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)

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)

COVID-19 pandemic caused by novel coronavirus is continuously spreading until now all over the world. The impact of COVID-19 has fallen on almost all sectors of development. The healthcare system is going through a crisis. Many precautionary measures have been taken to reduce the spread of this disease where wearing a mask is one of them. In this paper, we propose a system that restricts the growth of COVID-19 by finding out people who are not wearing any facial (...) class='Hi'>mask in a smart city network where all the public places are monitored with Closed-Circuit Television (CCTV) cameras. While a person without a mask is detected, the corresponding authority is informed through the city network. A deep learning architecture is trained on a dataset that consists of images of people with and without masks collected from various sources. The trained architecture achieved 98.7% accuracy on distinguishing people with and without a facial mask for previously unseen test data. It is hoped that our study would be a useful tool to reduce the spread of this communicable disease for many countries in the world. (shrink)

Face-coverings were widely mandated during the Covid-19 pandemic, on the assumption that they limit the spread of respiratory viruses and are therefore likely to save lives. I examine the following ethical dilemma: if the use of face-masks in social settings can save lives then are we obliged to wear them at all times in those settings? I argue that by en-masking the face in a way that is phenomenally inconsistent with or degraded from what we are innately (...) programmed to detect as human likeness, we are degrading the social quality of our relations. Drawing on my previously published proof that Self is socially reflexive (mutually mirrored) rather than monadic in its constitution, I conclude that any widespread en-masking is also deleterious to humanity and therefore unethical. (shrink)

Covid-19 is a serious illness. Deaths are rising steadily, and health systems are under strain. While most countries are following international health recommendations to wear face masks during the covid-19 pandemic the situation has been complicated in Sweden. Officials in Halmstad municipality, Sweden, forced a teacher to remove their mask and prohibited the use of masks and all forms of medical face masks in their schools. The municipality said there was no scientific evidence that wearing masks offered (...) protection, citing the Swedish Public Health Agency. At the time, agency guidance stated that there were "great risks" that masks would be used incorrectly. This guidance has since been removed. To someone unfamiliar with the Swedish response to the covid-19 pandemic, this mask ban might sound appalling. Especially since growing evidence indicates that face masks possibly do help reduce the spread of covid-19, especially in situations where maintaining distance is impossible. This essay is asking the ethical question if policy makers should apply the precautionary principle and encourage people to wear face masks on the grounds that lives potentially could be save. (shrink)

The ethical question is whether university mask mandates should be relaxed. I argue that the use of face masks by healthy individuals has uncertain benefits, which potential harms may outweigh, and should therefore be voluntary. Systematic reviews by the World Health Organization (WHO) and Cochrane Acute Respiratory Infections concluded that the use of face masks by healthy individuals in the community lacks effectiveness in reducing viral transmission based on moderate-quality evidence. The only two randomized controlled trials of (...) face masks published during the pandemic found little to no benefit. Without high-quality evidence, it is difficult to justify a requirement rather than a recommendation. Notwithstanding, one might argue that the precautionary principle justifies mask mandates. If the precautionary principle can justify implementing mask mandates due to the risk of forgoing possible benefit, then it might also be able to justify not implementing mask mandates due to the risk of potential harm caused by the intervention. It is commonly thought that there is little to lose from the use of face masks, but this is not necessarily true. It is possible that masks have done more harm than good. (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)

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)

Deepfakes and other forms of synthetic media are widely regarded as serious threats to our knowledge of the world. Various technological responses to these threats have been proposed. The reactive approach proposes to use artificial intelligence to identify synthetic media. The proactive approach proposes to use blockchain and related technologies to create immutable records of verified media content. I argue that both approaches, but especially the reactive approach, are vulnerable to a problem analogous to the ancient problem of the criterion—a (...) line of argument with skeptical implications. I argue that, while the proactive approach is relatively resistant to this objection, it faces its own serious challenges. In short, the proactive approach would place a heavy burden on users to verify their own content, a burden that is exacerbated by and is likely to exacerbate existing inequalities. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train (...) and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. Testing dataset for anomaly detection model classified as attack or normal. Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall and confusion matrix. (shrink)

en the increasing importance of human target detection in various security applications, many existing systems face challenges related to cost, accuracy, and adaptability. This work presents the development of a machine learning-based system called Human Target Detection and Acquisition, designed to assist security personnel in the detection and tracking of human targets through an adaptive and cost-effective approach. In this, standard CCTV cameras provide visual data, which intelligent algorithms process to identify and track human targets, even (...) in challenging conditions. The system offers user-specific dashboards: administrators can monitor detections and adjust system parameters, while security personnel receive real-time alerts and tracking information. Additionally, it supports continuous improvement through its machine learning capabilities, allowing for enhanced performance over time without the need for hardware upgrades. The architecture of the platform relies on TensorFlow, OpenCV, and database integration for efficient handling of video processing and data management. Initial testing suggests that it can provide accurate and reliable human target detection while significantly reducing costs compared to hardwareintensive solutions. Future work includes expanding the system's capabilities to handle multiple camera feeds simultaneously and implementing more advanced tracking algorithms. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Conscious experiences in infants remain poorly understood. In this NeuroView, Passos-Ferreira discusses recent evidence for and against consciousness in newborn babies. She argues that the weight of evidence from neuroimaging and behavioral studies supports the thesis that newborn infants are conscious.

