The integration of ethics into the day-to-day work of research and innovation is an important but difficult challenge. However, with the Aachen method for identification, classification and risk analysis of innovation-based problems an approach from an engineering perspective is presented that enables the integration of ethical, legal and social implications into the day-to-day work of R&I practitioners. AMICAI appears in particular capable of providing a procedural guidance for R&I practitioners based on a method established in (...) engineering science, breaking down the object of consideration into partial aspects and prioritizing the innovation-based problems in dependence of potential risk. This enables the user to apply AMICAI continuously during all stages of the research and development process and to analyze and choose between certain sociotechnical alternatives. In this way, problems that affect ethical, legal, and social aspects can be understood, reflected and considered in the mostly technically focused R&D process. The paper gives a general guidance about AMICAI by describing principles and assumptions, providing the steps of analysis and application aids, giving an example application, explaining the necessary adjustments of AMICAI compared to the methodical basis of failure mode, effects, and criticality analysis and discussing the advantages and limits. AMICAI’s simple applications can stimulate interdisciplinary cooperation in the R&D process and be a starting point for the development of an “open RRI risk analysis platform” allowing society to evaluate innovation-based problems. (shrink)

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

Research ethics committees (RECs) evaluate whether the risk-benefit ratio of a study is acceptable. Decentralized clinical trials (DCTs) are a novel approach for conducting clinical trials that potentially bring important benefits for research, including several collateral benefits. The position of collateral benefits in risk-benefit assessments is currently unclear. DCTs raise therefore questions about how these benefits should be assessed. This paper aims to reconsider the different types of research benefits, and their position in risk-benefit assessments. We (...) first propose a categorization of research benefits, based on the types of benefits that can be distinguished from the literature and ethical guidelines. Secondly, we will reconsider the position of collateral benefits. We argue that these benefits are not fundamentally different from other benefits of research and can therefore be included in risk-benefit assessments of DCTs. (shrink)

Computer vision research involving street and road detection methods usually focuses on driving assistance and autonomous vehicle systems. In this context, street segmentation occurs in real-time, based on images centered on the street. This work, on the other hand, uses street segmentation for urban planning research to classify pavement types of a city or region, which is particularly important for developing countries. For this application, it is needed a dataset with images from various locations for each street. These images (...) are not necessarily centered on the street and include challenges that are not common in street segmentation datasets, such as mixed pavement types and the presence of faults and holes on the street. We implemented a multi-class version of a state-of-the-art segmentation algorithm and adapted it to perform street pavement classification, handling navigation along streets and angle variation to increase the accuracy of the classification. A data augmentation approach is also proposed to use preliminary results from the test instances as new ground truth and increase the amount of training data. A dataset with more than 300,000 images from 773 streets from a Brazilian city was built. Our approach achieved a precision of 0.93, showing the feasibility of the proposed application. (shrink)

The status of risk factors and disease remains a disputed question in the theory and practice of medicine and healthcare, and so does the related question of delineating disease boundaries. I present a framework based on Bernard Lonergan’s account of emergent probability for differentiating (1) generically distinct levels of systematic function within organisms and between organisms and their environments and (2) the methods of functional, genetic, and statistical investigation. I then argue on this basis that it is possible (...) to understand disease in terms of biological or higher intra-level dysfunction, risk factors—including genetic risk factors—in terms of statistical inter-level conditioning of a given stage or developmental sequence of systematic functioning, and the empirical boundaries of disease in terms of the limits of both functional categorization (from an epistemic standpoint) and upper-level integration of lower-level processes and events (from an ontological standpoint). (shrink)

Mental disorders as defined by current classifications are not fully supported by scientific evidence. It is unclear whether main disorders should be broken down into separate categories or disposed along a continuous spectrum. In the near future, new classes of mental disorders could be defined through associations of so-called abnormalities observed at the genetic, molecular and neuronal circuitry levels.

The temperament of the body is an essential constituent for health conservancy and diagnosis of several diseases. Hence, general body temperament and uterine dystemperament with abnormal vaginal discharge need evaluation. In addition, we also applied a computational intelligence technique for enhancing scientific validity to classify the warm-cold and wet-dry temperaments. This trial included a total of 66 participants with a vaginal discharge of reproductive age. Data included demographic characteristics of the participants, symptoms associated with vaginal discharge, women’s general temperament, and (...) symptoms of uterine dystemperament. Correlation between general body temperament and age, abnormal vaginal discharge, and its associated symptoms was also performed. Additionally, we used the Support Vector Machine-Radial Basis Function model to classify the warm-cold and wet-dry temperaments. Warm general temperament was highly prevalent, followed by moderate on the warm-cold temperament scale. In wet-dry temperament, moderate general body temperament was more prevalent. In warm-cold and wet-dry scores, 78.78% and 74.24% had warm and wet uterine dystemperament, respectively. The age and symptoms were correlated with general temperament. A strong positive correlation was found between warm general temperament and warm dystemperament of the uterus. In addition, our SVM-RBF CV-5 classification model achieved the highest accuracy. Our results showed that vaginal discharge is more common in warm general temperament and warm-wet dystemperament of the uterus. The same has been proven by computational intelligence. Nevertheless, vaginal discharge can also happen in normal and other temperaments. (shrink)

