Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential mistakes. We demon­strate the methodology by outlining the different types of mistakes that are found when the algorithms are applied to SNOMED-CT. On the basis of the results, we argue that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.

“ A man orders a whole pizza pie for himself and is asked whether he would like it cut into eight or four slices. He responds, ‘Four, I’m on a diet ”’ (Noël Carroll) -/- While not hilarious --so funny that it induces chortling punctuated with outrageous vomiting--this little gem is amusing. We recognize that something has gone wrong. On a first reading it might not compute, something doesn’t quite make sense. Then, aha! , we understand the hapless dieter has (...) misapplied general rules of thumb, mental short-cuts, or heuristics, that we were also initially committed to and that would usually be good enough to rely upon— fewer slices equals fewer calories; diets require fewer calories, etc—but in this particular case they fail, and the feeling of mirth is our reward for making this discovery. We don’t say all of that after a punchline, of course, but that’s what is happening according to the Humor as Error-Detection Theory : our sense of humor can sense our errors. This chapter will focus on the overlap and benefits of a humorous and philosophical attitude toward the world and our place in it. The historian of philosophy Will Durant tells us that genuine philosophy begins when one learns to doubt; we can say something similar with humor--trust me. (shrink)

The results are interpreted in terms of a deficit in initial rule learning and subsequent generalization of these rules to new stimuli. Negative feedback is adequately processed at a neural level but this information is not used to improve behaviour on subsequent trials. As learning is degraded, the process of error detection at the moment of the actual response is diminished. Therefore, the current study demonstrates that disturbed error-monitoring processes play a central role in the often reported (...) learning deficits in individuals with PP. (shrink)

We present the details of a methodology for quality assurance in large medical terminologies and describe three algorithms that can help terminology developers and users to identify potential mistakes. The methodology is based in part on linguistic criteria and in part on logical and ontological principles governing sound classifications. We conclude by outlining the results of applying the methodology in the form of a taxonomy different types of errors and potential errors detected in SNOMED-CT.

A fundamental assumption of theories of decision-making is that we detect mismatches between intention and outcome, adjust our behavior in the face of error, and adapt to changing circumstances. Is this always the case? We investigated the relation between intention, choice, and introspection. Participants made choices between presented face pairs on the basis of attractiveness, while we covertly manipulated the relationship between choice and outcome that they experienced. Participants failed to notice conspicuous mismatches between their intended choice and the (...) outcome they were presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. We call this effect choice blindness. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this (...) end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the (...) QE is akin to functional characteristics of a specific class of retinal ganglion cells (the so-called Y-cells) in the visual systems of the primate and the cat, the sensitivity of the QE surpasses the capacity limits of human visual detection. Here, the quantization error in the SOM is found to reliably signal changes in contrast or colour when contrast information is removed from or added to the image, but not when the amount and relative weight of contrast information is constant and only the local spatial position of contrast elements in the pattern changes. While the RGB Mean reflects coarser changes in colour or contrast well enough, the SOM-QE is shown to outperform the RGB Mean in the detection of single-pixel changes in images with up to five million pixels. This could have important implications in the context of unsupervised image learning and computational building block approaches to large sets of image data (big data). (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest (...) Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in dvance regarding: on-going construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. In (...) our proposed system we propose a new forecasting method that uses a Convolutional Neural Network monthly rainfall for a selected location. In our proposed system we are going to forecast the rainfall result based on the mean square error, mean absolute error and root mean square error, which we get in train and test of the dataset based deep learning technique. (shrink)