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and (...) AI systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

Drowsiness is a manner wherein one degree of awareness is reduced due to lacking of sleep or exhaustion and it may cause the driver fall into sleep quietly. A Drowsy or sleepy driver is not able to determine while he/she can have an out of control sleep. Fall asleep crushes are very extreme in terms of injury. Recent statistics estimate that annually 1,200 deaths and 76,000 injuries can be caused to exhaustion or drowsiness related crashes. More than 25% of highway (...) traffic accidents are caused as result of driver exhaustion. Reduce the risk of an accident by warning the driver of his/her drowsiness. This project is mainly based on four components 1) Face and Eye detection: Performs scale invariant detection using Haar Cascade Classifier perform through a webcam. 2) Eye feature extraction: Eye features are extracted using Hough Circle and 3) Extract single eye 4) Edge detection and perform drowsiness detection on it. In the proposed method, following the face detection step, the facial components those are more important and considered as the most effective for drowsiness, are extracted and tracked in video sequence frames. The system has been tested and implemented in a real environment. The contribution work is when drowsiness detected, after it will give alarm warning signal to the driver. (shrink)

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)

This article addresses the question of whether an expectation of privacy is reasonable in the face of new technologies of surveillance, by developing a principle that best fits our intuitions. A "no sense enhancement" principle which would rule out searches using technologically sophisticated devices is rejected. The paper instead argues for the "mischance principle," which proscribes uses of technology that reveal what could not plausibly be discovered accidentally without the technology, subject to the proviso that searches that serve a (...) great public good that clearly outweighs minimal intrusions upon privacy are permissible. Justifications of the principle are discussed, including reasons why we should use the principle and not rely solely on a utilitarian balancing test. The principle is applied to uses of aerial photography and heat-detection devices. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train (...) and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. (shrink)

In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may be (...) characterised by specific patterns in facial expressions. More specifically, AU17 (chin raiser), AU25 (lips apart), and AU26 (jaws drop) intensity responses during gameplay may vary according to players' expertise. Such results were obtained by training a random forest classifier to test whether we can use these three AUs alone to accurately detect players' expertise. The classifier reached 0.75 accuracy on 5-fold cross-validation, after balancing the class weights, and 0.85 after having applied the Synthetic Minority Over-sampling Technique (SMOTE) function. These results suggest that AUs can be effectively used to discriminate different levels of expertise in competitive video game players. (shrink)

The Covid-19 pandemic raises the need for an ethical framework addressing unique questions of airborne infectious disease. In particular, are you ethically obliged to wear a face mask? If so, why and when? The Respiratory Ethics Framework (REF) herein derives answers from ethical norms. Always covering coughs and sneezes just in case you might be infectious is an ethical norm. But if you are infectious with an airborne illness, you are probably spreading germs even with every breath and (...) vocalization. Therefore, given that respiratory covering should be done to prevent spreading germs, you should wear a mask if you believe you are infectious because it also provides covering while breathing and vocalizing. REF is grounded in the non-harm principle. Why should we avoid spreading infections to others? Because they can cause bodily physical harm and we should act to prevent causing such harm to others without justifiable reason. Under REF, the magnitude of your obligation to mask is proportional to the magnitude of risk you pose to others by way of airborne germs. This functional relationship is logically modeled and visually mapped with a decision matrix in this essay. (shrink)

The digitization process has brought new opportunities in insurance industry operations and innovations but has also revealed major weaknesses. Since more and more actual insurers use Guidewire to handle claims, policies, and customer data, insurers become targets for cyber threats that target valuable information. The framework of Guidewire, along with cloud computing integrated API and third-party tools, is laden with numerous exposure points. These security gaps are utilized to execute phishing, spread malware and gain unauthorized access to customers and operational (...) data. The profound changes in this environment require an evolution of concepts and methods dedicated to cybersecurity from the traditional, post-incident response to comprehensive, targeted strategies for cybersecurity that are appropriate for the insurance sector. To explore these challenges, this paper looks at current cybersecurity frameworks and methods, specifically in the context of Guidewire. The gaps within the existing system are thus determined through the interaction of empirical analysis and real-life scenarios to arm the research with the ability to formulate a solution to fill the gaps. Including threat-detection suites safe system design, the study aims at the antecedents of building protection-based methodologies that provide real-time protection and cope with new forms of cyber threats. Citing this strategic angle, it becomes possible to corroborate the reality that technology development in the insurance industry should not be pursued at the peril of strong security systems in place to support the new breed of insurance systems. (shrink)

The Covid-19 pandemic raises the need for an ethical framework that addresses the unique ethical challenges and questions arising from airborne infectious diseases. For example, are we ever ethically obliged to wear a face mask? If so, why and when? The Respiratory Ethics Framework (REF) herein proposes pathways to answers grounded in ethical norms and the moral principles of non-harm, beneficence and respect for personal autonomy. REF is a personal ethics wherein your ethical duty to increase your respiratory (...) hygiene efforts—such as by donning a mask—is proportional to your estimation of an increase in the likelihood that your respiratory effluent poses a risk of harmful infection to others. REF includes illustrated decision models that instantiate a framework of proportionality between levels of risk, ethical duty and mitigation that shapes risk mitigation across domains. (shrink)