The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article (...) illustrates with an example how psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification. (shrink)

There is scarce knowledge about the influence of the professional group, education, and age on public perspectives on the risks and benefits of forensic DNA databases. Based on data collected through an online questionnaire applied to 628 individuals in Portugal, this research fills that gap. More than three quarters of the respondents believed that the Portuguese forensic DNA database can help fight crime more efficiently and develop a swifter and more accurate justice, whereas only approximately half thought that it (...) could deter and prevent crime. Lack of security and control over access to data and the future unforeseen misuses of genetic information were the most relevant risks selected by the participants. Health care and life sciences professionals were more likely to agree with all the benefits, as opposed to those working in the field of law enforcement. More educated and older participants disagreed with benefits more often. Concerns with the risks increased with age. (shrink)

Electroencephalography and functional near-infrared spectroscopy have potentially complementary characteristics that reflect the electrical and hemodynamic characteristics of neural responses, so EEG-fNIRS-based hybrid brain-computer interface is the research hotspots in recent years. However, current studies lack a comprehensive systematic approach to properly fuse EEG and fNIRS data and exploit their complementary potential, which is critical for improving BCI performance. To address this issue, this study proposes a novel multimodal fusion framework based on multi-level progressive learning with multi-domain features. (...) The framework consists of a multi-domain feature extraction process for EEG and fNIRS, a feature selection process based on atomic search optimization, and a multi-domain feature fusion process based on multi-level progressive machine learning. The proposed method was validated on EEG-fNIRS-based motor imagery and mental arithmetic tasks involving 29 subjects, and the experimental results show that multi-domain features provide better classification performance than single-domain features, and multi-modality provides better classification performance than single-modality. Furthermore, the experimental results and comparison with other methods demonstrated the effectiveness and superiority of the proposed method in EEG and fNIRS information fusion, it can achieve an average classification accuracy of 96.74% in the MI task and 98.42% in the MA task. Our proposed method may provide a general framework for future fusion processing of multimodal brain signals based on EEG-fNIRS. (shrink)

Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. (...) Deep neural network algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy and correct prediction and offers the prospect of other research in this area. (shrink)

The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering (...) center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm. (shrink)

Due to the advent of deglobalization and regional integration, this article aims to adopt LASSO-based network connectedness to estimate the multiscale tail risk spillover effects of global stock markets. The results show that tail risk varies across frequencies and shocks. In static analysis, the risk is centered mostly on the developed European and North American markets at a low frequency, and regionalization is imposed on the moderate frequency. Moreover, emerging markets could be sources of risk (...) spillover, especially at the highest frequency where there is no absolute risk center. In dynamic analysis, we use rolling window estimation and find that different frequencies identify distinct episodes of shocks, which provides us with the reason for the diverse risk centers at different time scales in static analysis. Our findings provide heterogeneous financial practitioners, regulators, and investors with diverse characteristics of stock markets under multiple time horizons and help them operate their own trading strategies. (shrink)

In “Risk Based Passenger Screening in Aviation Security: Implications and Variants of a New Paradigm”, Sebastian Weydner-Volkmann describes the current paradigm shift from ‘traditional’ forms of screening to ‘risk based passenger screening’ (RBS) in aviation security. This paradigm shift is put in the context of the wider historical development of risk management approaches. Through a discussion of Michel Foucault, Herfried Münkler and Ulrich Beck, Weydner-Volkmann analyses the shortcomings of such approaches in public security policies, which (...) become especially evident in the aviation security context. As he shows, the turn towards methods of RBS can be seen as an attempt to address a trade-off ‘trilemma’ between the effective provision of security, the implied costs for industry and passengers, and the ethical, legal and societal implications of the screening procedures. In order to analyse foreseeable outcomes of embracing RBS, he differentiates three prototypical variants of the new paradigm on the basis of their main referent and rationale. For each variant, he then subsequently assesses the implications for the ‘trilemma’, after having unveiled the criteria of analysis that will necessarily have to be followed within a serious appraisal of RBS methods. (shrink)

This article describes the peculiarities of the formation and modeling of the enterprise risk management system in modern conditions. The main purpose of the research is further development of theoretical and methodical principles and development of practical recommendations aimed at improvement of management of business risks of wholesale enterprises. The critical analysis of literature sources and approaches to solving problems of enterprise risk management testifies to the lack of attention of scientists of the present-day to the investigated problem (...) in the sphere of activity of enterprises of wholesale trade. The urgency of this problem is caused by the existing losses of wholesale companies due to imperfect management as a result of the influence of various business risks. The methodological basis of the research is the system of methods used to obtain the final results: theoretical generalization, scientific abstraction and comparison – to deepen the essence of the concept of "business risk"; analysis and synthesis, grouping – for classification of business risks in the sphere of wholesale trade; methods of analysis, synthesis and logical generalization – to obtain conclusions during determination of the state and problems of realization of business activity in the sphere of wholesale trade of enterprises of Ukraine; systematic approach – for improvement of system of management of business risks of enterprises of wholesale trade on the basis of commercial values. The object of the research is the business risk in the wholesale trade environment. In the article the further development of the risk-based apparatus has been made, the negative consequences of the impact of business risks on the activities of the wholesale trade enterprises have been clarified, the enterprise risk management system of the enterprises has been built, which will reflect an integrated approach to its realization, practical recommendations on the formation of a model of management of business risks of the wholesale trade enterprises on the basis of commercial values. The results of the survey can be useful for business structures, which are focused on improving the management of business risks. (shrink)