From the geometric formulation of gravity, according to the Einstein-Grosmann-Hilbert equations, of November 1915, as the geodesic movement in the semirimennian manifold of positive curvature, spacetime, where due to absence of symmetries, the conservation of energy-impulse is not possible taking together the material processes and that of the gravitational geometric field, however, given those symmetries in the flat Minkowski spacetime, using the De Sitter model, Einstein linearizing gravitation, of course, really in the absence of gravity, in 1916, purged of some (...) mathematical errors in 1918, he introduced "gravitational waves" as disturbances in the curvature of space, and in the absence of knowing physically what spacetime is and philosophically in dispute, that previously in 1936 and definitively in 1937, Einstein showed they did not exist. It was through the works arising from the dynamics of academic discourse, from the perspective not of Einstein but of Weyl, that Bondi, Pirani, Robinson and Trautman, in the 1950s, after Einstein's death, "gravitational waves" were reintroduced and led to experimental search. In 2002, from Sergei Kopeikin's VLBI experiment, its supposed speed was established, without obtaining unanimous recognition from the community of scientists but rather dividing them. And it was in February 2016 that the aLIGO-aVirgo collaboration announced that they had detected them for the first time. In this work, the history that led to this supposed discovery is presented and it is stated that the waves detected are really from the quantum vacuum in which everything that exists is immersed, the author's thesis exposed immediately in response to that 2016 announcement. (shrink)

From the geometric formulation of gravity, according to the Einstein-Grosmann-Hilbert equations, of November 1915, as the geodesic movement in the semirimennian manifold of positive curvature, spacetime, where due to absence of symmetries, the conservation of energy-impulse is not possible taking together the material processes and that of the gravitational geometric field, however, given those symmetries in the flat Minkowski spacetime, using the De Sitter model, Einstein linearizing gravitation, of course, really in the absence of gravity, in 1916, purged of some (...) mathematical errors in 1918, he introduced "gravitational waves" as disturbances in the curvature of space, and in the absence of knowing physically what spacetime is and philosophically in dispute, that previously in 1936 and definitively in 1937, Einstein showed they did not exist. It was through the works arising from the dynamics of academic discourse, from the perspective not of Einstein but of Weyl, that Bondi, Pirani, Robinson and Trautman, in the 1950s, after Einstein's death, "gravitational waves" were reintroduced and led to experimental search. In 2002, from Sergei Kopeikin's VLBI experiment, its supposed speed was established, without obtaining unanimous recognition from the community of scientists but rather dividing them. And it was in February 2016 that the aLIGO-aVirgo collaboration announced that they had detected them for the first time. In this work, the history that led to this supposed discovery is presented and it is stated that the waves detected are really from the quantum vacuum in which everything that exists is immersed, the author's thesis exposed immediately in response to that 2016 announcement. (shrink)

McTaggart, in his famous paper, “The Unreality of Time” (1908), argues in favor of the sceptical claim that time is unreal. His main argument is based on detecting a paradox in our ordinary descriptions of time or events occurring in time. Based on our common sense conception of time, time and the events happening in it can be described in two ways: either as having the properties of “being past”, “being present” and “being future”, or as having the properties of (...) “being earlier than”, “being later than”, or “being simultaneous with”. McTaggart argues that employing the second sort of properties fails to properly explain “change” in time. However, having assumed the essentiality of the first type of properties to time, McTaggart argues that these properties will themselves lead to a paradox, according to which all events are at the same time in the past, present, and future. In this essay, we are going to provide a clear exposition of McTaggart’s argument and briefly review some of the main responses to it. We will then show that McTaggart’s argument will amount to error-theory about the content of our utterances about time. We will then employ Boghossian’s argument against error-theory (1990) to show why McTaggart’s argument leads to paradoxical conclusions. (shrink)

Inner speech is commonly understood as the conscious experience of a voice within the mind. One recurrent theme in the scientific literature is that the phenomenon involves a representation of overt speech, for example, a representation of phonetic properties that result from a copy of speech instructions that were ultimately suppressed. I propose a larger picture that involves some embodied objects and their misperception. I call it “the Body Error Theory,” or BET for short. BET is a form of (...) illusionism, but the particular version I favor is a cross-modal illusion. Newly described here, my hypothesis is that the experience of inner speech arises from a mix of interoception and audition. Specifically, there is the detection of slight but well-confirmed activities in the speech musculature that occur during inner speech, which helps to transform representations of normal but quiet nonverbal sounds that inevitably occur during inner speech, from breathing to background noise, into a mistaken perception of inner speech. Simply put, activities in the speech musculature mix with sounds to create the appearance of speech sounds, which thus explains the “voice within the mind.” I also show how BET’s cross-modal system fits with standard information processing accounts for speech monitoring and how it accommodates the central insights of leading theories of inner speech. In addition, I show how BET is supported by data from experience-sampling surveys and how it can be empirically tested against its rivals. (shrink)