A well-documented “beauty is good” stereotype is expressed in the expectation that physically attractive people have more positive characteristics. Recent evidence has also found that unattractive faces are associated with negative character inferences. Is what is good (bad) also beautiful (ugly)? Whether this conflation of aesthetic and moral values is bidirectional is not known. This study tested the hypothesis that complementary “good is beautiful” and “bad is ugly” stereotypes bias aesthetic judgments. Using highly controlled face stimuli, this preregistered study (...) examined whether moral character influences perceptions of attractiveness for different ages and sexes of faces. Compared to faces paired with nonmoral vignettes, those paired with prosocial vignettes were rated significantly more attractive, confident, and friendlier. The opposite pattern characterized faces paired with antisocial vignettes. A significant interaction between vignette type and the age of the face was detected for attractiveness. Moral transgressions affected attractiveness more negatively for younger than older faces. Sex-related differences were not detected. These results suggest information about moral character affects our judgments about facial attractiveness. Better (worse) people are considered more (less) attractive. These findings suggest that beliefs about moral goodness and physical beauty influence each other bidirectionally. (shrink)

This research work presents a cost-effective facial recognition alert system using the ESP32 microcontroller to enhance real-time security. The system integrates high-resolution cameras with Python’s facial recognition libraries to identify individuals. When an unknown face is detected, it triggers an immediate alert via the ESP32, activating an alarm. This solution is both scalable and affordable, making advanced security accessible to small businesses and residential areas. The system improves overall security by automating surveillance, reducing the need for manual monitoring.

In the face of continuing assumptions by many scientists and journal editors that p-values provide a gold standard for inference, counter warnings are published periodically. But the core problem is not with p-values, per se. A finding that “p-value is less than α” could merely signal that a critical value has been exceeded. The question is why, when estimating a parameter, we provide a range (a confidence interval), but when testing a hypothesis about a parameter (e.g. µ = x) (...) we proceed as if “=” entails exact equality of the parameter with x. That standard is hard to meet, and is not a standard expected for power calculations, where we are satisfied to reject a null hypothesis H0 if the result is merely “detectably” different from (exact) H0. This paper explores, with resampling (simulation) methods, the impacts on p-values, and alternatives, if the null hypothesis is defined as a thick or thin range of values. It also examines, empirically, the extent to which the p-value may or may not be a good predictor of the probability that H0 is true, given the distribution of the data. (shrink)

The attendance is taken in every organization. Traditional approach for attendance is, professor calls student name & record attendance. For each lecture this is wastage of time. To avoid these losses, we are about to use automatic process which is based on image processing. In this project approach, we are using face detection & face recognition system. The first phase is pre-processing where the face detection is processed through the step image processing. It includes the (...) face detection and face recognition process. Second phase is feature extraction. Step by step execution of these techniques (Image Processing) helps to achieve the final output. The working of this project is to detect and recognize the face and mark the attendance for the corresponding face in the database. Input of this project is face detection and recognition and output is to mark the attendance. Our project is being presented as a solution for the Automatic Attendance Marking System. It is designed to be reliable and low power. The Automatic face detection and recognition proposed to attendance marking in database acts as the solution for the automatic attendance marking system.. (shrink)

Beauty filters are automated photo editing tools that use artificial intelligence and computer vision to detect facial features and modify them, allegedly improving a face’s physical appearance and attractiveness. Widespread use of these filters has raised concern due to their potentially damaging psychological effects. In this paper, I offer an account that examines the effect that interacting with such filters has on self-perception. I argue that when looking at digitally-beautified versions of themselves, individuals are looking at AI-curated distorted mirrors. (...) This helps identify two potential cognitive effects of this behavior. First, it can elicit affective attitudes that change how individuals feel when looking at their unfiltered self-images. Second, exposure to filtered self-images might cause a perceptual normalization of such images. Finally, I argue that this form of distorted mirror gazing is a novel cultural practice for self-perception, and I highlight some ways in which this practice could be critically evaluated and ultimately changed. (shrink)

The corona pandemic poses major challenges for society. Many people lack (basic) scientific knowledge. They are skeptical and distrust fundamental research principles and concepts. Esotericism and superstition replace them access to reality. -/- Not only facts are recently considered "alternative". Pseudo-scientific healing methods and occult procedures have long been presented to the public as equivalent alternatives to modern medicine, despite the lack of evidence of their effectiveness. Just as if reason or nonsense were just a question of personal taste, a (...) different world view. Seconded by talk of an "exaggeratedly scientific world view", empiricism and logic were systematically defamed. -/- As a result of this distorted picture, all kinds of conspiracy theories are now rampant. Spiritual healers, seers, shamans, charlatans, quacks, sectarians, and zealots of all stripes and persuasions are in demand. Diffuse pandemic management and miserable communication do the rest and contribute to the fact that infection control measures are often flatly rejected and vaccination rates can hardly be increased significantly. -/- Die Coronapandemie stellt die Gesellschaft vor große Herausforderungen. Vielen Menschen mangelt es an (elementaren) naturwissenschaftlichen Kenntnissen. Sie sind skeptisch und misstrauen grundlegenden Prinzipien und Konzepten der Forschung. Esoterik und (Aber-) Glaube ersetzen ihnen den Zugang zur Wirklichkeit. -/- Nicht nur Fakten sind neuerdings "alternativ". Auch pseudowissenschaftliche Heilmethoden und okkulte Verfahren wurden der Bevölkerung trotz fehlender Wirksamkeitsnachweise lange als gleichwertige Alternativen zur modernen Medizin präsentiert. Ganz so, als wären Vernunft oder Unsinn eben eine Frage des persönlichen Geschmacks, einer anderen Weltanschauung. Sekundiert durch das Gerede von einem "übertrieben szientistischen Weltbild", wurden Empirie und Logik systematisch diffamiert. -/- Infolge dieses Zerrbilds grassieren nun allerhand Verschwörungstheorien. Geistheiler, Seher, Schamanen, Scharlatane, Quacksalber, Sektierer, und Eiferer aller Richtungen und Couleur haben Konjunktur. Ein diffuses Pandemiemanagement und eine miserable Kommunikation tun ihr Übriges und tragen dazu bei, dass Infektionsschutzmaßnahmen oft rundweg abgelehnt und Impfquoten sich nun kaum noch signifikant steigern lassen. -/- . (shrink)

