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)

Analyses of factual causation face perennial problems, including preemption, overdetermination, and omissions. Arguably, the thorniest, are cases of omissive overdetermination, involving two independent omissions, each sufficient for the harm, and neither, independently, making a difference. A famous example is Saunders, where pedestrian was hit by a driver of a rental car who never pressed on the (unbeknownst to the driver) defective (and, negligently, never inspected) brakes. Causal intuitions in such cases are messy, reflected in disagreement about which omission mattered. (...) What these analyses mistakenly take for granted, is that at issue is the 'efficacy' of each omission. I argue, on the contrary, the puzzle of omissive overdetermination favors taking the act/omission distinction seriously. Factual causation, properly understood precludes omissions (i.e. omissions are not causal). Of course, the law also attaches liability to omissions, but this works differently from liability for real causes (e.g. omissions have a duty requirement, they also respond differentially to difference-making considerations). The manner in which liability attaches for omissions differs from that of straightforward causal liability, and is entirely dependent on the underlying causal structure. Attention to that structure (e.g. that the driver's hitting the pedestrian with his car is what actually caused the injury) sheds light on which omissions matter (e.g. driver's failure to press on the brakes) and why (because that failure removes a defense the driver would have to liability for the accident he caused). Other cases, where the parties' connection is entirely omissive (e.g. two physicians fail to detect independently lethal conditions), come out differently (tracking moralized elements). The analysis offered makes better sense of both why omissive determination cases are puzzling and how to resolve them. (shrink)

The aim of this paper is to make a comparison between different streets typology (with a distribution from center to suburb) specified in Tirana, taking into account the pedestrians' behavior on these streets. This paper will primarily focus on the following streets: “Myslym Shyri” street, “Bllok” area, “Kombinat” area, (an extension of Kavaja’s Street), and also “Ana Komnena” street (former “Fusha e Aviacionit”). The pedestrian actions, likeness, dissatisfaction, walking distance while transferring to another zone, greenery effects on pedestrians, time (...) spent, points of interest, level of frequency, pavement quality, the favorite time for walking, meeting points, benches usage, ambulant sellers, vegetation and greenery impact, water basins, public transport preference, and road signs are some of the topics to be addressed in this paper. Determination of these data will be made by employing a questionnaire and direct observation. The study should provide recommendations as guidance for municipalities and stakeholders to better understand the behavior of pedestrians in Tirana. The “Bllok” area and “Myslym Shyri” Street are the most frequented streets by pedestrians. “Myslym Shyri” street has a slight edge over the “Bllok” area and is populated by different categories of pedestrians involving children, youth, middle ages, and the elderly. Ana Komnena Street (former "Fusha e Aviacionit") is not lacking any urban components. Meanwhile, the pedestrians on Kombinati’s street suffer a little bit more because of the traffic jam and vehicle pollution. (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)

Introspection is a fundamental part of our mental lives. Nevertheless, its reliability and its underlying cognitive architecture have been widely disputed. Here, I propose a principled way to model introspection. By using time-tested principles from signal detection theory (SDT) and extrapolating them from perception to introspection, I offer a new framework for an introspective signal detection theory (iSDT). In SDT, the reliability of perceptual judgments is a function of the strength of an internal perceptual response (signal- to-noise ratio) (...) which is, to a large extent, driven by the intensity of the stimulus. In parallel to perception, iSDT models the reliability of introspective judgments as a function of the strength of an internal introspective response (signal-to-noise ratio) which is, to a large extent, driven by the intensity of conscious experiences. Thus, by modelling introspection after perception, iSDT can calibrate introspection’s reliability across a whole range of contexts. iSDT offers a novel, illuminating way of thinking about introspection and the cognitive processes that support it. (shrink)

This article demonstrates a novel conceptualization of sublimity: the sublime in the pedestrian. This pedestrian mode of sublimity is exemplified by the Biblical Abraham, the central figure of Kierkegaard’s pseudonymous Fear and Trembling. It is rooted in the analysis of one of the foundational stories of the three monotheistic religions: Abraham’s averted sacrifice of his son Isaac. The defining feature of this new, pedestrian mode of sublimity is that is remains hidden behind what I call a total (...) incognito. It is similar to the classical ‘elevated mode of sublimity’ as developed by Burke, Kant, and Schiller insofar as it denotes two contrasting feelings at once: repulsion and attraction. It is different insofar as Kierkegaard’s pedestrian mode of sublimity remains hidden from view and can only be shown indirectly. This article expounds the new, pedestrian mode of sublimity by investigating the relation between the incognito and the sublime in Fear and Trembling. It achieves that goal by engaging three perspectives: (1) the sublime failure that comes to the fore in the incognitos of an imaginary Abraham; (2) the ‘fear without being afraid’ that is invoked by God’s incognitos; (3) the total incognito of the pedestrian which conceals Abraham’s sublimity. (shrink)

