy. The works generated by this computer have been recorded by the London Symphony Orchestra and other renowned international soloists, and its impact has been notable in the literature (Ball, 2012; Coghlan, 2012; Berger, 2013). (shrink)

We present a maximum-entropy based system for identifying Named Entities (NEs) in biomedical abstracts and present its performance in the only two biomedical Named Entity Recognition (NER) comparative evaluations that have been held to date, namely BioCreative and Coling BioNLP. Our system obtained an exact match f-score of 83.2% in the BioCreative evaluation and 70.1% in the BioNLP evaluation. We discuss our system in detail including its rich use of local features, attention to correct boundary identification, innovative use of external (...) knowledge resources including parsing and web searches, and rapid adaptation to new NE sets. We also discuss in depth problems with data annotation in the evaluations which caused the final performance to be lower than the optimal. (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)

Rheumatoid arthritis is a progressive systemic autoimmune disease characterized by inflammation of the joints and surrounding tissues, which seriously affects the life of patients. The Sharp/van der Heijde method has been widely used in clinical evaluation for the RA disease. However, this manual method is time-consuming and laborious. Even if two radiologists evaluate a specific location, their subjective evaluation may lead to low inter-rater reliability. Here, we developed an efficient model powered by deep convolutional neural networks to solve these problems (...) and automated the overall scoring on hand X-rays. The depthwise separable convolution technique is used based on ResNet-50 due to the high resolution of hand X-rays. An inverted residual block is introduced to devise a ResNet-Dwise50 model to enhance the efficiency of the model. The model was trained and tested using bilateral posteroanterior images of 3818 patients. The experiment results show the ResNet-Dwise50 model achieved an MAE of 14.90 and RMSE of 22.01 while ensuring high efficiency. There was no statistical difference between the average scores given by two experienced radiologists and predicted scores from our model. (shrink)

Behavioral immune system describes psychological mechanisms that detect cues to infectious pathogens in the immediate environment, trigger disease-relevant responses and facilitate behavioral avoidance/escape. BIS activation elicits a perceived vulnerability to disease which can result in conformity with social norms. However, a response to superficial cues can result in aversive responses to people that pose no actual threat, leading to an aversion to unfamiliar others, and likelihood of prejudice. Pathogen-neutralizing behaviors, therefore, have implications for social interaction as well as illness behaviors (...) and responses to health communications. In this study, we investigate how PVD influences conformity, attitudes to other people and to lockdown regulations through the lens of the Reinforcement Sensitivity Theory. RST describes personality in terms of biologically-driven approach and avoidance motivations which support personal goals. Participants from the United Kingdom public completed an RST personality questionnaire and then read either coronavirus morbidity-mortality statistics and current United Kingdom government lifestyle regulations, just the regulations, or no information at all. They all completed the Perceived Vulnerability to Disease scale to assess BIS-relevant Germ Aversion and Perceived Infectability, followed by questions measuring social conformity, warmth toward others and attitudes toward lockdown measures. Significantly lower PVD scores were observed in the no-information condition, with the other conditions showing no difference. In terms of RST, approach behaviors related to goal-drive persistence work alongside fear in explaining conformity to social norms. Reward related approach behaviors partially explained warmth toward others, indicating that social rewards gained through interaction continue to be strong drivers of behavior. We found no role for RST traits in attitudes toward lockdown. Overall, coronavirus-related behavior is not driven purely by fear, but also by social and/or protection goals regulated by approach motivation. This study presents new insights into public perceptions of coronavirus and government regulated lifestyle restrictions, helping to explain social behaviors in terms of biologically driven mechanisms. Such understanding is vital if we are to successfully motivate public behavior to constrain spread of the virus. (shrink)