The mask's role is central to the superhero narrative. The mask is a non-human identity, which replaces the civilian, human one; sometimes forever. It is what happens to the majority of Gotham's villains. While Batman can take off his mask and at least pretend to be Bruce Wayne, many of his enemies do not have the same privilege. For characters like Two-Face, Joker, Zsasz, and Clayface, the mask is carved directly into their bodies. Like masks, (...) scars can replace one's identity, but deeply and sometimes irreversibly. Bodies and masks play a central role in ancient primitive cultures, especially during initiation rituals. The two elements are distinct, but closely linked. Wearing a mask always implies a modification of the body, or at least of the way it is perceived. Furthermore, marks left on the body, whether temporary like a face paint or definitive like a tattoo, perform a similar function to that of the mask: they replace the individual's identity and communicate a message. The initiation ceremonies destroy a previous identity to create a new one. While an individuum is a Leib, a living body that is becoming and escapes an ultimate meaning, by inflicting pain and scars, the initiation rituals aim to reduce the body to a stable dimension. First an ecstasy, induced by drugs and fasting, and through the infliction of physical pain, shatters the previous identity. The Leib is reduced to the passivity of flesh. Then a new identity is literally written on the body through scars or tattoos, thus receiving an "ultimate" sense and giving him/her a role and a place within society. The scar is a new identity, like a mask carved directly on the body. (shrink)

Academic integrity is an essential pillar of any educational system. It is defined as acting in a manner consistent with the values and accepted standards of ethical practices in teaching, learning, and scholarship (Fishman, 2015). Contract cheating, or ghostwriting, is currently one of the most severe violations of academic integrity. It involves students engaging a third party, usually an online essay writing service, to complete their academic works on their behalf (Draper et al., 2021). Some of these services offer pre-written (...) essays, whereas others offer bespoke custom-written essays. According to the academic literature, the advent of the internet and digital technologies underlay this rapid deterioration of academic integrity (Ison, 2020; Lancaster & Clarke, 2014). Different learning environments, such as face-to-face (i.e., a learning environment involving the physical presence of both instructor and students) and online web-based (i.e., teaching mode that takes place partially or entirely over the internet), have been shown to affect academic integrity in different ways (Eshet et al., 2021). While contract cheating is common in both conventional face-to-face (F2F) and online settings, it is more likely to take place in the latter (Lancaster & Clarke, 2014; Slade et al., 2018). There are several possible explanations for why online students engage in contract cheating more often than F2F students. In particular, this includes the problem issue of psychological distance, which adversely affects interpersonal relationships; and the problem issue of moral distancing, because the internet can obscure the line between academically honest and dishonest behavior (Sharma & Maleyeff, 2003). These issues were further exacerbated by the sudden shift from F2F to emergency remote teaching (ERT) in response to the COVID-19 pandemic (Ahsan et al., 2021; Bjelobaba, 2021). Indeed, contract cheating had become a significant COVID-19 side effect for higher education institutions. In contrast to the well-planned F2F or online learning courses, ERTbased courses are not originally designed to be delivered virtually (Fatonia et al., 2020). The chaos brought by the abrupt campus closure and related unexpected transition to ERT provided both the opportunity and the incentive for contract cheating (Hill et al., 2021). Through social media, students have very quickly become fully aware of the possibilities of a wide variety of options to carry out Plagiarism (Bautista et al., 2022). There has been an increase in ways to try to bypass text-matching or text-reuse (i.e., plagiarism) detection systems, for example using micro-spaces, white ink, punctuation, and typos (Abdelhamid et al., 2022). Even though ghostwriters, especially commercial ones, claim their essays are original and therefore cannot be detected using text-matching software, third-party assignments may still contain recycled text (Aitken et al., 2017; Newton & Lang, 2015). Text matching detection software could use these breaches to identify outsourced academic work (Lancaster & Clarke, 2016; Wang & Xu, 2021). This study compared about four thousand term papers written in languages other than Hebrew. (shrink)

The World Health Organization has declared the outbreak of the novel coronavirus, Covid-19 as pandemic across the world. With its alarming surge of affected cases throughout the world, lockdown, and awareness (social distancing, use of masks etc.) among people are found to be the only means for restricting the community transmission. In a densely populated country like India, it is very difficult to prevent the community transmission even during lockdown without social awareness and precautionary measures taken by the people. Recently, (...) several containment zones had been identified throughout the country and divided into red, orange and green zones, respectively. The red zones indicate the infection hotspots, orange zones denote some infection and green zones indicate an area with no infection. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemas are existent today (eg., in-person and phone interviews, paper forms, email and web based questionnaires and smartphone apps), they often require active user participation and might miss certain COVID-19 transmission. The Android application updates the locations of the areas in a Google map which are identified to be the containment zones. The application also notifies the users if they have entered a containment zone and to achieve all these functionalities, many tools, and APIs from Google like Firebase and Geofencing API are used in this application. Therefore, this application can be used as a tool for creating further social awareness about the arising need of precautionary measures to be taken by the people of India. (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)