McTaggart, in his famous paper, “The Unreality of Time” (1908), argues in favor of the sceptical claim that time is unreal. His main argument is based on detecting a paradox in our ordinary descriptions of time or events occurring in time. Based on our common sense conception of time, time and the events happening in it can be described in two ways: either as having the properties of “being past”, “being present” and “being future”, or as having the properties of (...) “being earlier than”, “being later than”, or “being simultaneous with”. McTaggart argues that employing the second sort of properties fails to properly explain “change” in time. However, having assumed the essentiality of the first type of properties to time, McTaggart argues that these properties will themselves lead to a paradox, according to which all events are at the same time in the past, present, and future. In this essay, we are going to provide a clear exposition of McTaggart’s argument and briefly review some of the main responses to it. We will then show that McTaggart’s argument will amount to error-theory about the content of our utterances about time. We will then employ Boghossian’s argument against error-theory (1990) to show why McTaggart’s argument leads to paradoxical conclusions. (shrink)

Misinformation can have significant societal consequences. For example, misinformation about climate change has confused the public and stalled support for mitigation policies. When people lack the expertise and skill to evaluate the science behind a claim, they typically rely on heuristics such as substituting judgment about something complex (i.e. climate science) with judgment about something simple (i.e. the character of people who speak about climate science) and are therefore vulnerable to misleading information. Inoculation theory offers one approach to effectively neutralize (...) the influence of misinformation. Typically, inoculations convey resistance by providing people with information that counters misinformation. In contrast, we propose inoculating against misinformation by explaining the fallacious reasoning within misleading denialist claims. We offer a strategy based on critical thinking methods to analyse and detect poor reasoning within denialist claims. This strategy includes detailing argument structure, determining the truth of the premises, and checking for validity, hidden premises, or ambiguous language. Focusing on argument structure also facilitates the identification of reasoning fallacies by locating them in the reasoning process. Because this reason-based form of inoculation is based on general critical thinking methods, it offers the distinct advantage of being accessible to those who lack expertise in climate science. We applied this approach to 42 common denialist claims and find that they all demonstrate fallacious reasoning and fail to refute the scientific consensus regarding anthropogenic global warming. This comprehensive deconstruction and refutation of the most common denialist claims about climate change is designed to act as a resource for communicators and educators who teach climate science and/or critical thinking. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques such (...) as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration. (shrink)

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, (...) in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Must philosophers incorporate tools of experimental science into their methodological toolbox? I argue here that they must. Tallying up all the resources that are now part of standard practice in analytic philosophy, we see the problem that they do not include adequate resources for detecting and correcting for their own biases and proclivities towards error. Methodologically sufficient resources for error- detection and error-correction can only come, in part, from the deployment of specific methods from the sciences. (...) However, we need not imagine that the resulting methodological norms will be so empirically demanding as to require that all appeals to intuition must first be precertified by a thorough vetting by teams of scientists. Rather, I sketch a set of more moderate methodological norms for how we might best include these necessary tools of experimental philosophy. (shrink)

There is a widespread attitude in epistemology that, if you know on the basis of perception, then you couldn't have been wrong as a matter of chance. Despite the apparent intuitive plausibility of this attitude, which I'll refer to here as “stochastic infallibilism”, it fundamentally misunderstands the way that human perceptual systems actually work. Perhaps the most important lesson of signal detection theory (SDT) is that our percepts are inherently subject to random error, and here I'll highlight some (...) key empirical research that underscores this point. In doing so, it becomes clear that we are in fact quite willing to attribute knowledge to S that p even when S's perceptual belief that p could have been randomly false. In short, perceptual processes can randomly fail, and perceptual knowledge is stochastically fallible. The narrow implication here is that any epistemological account that entails stochastic infallibilism, like safety, is simply untenable. More broadly, this myth of stochastic infallibilism provides a valuable illustration of the importance of integrating empirical findings into epistemological thinking. (shrink)