AimTo investigate the correlation between dietary score and depression in patients with cancer.MethodsIn this cross-sectional study, data were collected from the National Health and Nutrition Examination Survey 2005–2018, a cross-sectional and nationally representative database, to compare 322 patients with depression to 2,868 with no depression. Mediterranean diet, Dietary Approaches to Stop Hypertension diet, and the Healthy Eating Index 2015 score were calculated. Depression was assessed using the Patient Health Questionnaire-9. Weighted logistic regression models were used to explore the relationship between (...) dietary scores and depression in patients with cancer. Subgroup analysis was performed by sleep disorders, sex, cancer type, number of tumors, and pain relief prescription treatment.ResultsThe final study sample included 3,190 adults, with 56.94% of them being women, representing 2,177 non-Hispanic white adults. After multivariable adjustment, the MEDS score was correlated with a reduced risk of depression in patients with cancer [odds ratio : 0.90, 95% confidence interval : 0.82–0.97, p = 0.010]. Moreover, the MEDS score was correlated with depression in cancer patients with sleep disorders, in female patients with cancer, particularly in female cancer reproductive system patients. MEDS score also showed a decreased risk of depression in patients with 1 cancer. MEDS score and DASH score were related to a decreased risk of depression in patients with cancer using pain relief prescription.ConclusionGood diet quality is significantly correlated with decreased risk of depression in patients with cancer. Aligning with the Dietary Guidelines, such as the MEDS diet, may be beneficial to the reduced risk of depression in patients with cancer. (shrink)

BackgroundSystems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology ; “big data” statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to (...) generate systems medicine knowledge and clinicians working to apply it.DiscussionThis article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through “big data” approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the “death computer,” to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their “quantified self.”SummaryThis paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care. (shrink)

Machine learning platforms have emerged as a new promissory technology that some argue will revolutionize work practices across a broad range of professions, including medical care. During the past few years, IBM has been testing its Watson for Oncology platform at several oncology departments around the world. Published reports, news stories, as well as our own empirical research show that in some cases, the levels of concordance over recommended treatment protocols between the platform and human oncologists have been quite low. (...) Other studies supported by IBM claim concordance rates as high as 96%. We use the Watson for Oncology case to examine the practice of using concordance levels between tumor boards and a machine learning decision-support system as a form of evidence. We address a challenge related to the epistemic authority between oncologists on tumor boards and the Watson Oncology platform by arguing that the use of concordance levels as a form of evidence of quality or trustworthiness is problematic. Although the platform provides links to the literature from which it draws its conclusion, it obfuscates the scoring criteria that it uses to value some studies over others. In other words, the platform “black boxes” the values that are coded into its scoring system. (shrink)

Data on farming systems in Petén, Guatemala, were used to develop an agricultural intensity index. The index can be used to assign an intensity “score” to a given farming system based on the array of practices used by the farmer, each practice’s contribution to production intensity, and the scale at which these practices are used. The scores assigned to 118 farmers in three study areas in Petén were analyzed through analysis of variance (ANOVA) to identify the factors that account (...) for the variation in intensity levels, as measured through the index. The analyses reveal that the factors influencing agricultural intensity in Petén vary greatly from one study area to the next. This is due to differences in livelihood opportunities and strategies that, in turn, affect how agriculture fits into the local economy and how and why intensification is pursued. Variation in intensity levels can best be understood by considering the factors at the household and sub-regional scales that influence (a) whether farmers feel a need to intensify, (b) whether they see some benefit in doing so, and (c) whether they have the resources required to intensify production through particular strategies. Close attention must be paid to these factors by conservation and development organizations seeking to influence land use patterns and conserve forest in Petén. (shrink)

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus (...) and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences. (shrink)

The complementary properties and functions of cognitive artifacts and other external resources are integrated into the human cognitive system to varying degrees. The goal of this paper is to develop some of the tools to conceptualize this complementary integration between agents and artifacts. It does so by proposing a multidimensional framework, including the dimensions of information flow, reliability, durability, trust, procedural transparency, informational transparency, individualization, and transformation. The proposed dimensions are all matters of degree and jointly they constitute a multidimensional (...) space in which situated cognitive systems can be located and have certain dimensional configurations. These dimensions provide a new perspective on the conditions for cognitive extension. They are, however, not meant to provide a set of necessary and sufficient conditions, but to provide a toolbox for investigating the degree and nature of the integration of agent and artifact into “new systemic wholes”. The higher a situated system scores on the proposed dimensions, the more functional integration occurs, and the more tightly coupled the system is. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision (...) tree, K nearest neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