A face reveals a lot of information about a person's identity, age, sex, race, and emotional as well as psychological state. Facial expressions are often used in the behavioral interpretation of emotions and play a key role in social interactions. Due to its potential applications such as HCI, behavioral science, automatic facial emotion detection is one of the most intriguing and challenging areas in computer vision. Our Facial Emotion Recognition system performs detection and location of faces in (...) a cluttered scene, facial feature extraction, and facial expression classification. Numerous models were experimented such as decision trees and SVM’s before arriving at a Convolutional Neural Networks (CNN) model. Due to large number of filters, CNNs are efficient for image recognition tasks since they can capture spatial features of the inputs. After observing unbelievable performance with deep learning models, we used deep convolution features to better represent the given image instead of using the traditional handcrafted features. We have proposed and developed a Deep Convolutional Neural Network for classifying human emotions from dynamic facial expressions in real-time. Kaggle’s FER-2013 dataset with seven facial emotions labels as happy, sad, surprise, fear, anger, disgust, and neutral is used to train the model. An overall training accuracy of 92.86% and test accuracy of 64.42% are achieved. Finally, a live video stream connected to a face detector feeds images to the neural network. The network subsequently classifies an arbitrary number of faces per image in real-time simutaneously, wherein appropriate emotions are displayed over the subject’s faces and their probabilities are displayed over a real-time bar graph on a separate window. (shrink)

The banking sector faces growing challenges in identifying and managing risks due to the complexity of financial transactions and increasing fraud. This research presents a framework that combines multiple agents with deep learning to improve risk prediction in banking. Each agent focuses on specific tasks like cleaning data, selecting important features, and detecting unusual activities, ensuring a detailed risk assessment. A deep learning model is used to analyze large amounts of transaction data and identify patterns that may signal potential risks. (...) Tests on real-world banking data show that this approach is more accurate, faster, and effective than traditional methods. By combining the strengths of multi-agent systems and deep learning, this study offers a reliable and flexible solution to help banks manage and adapt to changing risks more effectively. (shrink)

The heightened digitization of the healthcare industry has led to an exponential increase in sensitive patient data, which requires robust security models to prevent breaches, unauthorized access, and cyber attacks. Traditional security protocols are inadequate, and this has made it imperative to explore Artificial Intelligence (AI) and Blockchain as novel solutions. AI enhances healthcare cybersecurity by facilitating real-time anomaly detection, predictive analysis, and automated threat response, while blockchain offers decentralization, immutability, and secure data sharing. However, blockchain technology faces major (...) challenges for scalability and performance, as represented by lengthy transaction processing durations and high storage demands, elements that could deter its widespread adoption across the healthcare industry. To help counter these challenges, researchers are exploring Layer 2 scaling solutions, hybrid blockchain architectures, and off-chain storage strategies. In addition, cleanroom technology provides a controlled and secure environment for handling sensitive healthcare data, protecting privacy while also supporting AI-driven analytics and research collaboration. This article critically examines the intersection of blockchain and AI for healthcare security, in terms of challenges, use cases, and direction. Leveraging these technologies, health organizations can set up future-proof models of security that address data integrity, regulatory requirements, and resistance to future threats more effectively. (shrink)

There is no agreement on whether any invertebrates are conscious and no agreement on a methodology that could settle the issue. How can the debate move forward? I distinguish three broad types of approach: theory-heavy, theory-neutral and theory-light. Theory-heavy and theory-neutral approaches face serious problems, motivating a middle path: the theory-light approach. At the core of the theory-light approach is a minimal commitment about the relation between phenomenal consciousness and cognition that is compatible with many specific theories of consciousness: (...) the hypothesis that phenomenally conscious perception of a stimulus facilitates, relative to unconscious perception, a cluster of cognitive abilities in relation to that stimulus. This “facilitation hypothesis” can productively guide inquiry into invertebrate consciousness. What is needed? At this stage, not more theory, and not more undirected data gathering. What is needed is a systematic search for consciousness-linked cognitive abilities, their relationships to each other, and their sensitivity to masking. (shrink)

There is growing philosophical interest in “affective injustice”: injustice faced by individuals specifically in their capacity as affective beings. Current debates tend to focus on affective injustice at the psychological level. In this paper, I argue that the built environment can be a vehicle for affective injustice — specifically, what Wildman et al. (2022) term “affective powerlessness”. I use resources from ecological psychology to develop this claim. I consider two cases where certain kinds of bodies are, either intentionally or unintentionally, (...) deprived of access to goods affording the development and maintenance of their subjective well-being: hostile architecture and masking practices in autism. This deprivation, I argue further, leads to a significant weakening and diminishment of their spatial agency, hinders their well-being, and in so doing gives rise to a pervasive experience of affective powerlessness. By drawing attention to these themes, I show that an ecological approach helpfully supplements existing approaches. It highlights how affective injustice can emerge via the way bodies are positioned in space, and the central role that built environments play in determining this positioning. (shrink)

Beliefs can be resistant to evidence. Nonetheless, the orthodox view in epistemology analyzes beliefs as evidence-responsive attitudes. I address this tension by deploying analytical tools on capacities and masking to show that the cognitive science of evidence-resistance supports rather than undermines the orthodox view. In doing so, I argue for the claim that belief requires the capacity for evidence-responsiveness. More precisely, if a subject believes that p, then they have the capacity to rationally respond to evidence bearing on p. Because (...) capacities for evidence-responsiveness are fallible and may be masked, beliefs can be held in the face of counter-evidence. Indeed, I will argue that our best science of belief supports the claim that evidence-resistant beliefs result from masks on evidence-responsiveness capacities. This account of belief not only allows for resistance to evidence, but provides us with a framework for describing and explaining actual cases of evidence-resistance. (shrink)