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

Deduction is important to scientific inquiry because it can extend knowledge efficiently, bypassing the need to investigate everything directly. The existence of closure failure—where one knows the premises and that the premises imply the conclusion but nevertheless does not know the conclusion—is a problem because it threatens this usage. It means that we cannot trust deduction for gaining new knowledge unless we can identify such cases ahead of time so as to avoid them. For philosophically engineered examples we have “inner (...) alarm bells” to detect closure failure, but in scientific investigation we would want to use deduction for extension of our knowledge to matters we don’t already know that we couldn’t know. Through a quantitative treatment of how fast probabilistic sensitivity is lost over steps of deduction, I identify a condition that guarantees that the growth of potential error will be gradual; thus, dramatic closure failure is avoided. Whether the condition is fulfilled is often obvious, but sometimes it requires substantive investigation. I illustrate that not only safe deduction but the discovery of dramatic closure failures can lead to scientific advances. (shrink)

Traditionally it has been thought that the moral valence of a proposition is, strictly speaking, irrelevant to whether someone knows that the proposition is true, and thus irrelevant to the truth-value of a knowledge ascription. On this view, it’s no easier to know, for example, that a bad thing will happen than that a good thing will happen (other things being equal). But a series of very surprising recent experiments suggest that this is actually not how we view knowledge. On (...) the contrary, people are much more willing to ascribe knowledge of a bad outcome. This is known as the epistemic side-effect effect (ESEE), and is a specific instance of a widely documented phenomenon, the side-effect effect (a.k.a. “the Knobe effect”), which is the most famous finding in experimental philosophy. In this paper, I report a new series of five experiments on ESEE, and in the process accomplish three things. First, I confirm earlier findings on the effect. Second, I show that the effect is virtually unlimited. Third, I introduce a new technique for detecting the effect, which potentially enhances its theoretical significance. In particular, my findings make it more likely that the effect genuinely reflects the way we think about and ascribe knowledge, rather than being the result of a performance error. (shrink)

Since anonymous agents can spread misinformation with impunity, many people advocate for greater accountability for internet speech. This paper provides a veritistic argument that accountability mechanisms can cause significant epistemic problems for internet encyclopedias and social media communities. I show that accountability mechanisms can undermine both the dissemination of true beliefs and the detection of error. Drawing on social psychology and behavioral economics, I suggest alternative mechanisms for increasing the trustworthiness of internet communication.

An important theme running through D.H. Mellor’s work is his realism, or as I shall call it, his objectivism: the idea that reality as such is how it is, regardless of the way we represent it, and that philosophical error often arises from confusing aspects of our subjective representation of the world with aspects of the world itself. Thus central to Mellor’s work on time has been the claim that the temporal A-series (previously called ‘tense’) is unreal while the (...) B-series (the series of ‘dates’) is real. The A-series is something which is a product of our representation of the world, but not a feature of reality itself. And in other, less central, areas of his work, this kind of theme has been repeated: ‘Objective decision making’ (1980) argues that the right way to understand decision theory is as a theory of what is the objectively correct decision, the one that will actually as a matter of fact achieve your intended goal, rather than the one that is justified purely in terms of what you believe, regardless of whether the belief is true or false. ‘I and now’ (1989) argues against a substantial subjective conception of the self, using analogies between subjective and objective ways of thinking about time and subjective and objective ways of thinking about the self. And in the paper which shall be the focus of my attention here, ‘Nothing like experience’ (1992), Mellor contests arguments which try and derive anti-physicalist conclusions from reflections on the subjective character of experience. A common injunction is detectable: when doing metaphysics, keep the subjective where it belongs: inside the subject’s representation of the world. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying (...) the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an (...) impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Management of information is facilitated by unambiguously tracking portions of reality over time. To track the portions of reality, a referent tracking system is used. The referent tracking system is able to communicate with other tracking systems and/or tradition information systems. Errors in the referent tracking system are detected and corrected to maintain actual representations of the portions of reality.