Data mining technology is an effective knowledge mining and data relationship induction technology based on massive data, which is widely used in data analysis in many fields. In order to improve the utilization effect of students’ performance and meet the teaching needs of modern education, data mining technology can be applied to the existing performance database to mine the data information and treatment. Data mining technology is used to analyse and process the data stored in the student achievement management system, (...) which provides the basis for improving the teaching quality and optimizing the teaching resources. Based on the analysis of the relevant data of large-scale English test results, this paper finds out the relevant rules that affect college English test results, forms the corresponding performance prediction rules, uses data mining technology to more comprehensively analyse the factors that affect students’ performance, establishes a model, and uses data mining tools to mine and analyse students’ English test data. It is of great practical significance to select the model with high accuracy, further optimize the parameters, make good use of the data, and then take targeted measures to guide the teaching reform, help students make more efficient learning plans, and improve and perfect the existing problems in teaching. (shrink)

There are important reasons for states to recognize and enforce the judgments of other states’ courts. There are also reasons that may militate against recognition or enforcement of certain foreign judgments, making it appropriate to calibrate or “fine tune” the presumption favoring recognition and enforcement so it is not applied too broadly. Most calibration principles, such as the principle that a judgment from a court lacking jurisdiction should not be recognized, are case-specific. However, one calibration principle that is, to our (...) knowledge, unique to the law of the United States stands out: the principle of systemic calibration, according to which U.S. courts must not recognize or enforce foreign judgments “rendered under a system which does not provide impartial tribunals or procedures compatible with the requirements of due process of law.” In this Article, we aim to shed empirical light on how U.S.-style systemic calibration operates in practice. We find that state-of-origin indicator scores related to systemic adequacy are on average higher when U.S. courts recognize or enforce foreign judgments than when they refuse to do so. Moreover, the probability of recognition and enforcement increases as these indicator scores increase. However, in only six of the 587 opinions in our dataset did a court refuse recognition or enforcement based explicitly on the systemic inadequacy ground. Thus, while the level of systemic calibration in U.S. courts is high, it is mostly achieved implicitly. Finally, even judgments from states with low systemic adequacy scores are sometimes recognized or enforced by U.S. courts. (shrink)

The complementary properties and functions of cognitive artifacts and other external resources are integrated into the human cognitive system to varying degrees. The goal of this paper is to develop some of the tools to conceptualize this complementary integration between agents and artifacts. It does so by proposing a multidimensional framework, including the dimensions of information flow, reliability, durability, trust, procedural transparency, informational transparency, individualization, and transformation. The proposed dimensions are all matters of degree and jointly they constitute a multidimensional (...) space in which situated cognitive systems can be located and have certain dimensional configurations. These dimensions provide a new perspective on the conditions for cognitive extension. They are, however, not meant to provide a set of necessary and sufficient conditions, but to provide a toolbox for investigating the degree and nature of the integration of agent and artifact into “new systemic wholes”. The higher a situated system scores on the proposed dimensions, the more functional integration occurs, and the more tightly coupled the system is. (shrink)

Policy officials often assert that the U.S. has the best health care system in the world, but a recent scorecard on U.S. health system performance finds that the U.S. achieves a score of only 65 out of a possible 100 points on key indicators of performance in five key domains: healthy lives, access, quality, equity, and efficiency, where 100 represents the best achieved performance in other countries or within the U.S. The U.S. should aim higher by adopting a set of (...) policies that will extend affordable health insurance to all; align financial incentives for health care providers to enhance value and achieve savings; organize the health care system around the patient to ensure that care is accessible and coordinated; meet and raise benchmarks for high-quality, efficient care; and ensure accountable national leadership and public-private collaboration. The incoming president and Congress should aspire to have the best health system in the world — not just assert it — and can do so by learning from examples of excellence within the U.S. and abroad. (shrink)

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to (...) separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring. (shrink)