The purpose of this article is to compare several street typologies (distributed from center to suburb) in Tirana, Albania while taking into consideration pedestrian behavior on these pathways. The streets "Myslym Shyri," "Bllok," "Kombinat" (an extension of Kavaja's Street), and "Ana Komnena" (formerly "Fusha e Aviacionit") will be the primary focus of this study. The following variables will be taken into account such as pedestrian behaviors, street identity, dissatisfaction, walking distance when shifting to another zone, effects of greenery (...) on pedestrians, time spent, points of interest, frequency, pavement quality, preferred walking periods, gathering places, utilization of benches, ambulant sellers, impact of vegetation and greenery, water basins, preference for public transportation, and road signs. Direct observations and a questionnaire will be used to determine the accurate information. The study should provide recommendations as guidance for municipalities and stakeholders to better understand the behavior of pedestrians in Tirana’s context. The streets that pedestrians frequent the most include the "Bllok" area and "Myslym Shyri" Street. The pedestrian resident population on "Myslym Shyri" street, which is dominated by people of all ages, including children, youth, and the elderly, has a slight advantage over the "Bllok" neighborhood. Furthermore, there is a wide variety of urban elements on Ana Komnena Street (formerly known as "Fusha e Aviacionit"). Meanwhile, the pedestrians on Kombinati’s street suffer a little bit more because of the traffic congestion and vehicle pollution. (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)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and reacts like humans, some of the computer activities with artificial intelligence are designed to include speech, recognition, learning, planning and problem solving. Deep learning is a collection of algorithms used in machine learning, it is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is used as a (...) technique to produce brain tumor detection and classification models using Magnetic Resonance Imaging (MRI) imaging for rapid and easy detection and identification of brain tumor. In this thesis, some ways and mechanisms will be reviewed to use deep learning techniques to produce a model for brain tumor detection. The goal is to find a good and effective way to detect brain tumor based on MRI to help the brain doctor in making decisions easily, accurately and rapidly. A recent report by the World Health Organization in February 2018 showed that the death rate from brain cancer or central nervous system (CNS) is the highest in the Asian continent. It is important to detect cancer early so that many of these lives can be saved. The model has been designed and implemented, including a dataset which consist of 10,000 images for brain tumor detection through the use of Deep learning algorithms based on neural networks. For testing, we have used our model, Inception, VGG16, MobileNet and ResNet models. The f-score accuracy we got for each model was as follows: Our model was 98.28, VGG16 was 99.86%, ResNet50 was 98.14%, MobileNet was 88,98%, and InceptionV3 was 99.88%. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...) prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with (...) the support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

Today’s everyone normal life can include a normal rate of playing computer games or video games; but what about an excessive or compulsive use of video games that impact on our life? Our kids, who usually spend a lot of time in playing video games will likely have a trouble in paying attention to their school lessons. In this paper, we introduce an expert system to help users in getting the correct diagnosis of the health problem of video game addictions (...) that range from (Musculoskeletal issues, Vision problems and Obesity). Moreover, this expert system provides information about the problem and tell us how we can solve it. SL5 Object expert system language was used to design and implement the expert system. (shrink)

The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...) in True Detective resembles the paths of Buddhism and pessimism, even though he is ethical in a strictly non-pessimistic and non-Buddhist fashion. Last, I will try to place these findings in perspective through the French philosopher Gilles Deleuze’s thoughts. Hereby, I hope to illustrate that joy, not suffering, is basic to human existence, and how human beings may overcome a spiritual pessimism. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

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.