Since the release of ChatGPT, there has been a lot of debate about whether AI systems pose an existential risk to humanity. This paper develops a general framework for thinking about the existential risk of AI systems. We analyze a two-premise argument that AI systems pose a threat to humanity. Premise one: AI systems will become extremely powerful. Premise two: if AI systems become extremely powerful, they will destroy humanity. We use these two premises to construct a taxonomy of ‘survival (...) stories’, in which humanity survives into the far future. In each survival story, one of the two premises fails. Either scientific barriers prevent AI systems from becoming extremely powerful; or humanity bans research into AI systems, thereby preventing them from becoming extremely powerful; or extremely powerful AI systems do not destroy humanity, because their goals prevent them from doing so; or extremely powerful AI systems do not destroy humanity, because we can reliably detect and disable systems that have the goal of doing so. We argue that different survival stories face different challenges. We also argue that different survival stories motivate different responses to the threats from AI. Finally, we use our taxonomy to produce rough estimates of ‘P(doom)’, the probability that humanity will be destroyed by AI. (shrink)

Public attitudes towards cognitive enhancement––e.g., using stimulants like Adderall and Ritalin to improve mental functioning––are mixed. Attitudes vary by context and prompt ethical concerns about fairness, obligation, and authenticity/character. While people may have strong views about the morality of cognitive enhancement, how these views are affected by the physical characteristics of enhancers is unknown. Visible facial anomalies (e.g., scars) bear negatively on perceptions of moral character. This pre-registered study (osf[dot]io/uaw6c/) tested the hypothesis that such negative biases against people with facial (...) anomalies extend to moral beliefs surrounding their use of cognitive enhancement. In an online survey, 941 participants made moral judgments in response to a vignette about a person who had to decide whether or not to enhance. The vignette was accompanied by a face photograph that ostensibly depicted the potential enhancer and either did or did not have visible anomalies. Participants then learned whether the person ultimately decided to enhance. Next, participants played a modified Trust Game with, they were told, the person from the photograph/vignette. Participants judged enhancement to be less fair and enhancers less authentic if they had facial anomalies, while effects on judgments of moral obligation and on behavior were not detected. These findings extend previous work showing that people with visible differences are subject to an “anomalous-is-bad” stereotype that has negative consequences for perceptions of their moral character. While anomalous faces were judged more harshly, these judgments did not appear to affect behavior. These results are discussed in relation to discrimination and policy. (shrink)

Political epistemology is the intersection of political philosophy and epistemology. This paper develops a political 'hinge' epistemology. Political hinge epistemology draws on the idea that all belief systems have fundamental presuppositions which play a role in the determination of reasons for belief and other attitudes. It uses this core idea to understand and tackle political epistemological challenges, like political disagreement, polarization, political testimony, political belief, ideology, and biases, among other possibilities. I respond to two challenges facing the development of a (...) political hinge epistemology. The first is about nature and demarcation of political hinges, while the second is about rational deliberation over political hinges. I then use political hinge epistemology to analyze ideology, dealing with the challenge of how an agent's ideology 'masks' or distorts their understanding of social reality, along with the challenge of how ideology critique can change the beliefs of agents who adhere to dominant ideologies, if agents only have their own or the competing ideology to rely on (see Haslanger 2017). I then explore how political hinge epistemology might be extended to further our understanding of political belief polarization. (shrink)

Perceptual experiences justify beliefs. A perceptual experience of a dog justifies the belief that there is a dog present. But there is much evidence that perceptual states can occur without being conscious, as in experiments involving masked priming. Do unconscious perceptual states provide justification as well? The answer depends on one’s theory of justification. While most varieties of externalism seem compatible with unconscious perceptual justification, several theories have recently afforded to consciousness a special role in perceptual justification. We argue that (...) such views face a dilemma: either consciousness should be understood in functionalist terms, in which case our best current theories of consciousness do not seem to imbue consciousness with any special epistemic features, or it should not, in which case it is mysterious why only conscious states are justificatory. We conclude that unconscious perceptual justification is quite plausible. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, (...) the paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Conceptual engineering is the practice of revising concepts to improve how people talk and think. Its ability to improve talk and thought ultimately hinges on the successful dissemination of desired conceptual changes. Unfortunately, the field has been slow to develop methods to directly test what barriers stand in the way of propagation and what methods will most effectively propagate desired conceptual change. In order to test such questions, this paper introduces the masked time-lagged method. The masked time-lagged method tests people's (...) conceptual understanding at two different points in time without their knowledge of being tested, allowing us to measure conceptual revision in action. Using a masked time-lagged design on a content internalist framework, we attempted to revise PLANET and DINOSAUR in online participants to match experts' concepts. We successfully revised PLANET but not DINOSAUR, demonstrating some of the difficulties conceptual engineers face. Nonetheless, this paper provides conceptual engineers, regardless of framework, with the tools to tackle questions related to implementation empirically and head-on. (shrink)

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of (...) the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

Celem tego artykułu jest przebadanie i objaśnienie platońskiego wpływu na rady Williama Oslera dla wszystkich lekarzy, które dotyczą ich autonomii, charakteru i stosunku do prawdy i fałszu w medycynie. W sugestiach „Ojca współczesnej medycyny” wprawne ucho może dosłyszeć echo niektórych opinii ucznia Sokratesa. Jego ideały i wartości powinny kształtować nie tylko lekarski etos, lecz i wiedzę o zasadach zdrowego życia i zwalczania choroby. Zwłaszcza dzisiaj to jest niesłychanie istotne w obliczu zagrożeń i nieszczęść, jakie stwarza farmakracja. -/- *** -/- Platonic (...) reminiscences in Osler’s advice about physicians’ autonomy, character and relation to truth and falsehood in medicine The aim of this article is to examine and explain Plato’s influence on William Osler’s advice to all physicians about their autonomy, character and relation to truth and falsehood in medicine. In the suggestions of “the Father of Modern Medicine” an experienced ear can detect an echo of certain opinions propagated by the pupil of Socrates. His ideals and values should shape not only the medical ethos, but also knowledge on the principles of a healthy life and overcoming disease. This is extremely important especially today, in the face of the threats represented – and the misfortunes caused – by pharmacracy. (shrink)