International regulation of autonomous weapon systems (AWS) is increasingly conceived as an exercise in risk management. This requires a shared approach for assessing the risks of AWS. This paper presents a structured approach to risk assessment and regulation for AWS, adapting a qualitative framework inspired by the Intergovernmental Panel on Climate Change (IPCC). It examines the interactions among key risk factors—determinants, drivers, and types—to evaluate the risk magnitude of AWS and establish risk tolerance (...) thresholds through a risk matrix informed by background knowledge of event likelihood and severity. Further, it proposes a methodology to assess community risk appetite, emphasizing that such assessments and resulting tolerance levels should be determined through deliberation in a multistakeholder forum. The paper highlights the complexities of applying risk-based regulations to AWS internationally, particularly the challenge of defining a global community for risk assessment and regulatory legitimization. (shrink)

Virtual enterprise is a dynamic alliance of businesses, in which multiple members undertake joint research, development, manufacturing, operation, etc. The complexity of the relationship between business members, coupled with many new technologies or methods applied in the alliance operation, leads to more uncertain factors and difficulties in the operation and risk management of the virtual enterprise. The distributed decision-making model is a fast and effective decision-making model, in which dispersed intellectual resources and information resources are dynamically integrated through virtual (...) organization forms and operating mechanisms, and the dynamic reorganization, result evaluation, and synthesis of the decision-making system are realized by certain control rules. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the risk control of virtual enterprise; elaborated the development background, current status, and future challenges of distributed decision-making model; introduced the related works of decision-making variable calculation and constraint determination; constructed a risk control model for virtual enterprise based on distributed decision-making model; analyzed the multiobjective model and interval programming model of risk control; established a risk control approach for virtual enterprise based on distributed decision-making model; performed the optimal allocation of risk control funds and the selective optimization of backup allies; and finally conducted a case analysis. The study results show that, in view of the diversity of relationships between alliance businesses and the complexity of the distribution of cooperative areas, the distributed decision-making model can more reasonably describe the relationship between alliance members and accurately control the risks of virtual enterprise from multiple angles, which can effectively manage the risks of virtual enterprise. The study results of this paper provide a reference for further research on the risk control of virtual enterprise based on distributed decision-making model. (shrink)

In this paper, we provide a detailed review of previous classifications of 2 × 2 games and suggest a mathematically simple way to classify the symmetric 2 × 2 games based on a decomposition of the payoff matrix into a cooperative and a zero-sum part. We argue that differences in the interaction between the parts is what makes games interesting in different ways. Our claim is supported by evolutionary computer experiments and findings in previous literature. In addition, we provide (...) a method for using a stereographic projection to create a compact 2-d representation of the game space. (shrink)

The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base (...) LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms. (shrink)

The effectiveness of a quality management program of medical care has to meet not just the quality management standards, but also has to be flexible, taking into account the features of a medical organization and the existing problems in the medical care support. The effectiveness of a quality management program is based on feedback, so the quality control of medical care is so important. The study of problems in medical care gives us the ability to improve both, the quality (...) management program of medical care and the quality and scope of medical services constantly. (shrink)

Total knee arthroplasty (TKA) is a commonly implemented elective surgical treatment for end-stage osteoarthritis of the knee, demonstrating high success rates when assessed by objective medical outcomes. However, a considerable proportion of TKA patients report significant dissatisfaction postoperatively, related to enduring pain, functional limitations, and diminished quality of life. In this conceptual analysis, we highlight the importance of assessing patient-centred outcomes routinely in clinical practice, as these measures provide important information regarding whether surgery and postoperative rehabilitation interventions have effectively remediated (...) patients’ real-world “quality of life” experiences. We propose a novel precision medicine approach to improving patient-centred TKA outcomes through the development of a multivariate machine-learning model. The primary aim of this model is to predict individual postoperative recovery trajectories. Uniquely, this model will be developed using an interdisciplinary methodology involving non-linear analysis of the unique contributions of a range of preoperative risk and resilience factors to patient-centred TKA outcomes. Of particular importance to the model’s predictive power is the inclusion of a comprehensive assessment of modifiable psychological risk and resilience factors that have demonstrated relationships with TKA and other conditions in some studies. Despite the potential for patient psychological factors to limit recovery, they are typically not routinely assessed preoperatively in this patient group, and thus can be overlooked in rehabilitative referral and intervention decision-making. This represents a research-to-practice gap that may contribute to adverse patient-centred outcomes. Incorporating psychological risk and resilience factors into a multivariate prediction model could improve the detection of patients at risk of sub-optimal outcomes following TKA. This could provide surgeons and rehabilitation providers with a simplified tool to inform postoperative referral and intervention decision-making related to a range of interdisciplinary domains outside their usual purview. The proposed approach could facilitate the development and provision of more targeted rehabilitative interventions on the basis of identified individual needs. The roles of several modifiable psychological risk and resilience factors in recovery are summarised, and intervention options are briefly presented. While focusing on rehabilitation following TKA, we advocate for the broader utilisation of multivariate prediction models to inform individually-tailored interventions targeting a range of health conditions. (shrink)