The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...) in True Detective resembles the paths of Buddhism and pessimism, even though he is ethical in a strictly non-pessimistic and non-Buddhist fashion. Last, I will try to place these findings in perspective through the French philosopher Gilles Deleuze’s thoughts. Hereby, I hope to illustrate that joy, not suffering, is basic to human existence, and how human beings may overcome a spiritual pessimism. (shrink)

The existence of big bang relic neutrinos—exact analogues of the big bang relic photons comprising the cosmic microwave background radiation—is a basic prediction of standard cosmology. The standard big bang theory predicts the existence of 1087 neutrinos per flavour in the visible universe. This is an enormous abundance unrivalled by any other known form of matter, falling second only to the cosmic microwave background (CMB) photon. Yet, unlike the CMB photon which boasts its first (serendipitous) detection in the 1960s (...) and which has since been observed and its properties measured to a high degree of accuracy in a series of airborne/satellite and ground based experiments, the relic neutrino continues to be elusive in the laboratory. The chief reason for this is of course the feebleness of the weak interaction. At present, the observational evidence for their existence rests entirely on cosmological measurements, such as the light elemental abundances, anisotropies in the cosmic microwave background, and the large-scale matter power spectrum. In this paper we argue that Direct Detection of relic neutrino background is indeed impossible by any means, because of two chief reasons: (a) there was no such thing of cosmic singularity, hence the hot big bang/primeval atom model was based on false premises (quantum birth assumption); (b) the neutrino existence itself is not unquestionable, in particular if we consider a realism view of vector potential, A, in classical electrodynamics. (shrink)

Despite the emphasis on dialogue and argumentation in educational settings, still not much is known about how best we can support learners in their interthinking, reasoning, and metadialogic understanding. The goal of this classroom intervention study is to explore the degree of students’ dialogicity and its possible increase during a learning programme implementing dialogic and argument-based teaching goals and principles. In particular, we focus on how students from 5 to 15 years old engage with each other's ideas, and whether/how this (...) engagement is influenced by lesson and classroom setting factors. The participants were 4208 students distributed in preprimary, primary, and secondary classrooms of five countries (UK, Portugal, Germany, Spain, and Cyprus). Findings suggest that there is a consistent increase with age for some high-dialogicity moves, and students behave more dialogically in whole-class discussions rather than small-group activities. (shrink)

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)

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.

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

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

Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people's intentions. In this paper, (...) Rutherford and Ray's experiment is analyzed and it is concluded that they do not prove that people with autism can understand intentions. Such an experiment only shows that autistic people and general population share common logical-linguistic abilities. (shrink)

The study aimed to identify the effect of applying detection and prevention tools for career fraud in combating and preventing fraud and reducing its risks through an applied study on Palestinian Ministry of Health in Gaza Strip, Palestine. To achieve the objectives of the study, the researchers used the questionnaire as a main tool to collect data, and the descriptive and analytical approach to conducting the study. The study population consisted of (501) supervisory employees working at MOH in Gaza (...) Strip, Palestine. The researchers used the stratified random sample method and the multiple regression method to measure the effect. The study concluded a set of results, the most important of which is a positive evaluation among respondents about the levels of application of tools to detect and prevent job fraud within MOH, and the presence of a high interest in tools to prevent job fraud during the implementation of its various work. The study recommended the necessity for the Palestinian National Authority to develop and approve laws regulating health sector, so that those laws guarantee the prevention of conflicts of interest, especially for the category of doctors, by preventing job duplication of doctors working in the government sector. Also study recommended the need for MOH to adopt principles of health sector governance and begin immediately to implement them. This process to ensure transparency, disclosure and accountability in the business framework in a manner that realizes the effective protection of stakeholders and does not compromise or discriminate in the application of these principles, which ensures that all forms of job fraud are organized in an organized manner and within fixed legal frameworks. (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 mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the dataset (...) collected from Arab countries to study an individual’s mental health and indicative help-seeking pointers based on their responses to specific pre-defined questions in a multicultural society. The proposed model validates the claims mathematically and uses different machine learning classifiers to identify individuals who are either currently or previously diagnosed with depression or demonstrate unintentional “save our souls” (SOS) behaviors for an early prediction to prevent risks of danger in life going forward. The accuracy is measured by comparing with the classifiers using several visualization tools. This analysis provides the claims and authentic sources for further research in the multicultural public medical sector and decision-making rules by the government. (shrink)