Performance Generating Systems (PGS) are rule- and task-based approaches to improvisation on stage in theatre, dance, and music. These systems require performers to draw on predefined source materials (texts, scores, memories) while working on complex tasks within limiting rules. An interdisciplinary research team at a large Western Canadian university hypothesized that learning to sustain this praxis over the duration of a performance places high demands on executive functions; demands that may improve the performers’ executive abilities. These performers need (...) to continuously shift attention while remaining responsive to embodied and environmental stimuli in the present, they are required to inhibit automated responses and impulses using the rules of the system, and they strive towards addressing multitasking challenges with fluidity and flexibility. This study set out to test the mentioned hypothesis deductively and identify mediating processes inductively, using mixed empirical methods. In a small sample experiment with a control group (28 participants; 15 in intervention group, 13 in control group), standardized quantitative tests of executive functions (D-KEFS) were administered before and after an eight-week intervention. Participant-reported qualitative observations from the praxis were also collected throughout the intervention for grounded analysis. Within the limitations of small sample data, we found both statistically significant and trending effects on inhibition, problem-solving initiation, fluidity, and cognitive flexibility. Examining the mediating process, we found that participants experienced significant challenges sustaining the practice halfway through the intervention. The participant-reported solutions to these challenges, which emerged as the strongest behavioural patterns when coding the qualitative data to saturation, were strategies of problem-solving and of re-directing attention. These strategies support and advance our understanding of the effects measured in the standardized tests. In terms of application, our results identify characteristics of PGS that could potentially maintain and strengthen executive functions over and above less demanding performing arts interventions. The results also deliver new insight into how PGS works, which may contribute to the development and teaching of this artistic practice. (shrink)

In this paper we propose the multiple complex systems approach for assessing domain-general complex problem-solving skills and its processes knowledge acquisition and knowledge application. After defining the construct and the formal frameworks for describing complex problems, we emphasise some of the measurement issues inherent in assessing CPS skills with single tasks. With examples of the MicroDYN test and the MicroFIN test, we show how to adequately score problem-solving skills by using multiple tasks. We discuss implications for problem-solving research and (...) the assessment of CPS skills in general. (shrink)

Systems of logico-probabilistic (LP) reasoning characterize inference from conditional assertions interpreted as expressing high conditional probabilities. In the present article, we investigate four prominent LP systems (namely, systems O, P, Z, and QC) by means of computer simulations. The results reported here extend our previous work in this area, and evaluate the four systems in terms of the expected utility of the dispositions to act that derive from the conclusions that the systems license. In addition (...) to conforming to the dominant paradigm for assessing the rationality of actions and decisions, our present evaluation complements our previous work, since our previous evaluation may have been too severe in its assessment of inferences to false and uninformative conclusions. In the end, our new results provide additional support for the conclusion that (of the four systems considered) inference by system Z offers the best balance of error avoidance and inferential power. Our new results also suggest that improved performance could be achieved by a modest strengthening of system Z. (shrink)

Recommender systems are recently developed computer-assisted tools that support social and informational needs of various communities and help users exploit huge amounts of data for making optimal decisions. In this study, we present a new recommender system for assessment and risk prediction in child welfare institutions in Israel. The system exploits a large diachronic repository of manually completed questionnaires on functioning of welfare institutions and proposes two different rule-based computational models. The system accepts users’ requests via a simple graphical (...) interface, calculates the institutions’ profiles according to user preferences, and presents assessment scores, trends and comparative analyses of the corresponding data using assorted visual aids. Based on the analysis, the system offers three different strategies for objective assessment of the institutions’ functioning and risks. Qualitative and quantitative evaluation of the system’s effectiveness and accuracy demonstrates that it substantially improves the assessment process of a welfare institution. Moreover, it provides an effective tool for objective large-scale analysis of the institution’s overall state and trends, which were previously based primarily on the institution supervisors’ subjective judgment and intuition. In addition, the proposed recommender system has great practical and social impact as it may help identify and avert potential problems, malfunctions, flaws, risks and even tragic incidents in child welfare institutions, as well as increase their overall functioning levels. As a result, as a long-term social implication, the system may also help reduce inequality and social gaps in the Israeli society. (shrink)

A personalized recommender system is broadly accepted as a helpful tool to handle the information overload issue while recommending a related piece of information. This work proposes a hybrid personalized recommender system based on affinity propagation, namely, APHPRS. Affinity propagation is a semisupervised machine learning algorithm used to cluster items based on similarities among them. In our approach, we first calculate the cluster quality and density and then combine their outputs to generate a new ranking score among clusters for the (...) personalized recommendation. In the first phase, user preferences are collected and normalized as items rating matrix. This generated matrix is then clustered offline using affinity propagation and kept in a database for future recommendations. In the second phase, online recommendations are generated by applying the offline model. Negative Euclidian similarity and the quality of clusters are used together to select the best clusters for recommendations. The proposed APHPRS system alleviates problems such as sparsity and cold-start problems. The use of affinity propagation and the hybrid recommendation technique used in the proposed approach helps in improving results against sparsity. Experiments reveal that the proposed APHPRS performs better than most of the existing recommender systems. (shrink)