In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are (...) used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems. (shrink)

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an (...) eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks. (shrink)

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

Most research on risk aversion in behavioral science with human subjects has focused on a component of risk aversion that does not adapt itself to context. More recently, studies have explored risk aversion adaptation to changing circumstances in sequential decision-making tasks. It is an open question whether one can identify evidence, at the single subject level, for such risk aversion adaptation. We conducted a behavioral experiment on human subjects, using a sequential decision making task. We developed (...) a model-based approach for estimating the adaptation of risk-taking behavior with single-trial resolution by modelling a subject’s goals and internal representation of task contingencies. Using this model-based approach, we estimated the subject-specific adaptation of risk aversion depending on the current task context. We found striking inter-subject variations in the adaptation of risk-taking behavior. We show that these differences can be explained by differences in subjects’ internal representations of task contingencies and goals. We discuss that the proposed approach can be adapted to a wide range of experimental paradigms and be used to analyze behavioral measures other than risk aversion. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Philosophy, Psychiatry, & Psychology 10.1 (2003) 57-62 [Access article in PDF] Listening to People Or Listening to Prozac?Another Consideration of Causal Classifications Jennifer Hansen Keywords causal classification, descriptivism, melancholia, neurasthenia, depression, cultural relativism. The shape and detail of depression have gone through a thousand cartwheels, and the treatment of depression has alternated between the ridiculous and the sublime, but the excessive sleeping, inadequate eating, suicidiality, withdrawal from social (...) interaction, and relentless despair are all as old as the hill tribes, if not as old as the hills. In the years since man achieved the capacity for self-reference, shame has come and gone; treatments for bodily complaints have alternated and crossed with treatments for spiritual ones; pleas to external gods have echoed pleas to internal demons. To understand the history of depression is to understand the invention of human being as we now know and are him. Our Prozac-popping, cognitively focused, semi-alienated postmodernity is only a stage in the ongoing understanding and control of mood and character. (Solomon 2001, 286) ALTHOUGH ANDREW SOLOMON clearly believes that "the shape and detail of depression have gone through a thousand cartwheels," Jennifer Radden gives us pause before assuming that depression is just another name for Dame Melancholy. Solomon's claim depends on a deeper metaphysical claim about the nature of depression and its causes. The current method of psychiatric nosology, descriptivism, does not necessarily support equating melancholia and depression. Furthermore, it appears that adopting descriptivism as the sole method of classifying mental disorders commits us to cultural relativist psychiatry. Below I reconsider whether we want to rule out causal classifications and suggest that embracing both descriptivism and causal classifications offer us greater possibilities in understanding human illness and culture.Although the occasion for Jennifer Radden's essay is to investigate whether or not melancholia and depression refer to the same thing, her essay detours into a consideration of extra-descriptivist models of psychiatric illness versus the descriptivism of current psychiatric nosology. This detour turns out to be warranted; her purely academic question yields to a timelier question concerning the proper methodology for classifying mental disorders. Whereas debates about psychiatric nosology are perennial, the recent success of the new generation of antidepressants [End Page 57] is reviving biological psychiatry and its search for the neurochemical origins of depression. Hence, embedded in Radden's question concerning whether or not melancholia is the same thing as clinical depression, is another important question concerning the causes of mental illness, particularly depression.Currently, with its descriptivist approach, the Diagnostic and Statistical Manual of Mental Disorders (DSM) does not classify mental illness, such as clinical depression, based on underlying causes. The cluster of symptoms and behavioral signs that lead a clinician to diagnose one as depressed that are contained in either the DSM or the International Classification of Diseases (ICD) may be too fuzzy or elusive to give any clear distinction between major depression, mild depression, and the blues. For example, one group of epidemiologists points out that [t]he DSM-III, DSM-III-R, and DSM-IV acknowledge that depression, like many mental disorders, has no currently obvious natural classes... If natural classes of depression exist, then their nosology awaits the discovery of distinct and reliable genetic, biological, clinical, or other predictors of risk, severity, or clinical course. Clinicians have a tradition of separating depressions into subtypes; however, this subtyping has not yet led to clear-cut delineations of syndromes in populations. (Kaelber 1995, 4) The evidence is still insufficient to establish the biological or psychological origins of depression. Although many theories exist that explain why people become depressed, scientifically we are not yet in a position to determine which is the right theory. From a pragmatic perspective, however, whether or not we know what causes depression, we can nonetheless attempt to alleviate suffering from depression. And it is with this goal in mind that the DSM-IV and ICD-10 classify mental disorders.Now, given that our current methodology for psychiatric nosology does not establish causes, it is also insufficient for determining what... (shrink)

As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. The primary contents are bidirectional encoder representation from transformers-convolution neural network sentiment tendency analysis, word frequency characteristic analysis, and semantic network analysis. The realism of the suggested framework is demonstrated by (...) using the waste classification policy as an example. The findings indicate a substantial relationship between perceived subject participation and policy pilot areas and that perceived subject participation is repeating. On this premise, specific recommendations are made to encourage policy implementation. (shrink)