Interaction between drivers and pedestrians is often facilitated by informal communicative cues, like hand gestures, facial expressions, and eye contact. In the near future, however, when semi- and fully autonomous vehicles are introduced into the traffic system, drivers will gradually assume the role of mere passengers, who are casually engaged in non-driving-related activities and, therefore, unavailable to participate in traffic interaction. In this novel traffic environment, advanced communication interfaces will need to be developed that inform pedestrians of the current state (...) and future behavior of an autonomous vehicle, in order to maximize safety and efficiency for all road users. The aim of the present review is to provide a comprehensive account of empirical work in the field of external human–machine interfaces for autonomous vehicle-to- pedestrian communication. In the great majority of covered studies, participants clearly benefited from the presence of a communication interface when interacting with an autonomous vehicle. Nevertheless, standardized interface evaluation procedures and optimal interface specifications are still lacking. (shrink)

Freud proposes that in unconscious processing, logical connections are also (heavily) based upon phonological similarities. Repressed concerns, for example, would also be expressed by way of phonologic ambiguity. In order to investigate a possible unconscious influence of phonological similarity, 31 participants were submitted to a tachistoscopic subliminal priming experiment, with prime and target presented at 1ms. In the experimental condition, the prime and one of the 2 targets were phonological reversed forms of each other, though graphemically dissimilar (e.g., “nice” and (...) “sign”); in the control condition the targets were pseudo-randomly attributed to primes to which they don’t belong. The experimental task was to “blindly” pick the choice most similar to the prime. ERPs were measured with a focus on the N320, which is known to react selectively to phonological mismatch in supraliminal visual word presentations. The N320 amplitude-effects at the electrodes on the midline and at the left of the brain significantly predicted the participants’ net behavioral choices more than half a second later, while their subjective experience is one of arbitrariness. Moreover, the social desirability score (SDS) significantly correlates with both the behavioral and the N320 brain responses of the participants. It is proposed that in participants with low SDS the phonological target induces an expected reduction of N320 and this increases their probability to pick this target. In contrast, high defensive participants have a perplexed brain reaction upon the phonological target, with a negatively peaking N320 as compared to control and this leads them to avoid this target more often. Social desirability, which is understood as reflecting defensiveness, might also manifest itself as a defense against the (energy-consuming) ambiguity of language. The specificity of this study is that all of this is happening totally out of awareness and at the level of very elementary linguistic distinctions. (shrink)

When brief blank fields are placed between alternating displays of an original and a modified scene, a striking failure of perception is induced: the changes become extremely difficult to notice, even when they are large, presented repeatedly, and the observer expects them to occur (Rensink, O'Regan, & Clark, 1997). To determine the mechanisms behind this induced "change blindness", four experiments examine its dependence on initial preview and on the nature of the interruptions used. Results support the proposal that representations at (...) the early stages of visual processing are highly volatile, and that focused attention is needed to stabilize them sufficiently to support the perception of change. (shrink)

General Relativity defines gravity like the metric of a Lorentzian manifold. Einstein formulated spacetime as quality structural of gravity, i.e, circular definition between gravity and spacetime, also Einstein denoted "Space and time are modes by which we think, not conditions under which we live" and “We denote everything but the gravitational field as matter”, therefore, spacetime is nothing and gravity in first approximation an effect of coordinates, and definitely a geometric effect. The mathematical model generates quantitative predictions coincident in high (...) grade with observations without physical meaning. Philosophy intervened: in Substantivalism, spacetime exists in itself while in Relationalism as metrical relations. But, it does not know what spacetime. The outcomes of model have supported during a century, validity of the General Relativity, interpreted arbitrarily. Einstein formulated, from quadrupoles of energy, the formation of ripples in spacetime propagating as gravitational waves abandoned, in 1938, when he said that they do not exist. LIGO announced the first detection of gravitational waves from a pair of merging black holes. They truly are waves of quantum vacuum. (shrink)

Political fact-checkers evaluate the truthfulness of politicians’ claims. This paper contributes to an emerging scholarly debate on whether fact-checkers treat political parties differently in a systematic manner depending on their ideology (bias). We first examine the available approaches to analyze bias and then present a new approach in two steps. First, we propose a logistic regression model to analyze the outcomes of fact-checks and calculate how likely each political party will obtain a truth score. We test our model with a (...) sample of fact-checks from Newtral, a major Spanish fact-checker. Our model would signal bias under two assumptions: a) all political parties are on average equally accurate in their statements; b) the verification method gives precise instructions and is implemented systematically. We investigate this second assumption with a series of interviews with Newtral fact-checkers. We show that standard verification protocols are so loosely implemented that fact-checks reflect a set of journalistic decisions, rather than a bias in the statistical sense. We call for a more rigorous definition of verification methods as a pre-requisite for an unbiased assessment of politician’s claims. (shrink)