When must a specific cognitive habit be called upon to solve a problem? In the subject’s learning process, “knowing-to” is connected with a conscious particular judgment of truth or “aha” moment enacting a new behavioral schema. This paper comments on recent experiments supporting the view that a shift from automatic to controlled forms of inhibition, involving conscious attention, is crucial for detecting errors and activating a new strategy in complex cognitive situations. The part that consciousness plays in this process agrees (...) with its philosophical description as “judge of truth”, and can thus be regarded as an essential precursor to the development of higher cognitive habits. In this regard, John Henry Newman’s explanation of human assent to truth, for which our consciousness of self is always prior, proves to be decisive. (shrink)

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and (...) HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average (...) error of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

Normative political theorists frequently compare hypothetical scenarios for the purpose of identifying reasons to prefer one kind of institution to alternatives. We examine three types of "unfair" comparisons and the reasoning errors associated with each. A theorist makes an _obscure comparison_ when one (or more) of the alternatives under consideration is underspecified; a theorist makes a _mismatched comparison_ when they fail to hold fixed the relevant contextual factors while comparing alternatives; and a theorist makes an _irrelevant comparison_ when they compare (...) alternatives assuming contextual factors that differ in important respects from those they "should" assume given their theoretical aims. We then introduce the notion of a modeling mindset and show how this mindset can help theorists detect and avoid the three types of error. We conclude with a reconstruction of Cohen's (2009) camping trip thought experiment to illustrate the approach. (shrink)

: According to Kant, the arguments of rational psychology are formal fallacies that he calls transcendental paralogisms. It remains heavily debated whether there actually is any formal error in the inferences Kant presents: according to Grier and Allison, they are deductively invalid syllogisms, whereas Bennett, Ameriks, and Van Cleve deny that they are formal fallacies. I advance an interpretation that reconciles these extremes: transcendental paralogisms are sound in general logic but constitute formal fallacies in transcendental logic. By formalising the (...) paralogistic inference, I will pinpoint the error as an illegitimate existential presupposition. Since - unlike transcendental logic - general logic abstracts from all objects, this error can only be detected in transcendental logic. (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our (...) investigation identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

Some contemporary philosophers suggest that we know just by introspection that folk psychological states exist. However, such an "armchair refutation" of eliminativism seems too easy. I first attack two strategems, inspired by Descartes, on how such a refutation might proceed. However, I concede that the Cartesian intuition that we have direct knowledge of representational states is very powerful. The rest of this paper then offers an error theory of how that intuition might really be mistaken. The idea is that (...) introspection does not detect any folk psychological states, but rather detects neural states that dispose us to certain sorts of linguistic behavior. Briefly, rather than detecting a mental appearance of the state-of-affairs that I am sitting near the fire, introspection detects a state disposing me to assert "It appears to me that I am sitting by the fire." The nature of this linguistic-dispositional state is then given a connectionist underpinning, where inputs from introspection lead to such assertive behavior. The Cartesian certainty we feel in self-ascribing such appearances is also explained. Even though I do not endorse such an error theory outright, I suggest that its tenability is enough to undercut introspection arguments against eliminativism. (shrink)