Parfit advertised the priority view as a new and fundamental theory in the ethics of distribution. He never discusses risk, and many writers follow suit when discussing the priority view. This article formalizes two popular arguments for a commonly accepted risk-free definition of the priority view. One is based on a direct attempt to define the priority view, the other is based on a contrast with utilitarianism and egalitarianism. But neither argument succeeds, and more generally, it (...) is not possible to make sense of the priority view in a risk-free framework. The article pays particular attention to the proper role of evaluative measurements in theorizing about the ethics of distribution. Despite appearances, approaching the priority view in terms of risk from the outset is argued to be quite natural. (shrink)

The purpose of the present thesis is to apply philosophical methods to the ongoing debate of the precautionary principle, in order to illuminate this debate. The thesis consists of an Introduction and five papers. Paper I con-cerns an objection to the method of conceptual analysis, the Charge from Psychology. After a brief characterisation of conceptual analysis, I argue that the Charge from Psychology is misdirected. In Paper II, the method of conceptual analysis is applied to the concept of precaution which (...) is ana-lysed in terms of precautionary actions. The purpose is explicatory. A definition involving three necessary and jointly sufficient conditions is proposed, and the implications of this analysis for the debate on the pre-cautionary principle are discussed. Paper III attempts to provide an ana-lytical apparatus which may be used for finding improved formulations of the precautionary principle. The approach is lexicographical. Several exist-ing and possible formulations of the precautionary principle are examined, and four common elements and a common structure of the precautionary principle are identified. It is suggested that the analytical apparatus pre-sented can be used in negotiations of the precautionary principle. Paper IV questions the soundness of some arguments against the precautionary prin-ciple. Five common arguments are discussed and rejected. In Paper V, two of these arguments are further discussed. I argue that an attempt at rejec-tion of the precautionary principle delivered by John Harris and Søren Holm is unwarranted, because their arguments against it are based on in-terpretations of the precautionary principle that ignore context. Paper VI deals with the idea of de minimis risk. After a discussion of the distinction between disregarding a risk and accepting it, I examine one way of deter-mining how small a risk ought to be in order to be disregarded, namely the use of natural risk levels as benchmarks. I argue that this approach fails, even if the distinction between what is natural and what is not natural can be upheld. (shrink)

In this text, our question is what is the current regulatory trend in countries that are not considered central in the development of artificial intelligence, such as Brazil: a preventive approach, or an experimental approach? We will analyze the bills (PL) that are being processed in legislative houses at the state level, and at the federal level, highlighting some elements, such as: Delimitation of the object (conceptualization), fundamental principles, ethical guidelines, relationship with human work, human supervision, and guidelines (...) for public authorities. Based on these elements, we identified how the issues that drive the contemporary debate on AI have been addressed in these legislative initiatives. The results indicate that the strongest national regulation proposal mentions risks, rights, governance, economic agents; these mentions dialogue with the concerns discussed by researchers around the world, regarding the mitigation and prevention of risks, regarding the preservation of individual and collective rights, the role of the State as a regulator and supervisor, and, as was to be expected, the maintenance of economic policies that possibly tend to protect the market; proposes that the foundations of the development, implementation and use of AI have values consistent with ‘human centered AI’ (Bingley et al. 2023), but also points to the neoliberal matrix; introduces human supervision as an effective element in the AI systems cycle; the trend that we can observe in Brazil is the configuration of a regulation that harmonizes articles that favor the preventive approach, with the presence of articles that favor the experimentation approach. (shrink)

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

Many organizations must adjust their supply chains and business environments to remain competitive and retain market share. However, despite the benefits of such developments for organizations, they can lead to supply chain disruptions in many cases. An essential aspect of modern organizations is closed-loop supply chain management, which reduces risk by coordinating processes. This paper presents a mathematical model of a closed-loop green supply chain under uncertain conditions. A detailed description of the model and current assumptions for a specific (...) industry are provided. Consequently, the model is developed with several relevant objectives for the design of the network. Our proposed model has the following characteristics: being multiobjective, multiproduct, and multiperiodic. Our analysis indicates that the applied model can be used both to increase profitability and to reduce environmental damage. The problem was made more uncertain using fuzzy logic. Torabi and Hassini method is combined with Jiménez’s proposed method to solve the problem. (shrink)

Acoustic emission technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing (...) the dimension was developed. By means of the principle component analysis for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage. (shrink)

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of (...) autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are the recommendation of an active stopping planner using different smooth generators from a modelling study, the validation of their physical characteristics and the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing. (shrink)

Based on applied linguistics, this study looked at the decision support system for emphasizing self-assurance in academic writing. From a generic perspective, academic writing has been considered both a process and a product. It has highlighted the planning composite processes, editing, composing, revising, and assessment, which depend upon the familiarity of someone with confidence in their capability for engagement in these activities. As a product, it has focused on the writing results through the product’s characteristics. These contain specific content (...) areas in acceptable depth and well-structured technical vocabulary. Higher education aims to support students in optimizing their potential for achieving satisfactory outcomes. For example, the assessment of grades involves academic writing, contributing to the degree course classification. Students have differences in many respects, such as expectations, background knowledge, and study and learning approaches. There were varying students’ beliefs about what academic writing is for evaluation. Modern-day motivations and theories highlight the significance of students’ confidence in their studies. The role of high confidence can support students to apply more effort toward setting challenging goals. Students may find it more difficult to succeed in higher education if they lack confidence in their academic writing abilities. A DSS has many applications in diverse areas and can play a significant role in the ranking and prioritization process. The current study has considered the DSS for prioritizing self-assurance in academic writing based on applied linguistics. Various criteria were considered for the evaluation of the research. The Super Decision software was used for the experimental process of the proposed research. The results of the study show the effectiveness of the proposed research. (shrink)