Memory Detection Tests (MDTs) are a general class of psychophysiological tests that can be used to determine whether someone remembers a particular fact or datum. The P300 MDT is a type of MDT that relies on a presumed correlation between the presence of a detectable neural signal (the P300 “brainwave”) in a test subject, and the recognition of those facts in the subject’s mind. As such, the P300 MDT belongs to a class of brain-based forensic technologies which have proved (...) popular and controversial in recent years. With such tests increasingly being proffered for use in the courtroom — to either support or call into question testimony — it would behoove the legal system to have some systematic framework for ensuring that they are used responsibly. In this paper, I defend one such framework for ensuring that this is the case: the legitimacy enhancing test. According to this test, it is appropriate to make use of technologies such as the P300 MDT whenever doing so would (probably) enhance the legitimacy of the trial. I argue that this test addresses tensions between scientific and legal norms of evidence, and exhibits a number of additional virtues including unification, simplicity and flexibility. Although the test is defended by considering the example of the P300 MDT, its significance is much broader than that. If it has the virtues I claim for it, it should provide a general framework for the responsible use of technologies, and the responsible innovation of social epistemic systems. (shrink)

Freeman Dyson has questioned whether any conceivable experiment in the real universe can detect a single graviton. If not, is it meaningful to talk about gravitons as physical entities? We attempt to answer Dyson’s question and find it is possible concoct an idealized thought experiment capable of detecting one graviton; however, when anything remotely resembling realistic physics is taken into account, detection becomes impossible, indicating that Dyson’s conjecture is very likely true. We also point out several mistakes in the (...) literature dealing with graviton detection and production. (shrink)

This sentence G ↔ ¬(F ⊢ G) and its negation G ↔ ~(F ⊢ ¬G) are shown to meet the conventional definition of incompleteness: Incomplete(T) ↔ ∃φ ((T ⊬ φ) ∧ (T ⊬ ¬φ)). They meet conventional definition of incompleteness because neither the sentence nor its negation is provable in F (or any other formal system). -- .

The lie detector test has long been treated with suspicion by the law. Recently, several authors have called this suspicion into question. They argue that the lie detector test may have considerable forensic benefits, particularly if we move past the classic, false-positive prone, autonomic nervous system-based (ANS-based) control question test, to the more reliable, brain-based, concealed information test. These authors typically rely on a “comparative advantage” argument to make their case. According to this argument, we should not be so suspicious (...) of lie detection evidence if it has comparative advantages over the epistemic methods currently utilised by the legal system. In this article, I add to this growing support by making a novel comparative advantage argument in favour of brain-based lie detection evidence. The argument focuses on the P300 concealed information test (P300 CIT), which has several unique properties, and on the effect it may have on pre-trial bargaining in criminal cases rather than in-court evidence. The thesis is that P300 CIT could allow for innocent defendants to credibly signal their innocence to investigators and prosecutors during pre-trial bargaining more effectively than current proposed methods for doing the same thing. I defend this argument from a number of objections, and suggest that it opens up an interesting avenue in the ongoing debate about the merits of this technology. Although the argument is presented with the criminal law in mind, it could form part of a more general cumulative case in favour of this technology. (shrink)

Research in the psychology of deception detection implies that Fricker, in making her case for reductionism in the epistemology of testimony, overestimates both the epistemic demerits of the antireductionist policy of trusting speakers blindly and the epistemic merits of the reductionist policy of monitoring speakers for trustworthiness: folk psychological prejudices to the contrary notwithstanding, it turns out that monitoring is on a par (in terms both of the reliability of the process and of the sensitivity of the beliefs that (...) it produces) with blind trust. The consequence is that while (a version of) Fricker’s argument for the necessity of a reduction succeeds, her argument for the availability of reductions fails. This does not, however, condemn us to endorse standard pessimistic reductionism, according to which there is no testimonial knowledge, for recent research concerning the methods used by subjects to discover deception in non-laboratory settings suggests that only a more moderate form of pessimism is in order. (shrink)