The paper discusses possible roles of consciousness in a semiotic activity of a cognitive agent. The discussion, we claim, is based on two related approaches to consciousness: on Chalmers’ theory of phenomenal and psychological consciousness and on Damasio’s neural theory, which draws a distinction between core and extended consciousness. Two stages of cognitive-semiotic processing are discussed: the moment of perception of a sign as a meaningful entity and the metasemiotic processes understood as the human capacity to reflect on signs and (...) their usage, analyse and control processes of recognition, interpretation of signs and to detect and correct errors in semiotic activity. In the case of the first stage, it is argued that signs as meaningful entities have a distincly experiential character. The feeling of meaningfulness is a result of phenomenal consciousness, in particular a result of the so-called valuation features of phenomenal experience. I claim that this aspect of cognitive-semiotic activity is possible owing to a special neural mechanism called a semiotic marker. It is argued that semiotic systems have to be able to use signs as signs, i.e. they should display some metacognitive capacities, in particular an ability to analyse semiosis at a metalevel. It is argued that such metasemiosis is dependent on psychological consciousness and is realized at the neural level in the form of extended consciousness. The paper is based on a particular understanding of cognitive semiotics as a discipline involving analyses of cognitive processes as semiotic processes, i.e. processes requiring usage of signs. (shrink)

Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are better (...) aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field. (shrink)

Recent advances in neuroimaging technologies have rendered multimodal analysis of operators’ cognitive processes in complex task settings and environments increasingly more practical. In this exploratory study, we utilized optical brain imaging and mobile eye tracking technologies to investigate the behavioral and neurophysiological differences among expert and novice operators while they operated a human-machine interface in normal and adverse conditions. In congruence with related work, we observed that experts tended to have lower prefrontal oxygenation and exhibit gaze patterns that are better (...) aligned with the optimal task sequence with shorter fixation durations as compared to novices. These trends reached statistical significance only in the adverse condition where the operators were prompted with an unexpected error message. Comparisons between hemodynamic and gaze measures before and after the error message indicated that experts’ neurophysiological response to the error involved a systematic increase in bilateral dorsolateral prefrontal cortex (dlPFC) activity accompanied with an increase in fixation durations, which suggests a shift in their attentional state, possibly from routine process execution to problem detection and resolution. The novices’ response was not as strong as that of experts, including a slight increase only in the left dlPFC with a decreasing trend in fixation durations, which is indicative of visual search behavior for possible cues to make sense of the unanticipated situation. A linear discriminant analysis model capitalizing on the covariance structure among hemodynamic and eye movement measures could distinguish experts from novices with 91% accuracy. Despite the small sample size, the performance of the linear discriminant analysis combining eye fixation and dorsolateral oxygenation measures before and after an unexpected event suggests that multimodal approaches may be fruitful for distinguishing novice and expert performance in similar neuroergonomic applications in the field. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A (...) neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Article Authors Metrics Comments Media Coverage Abstract Author Summary Introduction Results Discussion Supporting information Acknowledgments Author Contributions References Reader Comments (0) Media Coverage (0) Figures Abstract During language processing, humans form complex embedded representations from sequential inputs. Here, we ask whether a “geometrical language” with recursive embedding also underlies the human ability to encode sequences of spatial locations. We introduce a novel paradigm in which subjects are exposed to a sequence of spatial locations on an octagon, and are asked to (...) predict future locations. The sequences vary in complexity according to a well-defined language comprising elementary primitives and recursive rules. A detailed analysis of error patterns indicates that primitives of symmetry and rotation are spontaneously detected and used by adults, preschoolers, and adult members of an indigene group in the Amazon, the Munduruku, who have a restricted numerical and geometrical lexicon and limited access to schooling. Furthermore, subjects readily combine these geometrical primitives into hierarchically organized expressions. By evaluating a large set of such combinations, we obtained a first view of the language needed to account for the representation of visuospatial sequences in humans, and conclude that they encode visuospatial sequences by minimizing the complexity of the structured expressions that capture them. (shrink)