In the debate about the legal value of naked statistical evidence, Di Bello argues that (1) the likelihood ratio of such evidence is unknown, (2) the decision-theoretic considerations indicate that a conviction based on such evidence is unacceptable when expected utility maximization is combined with fairness constraints, and (3) the risk of mistaken conviction based on such evidence cannot be evaluated and is potentially too high. We argue that Di Bello’s argument for (1) works in a rather (...) narrow context, and that (1) is not exactly in line with the way expert witnesses are required to formulate their opinions. Consequently, Di Bello’s argument for (2), which assumes (1), does not apply uniformly to all convictions based on naked evidence. Moreover, if Di Bello’s analysis is correct, it applies also to eyewitness testimony, given empirical results about its quality, and so the distinctions drawn by DiBello cut across the distinction between naked statistical evidence and other types of evidence. Finally, if we weaken the rather strong requirement of precise measurability of the risk of mistaken conviction, to the availability of reasonable but imprecise and fallible estimates, many field and empirical studies show that often the risk of mistaken conviction based on naked statistical evidence can be estimated to a similar extent as the risk of mistaken conviction based on any other sort of evidence. (shrink)

Highlights: -/- • -/- Proposes forward-looking approach to studying climate collapse risks. • -/- Suggests diminishing returns on climate adaptation as a collapse mechanism. • -/- Suggests strategies for sustainable adaptation pathways in face of climate change. • -/- Illustrates analysis with examples of small island states and global food security. -/- Abstract: -/- This article proposes a forward-looking approach to studying societal collapse risks related to climate change. Such an approach should indicate how to study emerging (...) collapse risks and suggest strategies for adapting to them. Our approach is based on three postulates that facilitate a forward-looking approach: (1) collapse, if it occurred, would be a lengthy process rather than an abrupt event; (2) significant collapse risks already exist in some places; and (3) diminishing returns on adaptation to intensifying climate impacts are a key driver of collapse risks. The first two postulates suggests that collapse risks can be studied in process, while the third points to strategies for adaptation pathways that avoid unsustainable diminishing returns. Applying diminishing returns to climate change adaptation, rather than sociopolitical complexity or resource extraction, is also a novel theoretical contribution to collapse literature. [Open access]. (shrink)

Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq (...) deep neural network, such as a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks. (shrink)

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous (...) centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s. (shrink)

Sacred Science is one of the traditionalists' principles among them some philosophers like Sayyed Hossein Nasr, Frithjof Schuon, and Rene Guenon can be noted. Having this principle, a theory of science, and a religious theory in general an Islamic one in particular, can be explained and reconstructed. This reconstruction reveals that traditionalists can be considered as advocates of religious science. In this paper, concepts like sacred science, and tradition and traditionalism are explained, criteria of classification of religious science's theories (...) are mentioned, and theories of religious scienceare analyzed as minimal, maximal, and median ones, and also as reductive, established, and mixed theories. Reconstruction of traditionalists' account shows despite of differences among scholars' view, it is possible to accept religious science in a reductive approach based on sacred science, and traditional sciences can be shown as examples of religious science. Particularly, it can be said, in Sayyed Hossein Nasr's view, sciences with Islamic attributes are realized, and they have to be recognized in contemporary age; in other words, Islamic sciences must be revived. (shrink)

Renowned for his geological studies, Jean-Baptiste d'Omalius d'Halloy also pursued a far less known anthropological career. In different ‘editions’ of his main work, the first Belgian armchair anthropologist tried to divide the world population into races, branches, families and peoples. As a true figure of transition between the 18th and 19th century, he used both human and natural sciences to establish his racial classification, based on natural characters and geography, but also evolution, history and language. Influenced by both (...) William Frederic Edwards and Paul Broca, d'Omalius often defended polygenist views, despite his catholic and monogenist conviction and his refusal to accept the multiple origin of humankind. It is also notable that d'Omalius, like Tacitus and Montesquieu before him, claimed that the sole origin of humankind could to be situated in Northern European, which for d'Omalius still represented the homeland of the most civilized races. Critical of the ‘Aryan myth’, he stated that ‘Germanic’ culture and language had spread over Asia and not the other way around, an argument that caused conflict within learned societies and at international conferences. For the first time, based on new archival material, this article offers an overview of Belgian anthropology before the creation of Société d'Anthropologie de Bruxelles in 1882. (shrink)