The problem of determining an optimal benchmark to inefficient decision-making units is an important issue in the field of performance analysis. Previous methods for determining the projection points of inefficient DMUs have only focused on one objective and other features have been ignored. This paper attempts to determine the best projection point for each DMU when the inputs and outputs data are in stochastic form and presents an alternative definition for the best projection by considering three main aspects: technical efficient, (...) minimal cost, and maximal revenue as much as possible. Considering the important role of the electricity industry in the economic growth of each country, a practical example has been implemented on 16 regional electricity companies in Iran in 9 consecutive periods. The efficiency score along with the projection points of the three technical models ), cost, and stochastic revenue are compared with the projection point obtained from the model presented in this article, which simultaneously meets these three objectives, showing the improvement of companies’ performance. (shrink)

System justification theory holds that disadvantaged groups rationalize the current social system, even if it is unfavorable to them. Epistemic, relational, and existential needs are factors that explain this phenomenon. However, the literature has not yet examined and explained when disadvantaged groups no longer rationalize current social systems. This study uses a questionnaire survey method to study the moderating effect of collectivism on disadvantaged mindset and system-justifying beliefs. It found that collectivism can influence the predictive effect of disadvantaged mindset (...) on system-justifying beliefs. For people who scored low in collectivism, a disadvantaged mindset can significantly negatively predict system-justifying beliefs; for those who scored high in collectivism, a disadvantaged mindset no longer predicts system-justifying beliefs. Therefore, these results show that collectivist values are important for explaining system justification in disadvantaged groups. When collectivist values decline, the level of rationalization of the social system by disadvantaged groups also decreases. (shrink)

Insular populations have traditionally drawn a lot of attention from epidemiologists as they provide important insights regarding transmission of infectious diseases and propagation of epidemics. There are numerous historical instances where isolated populations showed high morbidity once a new virus entered the population. Building upon that and recent findings that the activation of the behavioral immune system depends both upon one’s vulnerability and environmental context, we predicted that, during the COVID-19 pandemic, place of residence explains a significant proportion of variance (...) in preferred interpersonal distances, animosity toward strangers, and willingness to punish those who do not adhere to COVID-19 preventive measures. With 48 populated islands, Croatia provides a fruitful testing ground for this prediction. We also opted to explore relations among BIS-related variables and social cognitions in a more natural context than has previously been done. The study was conducted online, on Croatian residents, during April and May 2020. As expected, the BIS variables contributed significantly to preferred interpersonal distances, negative emotions toward strangers, and willingness to punish those who do not adhere to COVID-19 preventive measures. Furthermore, our results showed that geographical location explained a significant amount of variance in preferred social distances and negative emotions toward foreigners. As Croatian islands are extremely frequent travel destinations, these differences between mainlanders and islanders cannot be explained by the lack of exposure to foreigners. Additionally, we found that scores on preferred interpersonal distances, pathogen disgust, and germ aversion were significantly higher compared to those obtained in Croatian samples before the COVID-19 pandemic. Furthermore, men scored higher in perceived infectability than before the COVID-19 pandemic, and women did not, which reflects the objectively higher risk of SARS-CoV-2 for men than for women. Taken together, our results support the notion that BIS is a highly adaptive and context-dependent response system, likely more reactive in more susceptible individuals. (shrink)

An extension to a rating system for tracking the evolution of parameters over time using continuous variables is introduced. The proposed rating system assumes a distribution for the continuous responses, which is agnostic to the origin of the continuous scores and thus can be used for applications as varied as continuous scores obtained from language testing to scores derived from accuracy and response time from elementary arithmetic learning systems. Large-scale, high-stakes, online, anywhere anytime learning and testing inherently comes with (...) a number of unique problems that require new psychometric solutions. These include (1) the cold start problem, (2) problem of change, and (3) the problem of personalization and adaptation. We outline how our proposed method addresses each of these problems. Three simulations are carried out to demonstrate the utility of the proposed rating system. (shrink)