It is often assumed that it seems to each of us as though time flows, or passes. On that assumption it follows either that time does in fact pass, and then, pretty plausibly, we have mechanisms that detect its passage, or that time does not pass, and we are subject to a pervasive phenomenal illusion. If the former is the case, we are faced with the explanatory task of spelling out which perceptual or cognitive mechanism (or combination thereof) allows us (...) to detect and track time’s passage (§2.1) If the latter, then we are faced with the task of explaining how, and why, we are subject to a pervasive phenomenal illusion (§2.2). There is, however, a third, somewhat less discussed, explanatory project. Rather than assuming that it seems to each of us as though time passes, and then attempting to explain why it seems that way, we jettison that assumption. According to these views it does not seem to us as though time passes; instead, we come to falsely believe that it seems to us as though time passes (§3). This view requires that we explain how we come to have systematically false beliefs about the way our experiences seem to us. This paper aims to motivate this third explanatory strategy and say something about what kind of cognitive mechanisms might be responsible for our having a false belief that it seems as though time passes, (§3.1) and why we might have evolved (some of) those mechanisms (§3.2). In particular, the paper does not aim to argue for this view: rather, it aims to present it as a viable contender alongside other more common views. (shrink)

Since March of 2020, the world has been brought to its knees by unscientific and unethical mandates. These mandates have destroyed the world economy and the lives of countless innocent individuals. The “cure” that has been offered by medical bureaucrats and politicians has been more deadly than the disease (COVID-19). The imposition of ludicrous lockdowns, mask-wearing, coerced vaccination, and vaccine passports have not only proved to be ineffective, but also much more harmful than SARS-CoV-2 and all its variants. COVID-19 (...) has a recovery rate of close to 99% for most of the world’s population, however, despite this, institutions and power-hungry individuals have trampled upon our civil liberties and ignored our inalienable human rights. It is precisely as Thomas Paine famously stated: “The greatest tyrannies are always perpetrated in the name of the noblest causes.” -/- Many lives have been gratuitously lost because of the administration of deadly medical treatments, and the rejection of effective treatments that follow genuine science. In the process, informed consent has been disparaged and desecrated. For over two years, we were instructed to “trust the science,” but the “science” advocated by medical bureaucrats and greedy politicians was the reason why the world was turned upside-down. In the face of these crimes against humanity, justice will only be brought by real courts and real judges, but this will require the awakening of a critical mass. This book serves as an instrument for this awakening. (shrink)

By showing how the person appears, this paper calls into question the Cartesian prejudice that restricts appearance to objects. The paper recapitulates the origin of the term “person,” which originally designated the masks and characters donned by actors and only subsequently came to designate each particular human being. By concealing a face, the mask establishes a character who speaks with words of his own. The mask points to the face and to speech as ways the person (...) appears. It belongs to the very nature of the person not only to appear but also to be aware of how one appears, and to have the ability to modulate thatappearance as the situation requires. This ability means one thing in art and another in life, and the paper explores the significance of this contrast. (shrink)