In this paper I defend what I call the argument from epistemic reasons against the moral error theory. I argue that the moral error theory entails that there are no epistemic reasons for belief and that this is bad news for the moral error theory since, if there are no epistemic reasons for belief, no one knows anything. If no one knows anything, then no one knows that there is thought when they are thinking, and no one (...) knows that they do not know everything. And it could not be the case that we do not know that there is thought when we believe that there is thought and that we do not know that we do not know everything. I address several objections to the claim that the moral error theory entails that there are no epistemic reasons for belief. It might seem that arguing against the error theory on the grounds that it entails that no one knows anything is just providing a Moorean argument against the moral error theory. I show that even if my argument against the error theory is indeed a Moorean one, it avoids Streumer's, McPherson's and Olson's objections to previous Moorean arguments against the error theory and is a more powerful argument against the error theory than Moore's argument against external world skepticism is against external world skepticism. (shrink)

Moral error theory of the kind defended by J. L. Mackie and Richard Joyce is premised on two claims: (1) that moral judgements essentially presuppose that moral value has absolute authority, and (2) that this presupposition is false, because nothing has absolute authority. This paper accepts (2) but rejects (1). It is argued first that (1) is not the best explanation of the evidence from moral practice, and second that even if it were, the error theory would still (...) be mistaken, because the assumption does not contaminate the meaning or truth-conditions of moral claims. These are determined by the essential application conditions for moral concepts, which are relational rather than absolute. An analogy is drawn between moral judgements and motion judgements. (shrink)

Five aspects of visual change detection are reviewed. The first concerns the concept of change itself, in particular the ways it differs from the related notions of motion and difference. The second involves the various methodological approaches that have been developed to study change detection; it is shown that under a variety of conditions observers are often unable to see large changes directly in their field of view. Next, it is argued that this “change blindness” indicates that focused (...) attention is needed to detect change, and that this can help map out the nature of visual attention. The fourth aspect concerns how these results affect our understanding of visual perception—for example, the implication that a sparse, dynamic representation underlies much of our visual experience. Finally, a brief discussion is presented concerning the limits to our current understanding of change detection. (shrink)

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

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

Moral error theories claim that (i) moral utterances express moral beliefs, that (ii) moral beliefs ascribe moral properties, and that (iii) moral properties are not instantiated. Thus, according to these views, there seems to be conclusive evidence against the truth of our ordinary moral beliefs. Furthermore, many error theorists claim that, even if we accepted moral error theory, we could still in principle keep our first-order moral beliefs. This chapter argues that this last claim makes many popular (...) versions of the moral error theory incompatible with the standard philosophical accounts of beliefs. Functionalism, normative theories of beliefs, representationalism, and interpretationalism all entail that being sensitive to thoughts about evidence is a constitutive feature of beliefs. Given that many moral error theorists deny that moral beliefs have this quality, their views are in a direct conflict with the most popular views about the nature of beliefs. (shrink)

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. (...) Moreover, the visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

Recently, companions in guilt strategies have garnered significant philosophical attention as a response to arguments for moral error theory, the view that there are no moral facts and that our moral beliefs are thus systematically mistaken. According to Cuneo (The normative web: an argument for moral realism, Oxford University Press, Oxford, 2007), Das (Philos Q 66:152–160, 2016; Australas J Philos 95(1):58–69, 2017), Rowland (J Ethics Soc Philos 7(1):1–24, 2012; Philos Q 66:161–171, 2016) and others, epistemic facts would be just (...) as metaphysically problematic (or ‘guilty’) as moral facts. But since epistemic error theory is implausible, arguments for moral error theory prove too much and should be rejected. My aim is to argue that the success of this strategy is limited. In particular, the companions in guilt response fails against error-theoretic arguments motivated by concerns about explanatory dispensability, as recently developed by Joyce (The evolution of morality, MIT press, Bradford, 2005) and Olson (Moral error theory: history, critique, defence, Oxford University Press, Oxford, 2014: Ch. 7). To succeed, the response would require a prima facie plausible argument to the effect that epistemic facts are metaphysically dubious because they, too, are explanatorily dispensable. But, as I show, any such argument proves self-effacing: its premise commits us to believing in epistemic facts, while its conclusion forces us to deny their existence. Consequently, companions in guilt strategies don’t offer a panacea against arguments for moral error theory. (shrink)