The objective of the paper is to implement and validate diagnosis in the medical field via refined neutrosophic fuzzy logic (RNFL). As such, we have proposed a Max-Min composition (MMC) method in RNFL. This method deals with the diagnosis under certain constraints like uncertainty and indeterminacy. Further, we have considered the diagnosis problems to validate the sensitivity analysis of the novel multi attribute decision-making technique. Finally, we gave the graphical representations and compared the obtained results with other existing measures in (...) refined neutrosophic fuzzy sets. (shrink)

Study shows that mask-wearing is a critical factor in stopping the COVID-19 transmission. By the time of this article, most states have mandated face masking in public space. Therefore, real-time face mask detection becomes an essential application to prevent the spread of the pandemic. This study will present a face mask detection system that can detect and monitor mask-wearing from camera feeds and alert when there is a violation. The face mask detection algorithm uses a haar cascade (...) classifier to find facial features from the camera feed and then utilizes it to detect the mask-wearing status. With the increasing number of cases all over the world, a system to replace humans to check masks on the faces of people is greatly needed. This system satisfies that need. This system can be employed in public places like transport stations and malls. It will be of great help in companies and huge establishments where there will be a lot of workers. Alongside this, we have used basic concepts of transfer learning in neural networks to finally output the presence or absence of a face mask in an image or a video stream. Experimental results show that our model performs well on the test data with 100 percent and 99 percent precision and recall, respectively. We are making a savvy framework that will identify whether the specific user has worn the mask or not and further tell the accuracy level of the detection. Our framework will be python and AI-based which will be safe and quick for implementation. This system will be of great help there because it is easy to obtain and store the data of the employees working in that Company and will very easily find the people who are not wearing the mask and a will be sent to that respective person to take Precautions not wearing a mask. Potential delays are analyzed, and efforts are made to reduce them to achieve real-time detection. The experiment result shows that the presented system achieves a real-time 45fps 720p Video output, with a face mask detection response of 0.15s and the accuracy of detection 99%. (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.

In the classical Turing test, participants are challenged to tell whether they are interacting with another human being or with a machine. The way the interaction takes place is not direct, but a distant conversation through computer screen messages. Basic forms of interaction are face-to-face and embodied, context-dependent and based on the detection of reciprocal sensorimotor contingencies. Our idea is that interaction detection requires the integration of proprioceptive and interoceptive patterns with sensorimotor patterns, within quite short time lapses, (...) so that they appear as mutually contingent, as reciprocal. In other words, the experience of interaction takes place when sensorimotor patterns are contingent upon one’s own movements, and vice versa. I react to your movement, you react to mine. When I notice both components, I come to experience an interaction. Therefore, we designed a “minimal” Turing test to investigate how much information is required to detect these reciprocal sensorimotor contingencies. Using a new version of the perceptual crossing paradigm, we tested whether participants resorted to interaction detection to tell apart human from machine agents in repeated encounters with these agents. In two studies, we presented participants with movements of a human agent, either online or offline, and movements of a computerized oscillatory agent in three different blocks. In each block, either auditory or audiovisual feedback was provided along each trial. Analysis of participants’ explicit responses and of the implicit information subsumed in the dynamics of their series will reveal evidence that participants use the reciprocal sensorimotor contingencies within short time windows. For a machine to pass this minimal Turing test, it should be able to generate this sort of reciprocal contingencies. (shrink)

I criticize Yamada's account of rule-following. Yamada's conditions are not necessary. And he misses the deepest level of the rule-following considerations: how meaning rules come about.

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports indicate that wearing facemasks while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using (...) deep and classical machine learning for face mask detection will be presented. A face mask detection dataset consists of with mask and without mask images, we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. -/- . (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows (...) its assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

Rutherford and Ray think that human beings have mental mechanisms that help them to detect individuals that, deliberately, do not follow a rule. In the same way, they hold that autism is not a disorder in which these mechanisms are damaged. This idea seems contrary to the thesis, supported by some researchers, that autistic people have a theory of mind deficit. This is because of, if Rutherford and Ray are right, autistic people can detect other people’s intentions. In this paper, (...) Rutherford and Ray’s experiment is analyzed and it is concluded that they do not prove that people with autism can understand intentions. Such an experiment only shows that autistic people and general population share common logical-linguistic abilities. (shrink)