In this paper, we present a Capabilities -based Approach to the acceptability and the tolerability of risks posed by natural and man-made hazards. We argue that judgments about the acceptability and/or tolerability of such risks should be based on an evaluation of the likely societal impact of potential hazards, defined in terms of the expected changes in the capabilities of individuals. Capabilities refer to the functionings, or valuable doings and beings, individuals are able to achieve given available (...) personal, material, and social resources. The likely impact of a hazard on individuals’ capabilities should, we argue, be compared against two separate thresholds. The first threshold specifies the minimum level of capabilities attainment that is acceptable in principle for individuals to have in the aftermath of a hazard over any period of time. This threshold captures the level that individuals’ capabilities ideally should not fall below. A risk is acceptable if the probability that the attained capabilities will be less than the acceptable level is sufficiently small. In practice, it can be tolerable for some individuals to temporarily fall below the acceptable threshold, provided this situation of lower capabilities attainment is temporary, reversible, and the probability that capabilities will fall below a tolerability threshold is sufficiently small. This second, tolerable threshold delimits an absolute minimum level of capabilities attainment below which no individual in a society should ever fall, regardless of whether that level of capabilities attainment is temporary or reversible. In this paper, we describe and justify this Capabilities -based Approach to the acceptability and tolerability of risks. We argue that the proposed theoretical framework avoids the limitations in current approaches to acceptable risk. The proposed approach focuses the attention of risk analysts directly on what should be our primary concern when judging the acceptability and the tolerability of risks, namely, how risks impact the well-being of individuals in a society. Also, our Capabilities -based Approach offers a transparent, easily communicable way for determining the acceptability and the tolerability of risks. (shrink)

At the present time, when we are experiencing one of the extraordinary conditions in which the basics of life are shaken, exceptional practices concerning the sources of the meaning of the world of life have become one of the urgent issues for the sociology of religion to consider. This study discusses the reactions of people to the effects of the COVID-19 pandemic on the regularity of social life in the early stages in the framework of Muslim religious groups. Since the (...) forms of social organization reveal reactions in their own specific forms, a research design that considers the differences were preferred. The provision that ‘man is social by nature,’ which is the indispensable principle of social sciences, has been the main feature of important issues to elaborate in the restrictions of today’s pandemic. Religious socialization, which is important for the inquiry, is a phenomenon that is desired to be carried out on a continuous basis. Its performative dimension draws attention to important elements that affect the formation and the social ontology of the group, such as consciousness and solidarity. While some religious groups insist on face-to-face interaction and rituals, some of them prefer to use effectively digital tools and channels. This differing approach has become a public issue, especially during the pandemic process. On the other hand, there is a discussion in the context of the digital world of new media tools and the travel possibilities of transportation technology. The theoretical basis of this study, which deals with the insistence of religious groups on face-to-face interaction and rituals is represented from the scope of Erving Goffman. His analyzes face-to-face communication and his comments emphasizing the ritualistic nature of daily life are operationalized through two basic case studies: the first case is the persistence and practice of Tablīghī Djamā‘at, in its annual meetings in the early days of the pandemic. The discussion started with the practice of the religious group in Delhi (Nizamuddin Markaz), since they did not cancel their annual meeting despite the diagnosis of the disease. The second case is based on the ritual insistence of pilgrims in visiting shrines in the Iranian city of Qom. The insistence of the Shiite Islamic understanding on ritual and pilgrimage and the solidarity built with the consciousness based on it is a well-known phenomenon. In the Covid-19 pandemic, the risk of contamination caused by the mobility of the pilgrims coming to the city of Qom from countries such as Pakistan was discussed, as well as the contact and social distance during the Hajj. Two cases were handled with a qualitative design including case analysis method and descriptive analysis technique. The main purpose and intended contribution of the article are to reveal the insistence of face-to-face interaction through the performative based on rituals as an analytical explanatory on the social level. Inferences based on the analysis of religious groups’ response to restrictions in times of emergency build the importance of the issue. Case information identified in the context of the case study shows that groups with traditional interaction forms insist on their own practice in the field of uncertainty and initiative. Unless necessity eliminates the initiative, the determination of this insistence of traditional religious groups is not only theological but also ontological confirmation. It is understood that the members of the group took the initiative to represent their reputation within the group and their extent to dedicate to the group. The fact that Tablīghī Djamā‘at, the largest group of the transnational Islamic da‘wa (call), did not give up on a method based on demographic mobility, has been a matter of discussion. The insistence of the group is because of the methods that require every ordinary believer to accept to convey the da‘wa as his primary duty. On the other hand, Shiism, which stands out with its traditional structure and ritual intensity in the Islamic world, insisted on the initiative process in the pilgrimage phenomenon, on which it attached special importance and built its solidarity and identity on this phenomenon. This insistence aims preserving the dynamics and social ontology of the religious group in the performance of the membership system. Essentially, the individual in the group insists on his position in face-to-face interaction because having a role in a group also means having a face. Thus, this insistence performance by religious groups should also be read as a reflection of self-technologies. In the final analysis, the insistence put forward should be considered essentially in relation to the insistence on the position in social ontology. (shrink)

Linguistic communication is an important part of the cross-cultural perspective, and linguistic textual emotion recognition is a key massage in interpersonal communication. Spanish is the second largest language system in the world. The purpose of this paper is to identify the emotional features in Spanish texts. The improved BiLSTM framework is proposed. We select three widely used Spanish dictionaries as the datasets for our experiments, and then we finally obtain text sentiment classification results through text preprocessing, text emotion feature (...) extraction, text topic detection, and emotion classification. We inserted the attention mechanism in the improved BiLSTM framework. It enables the shared feature encoder to obtain weighted representation results in the extraction of emotion features, which enhances the generalization ability of the model for text emotion feature recognition. Experimental results demonstrate that our approach performs better for specialized Spanish dictionary datasets. In terms of emotion recognition accuracy, the average value is as high as 76.21%. The overall performance outperforms current comparable machine learning methods and convolutional neural network methods. (shrink)

To assist in the evaluation process when determining architectures for new robots and intelligent systems equipped with artificial emotions, it is beneficial to understand the systems that have been built previously. Other surveys have classified these systems on the basis of their technological features. In this survey paper, we present a classification system based on a model similar to that used in psychology and philosophy for theories of emotion. This makes possible a connection to thousands of years of (...) discourse on the topic of emotion. Five theories of emotion are described based on an emotion theory model proposed by Power and Dalgleish. The paper provides classifications using a model of 10 new questions, for 14 major research projects that describe implementations or designs for systems that use artificial emotions for either robotics or general artificial intelligence. We also analyze the trends in the usage of various theories and complexity changes over time. (shrink)

The study considers the development of methods for detecting anomalous network connections based on hybridization of computational intelligence methods. An analysis of approaches to detecting anomalies and abuses in computer networks. In the framework of this analysis, a classification of methods for detecting network attacks is proposed. The main results are reduced to the construction of multi-class models that increase the efficiency of the attack detection system, and can be used to build systems for classifying network parameters during (...) the attack. A model of an artificial immune system based on an evolutionary approach, an algorithm for genetic-competitive learning of the Kohonen network and a method of hierarchical hybridization of binary classifiers with the addition to the detection of anomalous network connections have been developed. The architecture of the network distributed attack detection system has been developed. The architecture of the attack detection system is two-tier: the first level provides the primary analysis of individual packets and network connections using signature analysis, the second level processes the processing of aggregate network data streams using adaptive classifiers. A signature analysis was performed to study network performance based on the Aho-Korasik and Boyer-Moore algorithms and their improved analogues were implemented using OpenMP and CUDA technologies. The architecture is presented and the main points of operation of the network attack generator are shown. A system for generating network attacks has been developed. This system consists of two components: an asynchronous transparent proxy server for TCP sessions and a frontend interface for a network attack generator. The results of the experiments confirmed that the functional and non-functional requirements, as well as the requirements for computing intelligent systems, are met for the developed attack detection system. (shrink)

As emphasised in Islamic sources, justice is one of the most important issues covered in the religion of Islam. In fact, justice is a central theme in Islam and has a special value in this regard. Conversation about justice and its nature, as well as its realisation in human communities, has been thus far a necessity in human life. Actually, the establishment and implementation of justice in all areas are crucial for the utopia. Given the importance of this subject, the (...) present study aims to determine and explain the components of the justice-oriented Islamic community based on the teachings of Nahj al-Balaghah. Therefore, the components that introduce justice orientation in the Islamic community are extracted from the sermons, letters and wise sayings (viz. narrations) of Nahj al-Balaghah. Afterward, the extracted components are classified based on the similarity of the themes and concepts. In the end, five classifications are introduced, including distributive justice, procedural justice, interactional justice, social justice and fairness.Contribution: Based on the determined indices, the model of justice-oriented Islamic community is developed. It is thus recommended to pay more attention to the realisation of a justice-oriented Islamic community by researchers and leaders. This is mainly because of the fact that the opposite of a justice-oriented community is one with injustice, where oppression and disrespect for the rights of others occur, divine blessings change, divine wrath and anger are provoked and destruction approaches. (shrink)

Background: Clinical genomic implementation studies pose challenges for informed consent. Consent forms often include complex language and concepts, which can be a barrier to diverse enrollment, and these studies often blur traditional research-clinical boundaries. There is a move toward self-directed, web-based research enrollment, but more evidence is needed about how these enrollment approaches work in practice. In this study, we developed and evaluated a literacy-focused, web-based consent approach to support enrollment of diverse participants in an ongoing clinical (...) genomic implementation study. Methods: As part of the Cancer Health Assessments Reaching Many (CHARM) study, we developed a web-based consent approach that featured plain language, multimedia, and separate descriptions of clinical care and research activities. CHARM offered clinical exome sequencing to individuals at high risk of hereditary cancer. We interviewed CHARM participants about their reactions to the consent approach. We audio recorded, transcribed, and coded interviews using a deductively and inductively derived codebook. We reviewed coded excerpts as a team to identify overarching themes. Results: We conducted 32 interviews, including 12 (38%) in Spanish. Most (69%) enrolled without assistance from study staff, usually on a mobile phone. Those who completed enrollment in one day spent an average of 12 minutes on the consent portion. Interviewees found the information simple to read but comprehensive, were neutral to positive about the multimedia support, and identified increased access to testing in the study as the key difference from clinical care. Conclusions: This study showed that interviewees found our literacy-focused, web-based consent approach acceptable; did not distinguish the consent materials from other online study processes; and valued getting access to testing in the study. Overall, conducting empirical bioethics research in an ongoing clinical trial was useful to demonstrate the acceptability of our novel consent approach but posed practical challenges. (shrink)