-/- 1. Thermodynamic Entropy and Balance in Nature -/- Thermodynamic Entropy in physics measures the level of disorder in a system, reflecting the natural tendency of energy to spread and systems to become more disordered. -/- Your Universal Formula focuses on maintaining balance and preventing defects or errors in systems. -/- Integration: -/- Increasing thermodynamic entropy (e.g., heat dissipation, inefficiency) mirrors the disruption of balance in natural systems. -/- Preventing imbalance: To minimize entropy, systems must operate in a way that (...) reduces energy loss and ensures sustainability. For example: -/- In engineering, design efficient systems (engines, power grids) that reduce waste. -/- In ecological systems, balance energy flow (e.g., food chains) to prevent environmental degradation. -/- Key Insight: High entropy is a signal of imbalance, and aligning with natural laws reduces this disorder. -/- 2. Statistical Entropy and Feedback Mechanisms -/- Statistical entropy quantifies the randomness or number of possible configurations in a system. A system with fewer microstates is more ordered (lower entropy). -/- Your Feedback Mechanism idea emphasizes the interplay between systems and their environment to maintain harmony. -/- Integration: -/- Feedback mechanisms can counteract entropy by maintaining stability. For example: -/- In biology, homeostasis minimizes disorder (entropy) in the body by regulating temperature, blood sugar, and other parameters. -/- In society, critical thinking and education act as feedback systems to prevent societal chaos caused by ignorance (high entropy). -/- Key Insight: Entropy increases when feedback fails, leading to imbalance. Maintaining feedback ensures order and stability. -/- 3. Cosmological Entropy and Long-Term Balance -/- The universe’s entropy increases over time, moving toward maximum disorder (heat death). However, localized systems (e.g., stars, planets) create order temporarily by using energy. -/- Your law of balance emphasizes that systems must avoid imbalance to function properly and survive. -/- Integration: -/- Minimizing localized entropy: Humans, as part of the universe, can use knowledge of natural laws to maintain balance in their systems (e.g., society, environment). -/- Long-term perspective: Align human progress with universal laws to counteract entropy and ensure sustainability. -/- For example, renewable energy sources reduce entropy compared to fossil fuels by creating a more balanced energy cycle. -/- Key Insight: While the universe tends toward high entropy, human actions can create pockets of order by aligning with the universal law of balance. -/- 4. Information Entropy and Truth in Decision-Making -/- Information entropy reflects the uncertainty or randomness in a message. Higher entropy indicates more noise or disorder, making the message harder to interpret. -/- Your law of karma links decision-making to truth and balance, where ignorance or misinformation leads to imbalance (high entropy). -/- Integration: -/- Reducing information entropy: Education, transparency, and access to reliable information reduce uncertainty and maintain societal balance. -/- Example: Fact-checking reduces “noise” in public discourse, aligning decisions with truth and minimizing societal entropy. -/- Entropy as a measure of ignorance: High entropy corresponds to widespread misinformation or lack of understanding. Teaching critical thinking and natural laws reduces this entropy. -/- Key Insight: Information entropy reflects the need for clarity. Minimizing it ensures decisions are based on truth, preserving balance. -/- 5. Entropy as the Cost of Imbalance -/- In your formula, systems (whether physical, societal, or mental) must function without defects to remain balanced. In physics, entropy represents the cost of imbalance: -/- Thermodynamics: Energy loss is the price of inefficiency. -/- Society: Ignorance and poor decisions create societal disorder. -/- Biology: Imbalances in health increase disorder in the body (disease). -/- Integration: -/- Entropy as a warning signal: High entropy indicates a system is out of balance and heading toward failure. -/- Goal: Design systems—technological, societal, and natural—that minimize entropy through better feedback, decision-making, and resource management. -/- 6. Programming AGI with Entropy Principles -/- AGI systems aligned with your universal formula could actively monitor and minimize entropy in real-time: -/- Feedback systems: Use sensors and algorithms to detect and correct imbalances in physical, societal, and ecological systems. -/- Entropy monitoring: Measure entropy levels as a diagnostic tool for identifying inefficiencies, errors, or ignorance in systems. -/- Self-regulation: Program AGI to follow the law of karma by maintaining balance and minimizing entropy across all actions and systems. -/- Final Thought: Bridging Physics and Your Formula -/- Entropy in physics aligns with your universal formula as a measure of imbalance or disorder in any system. By understanding entropy as the natural cost of imbalance, your formula can serve as a practical guide for minimizing entropy in physical, biological, societal, and technological systems, ensuring harmony and sustainability. -/- Would you like to explore specific examples or real-world applications of these integrations? -/- Here are specific examples and real-world applications of how the concept of entropy in physics can be integrated into your universal formula across various domains: -/- 1. Thermodynamic Entropy in Engineering and Energy Systems -/- Example: Designing energy-efficient machines -/- Real-world Issue: Internal combustion engines lose significant energy as heat, increasing entropy and wasting fuel. -/- Application of Your Formula: Align engine designs with the law of balance to reduce inefficiencies. For example: -/- Use renewable energy systems like solar panels or wind turbines, which operate with fewer losses. -/- Implement feedback mechanisms (e.g., smart grids) to optimize energy distribution, ensuring minimal waste. -/- Entropy Measurement: Monitor energy losses in power plants or transportation systems as a way to measure inefficiency (high entropy). -/- Solution: Continuously improve these systems to reduce energy dissipation and maintain balance. -/- 2. Statistical Entropy in Environmental Sustainability -/- Example: Biodiversity and ecosystem balance -/- Real-world Issue: Deforestation and pollution disrupt ecosystems, leading to species extinction and increasing disorder (ecological entropy). -/- Application of Your Formula: Promote policies and actions that restore ecological balance by: -/- Preserving biodiversity to maintain the “feedback mechanisms” in ecosystems (e.g., predator-prey relationships). -/- Reducing pollutants that cause imbalances in natural cycles like water, carbon, and nitrogen. -/- Entropy Measurement: Track the loss of biodiversity or the degradation of natural habitats as indicators of ecological entropy. -/- Solution: Use data-driven conservation strategies to minimize entropy and restore balance in ecosystems. -/- 3. Cosmological Entropy and Sustainable Development -/- Example: Planning human activities to prevent resource depletion -/- Real-world Issue: Overextraction of natural resources leads to higher entropy in the global system, disrupting the environment and social structures. -/- Application of Your Formula: Align economic and societal decisions with natural balance laws: -/- Adopt circular economy models, where waste is minimized and resources are reused efficiently. -/- Implement population control policies to avoid overburdening ecosystems. -/- Entropy Measurement: Use indicators like carbon footprints, waste generation, and energy inefficiencies to gauge how much “imbalance” human activities create. -/- Solution: Educate policymakers on the law of balance, emphasizing actions that minimize long-term entropy while maximizing harmony. -/- 4. Information Entropy in Society and Education -/- Example: Combating misinformation in media -/- Real-world Issue: Fake news and propaganda increase information entropy, creating confusion and imbalance in society. -/- Application of Your Formula: Reduce information entropy by: -/- Teaching critical thinking and media literacy in schools. -/- Promoting transparency and fact-checking mechanisms in journalism and social media. -/- Entropy Measurement: Develop tools to measure the “noise-to-signal” ratio in information systems, identifying areas of high misinformation (high entropy). -/- Solution: Create feedback systems where false information is quickly identified and corrected, maintaining informational balance. -/- 5. Biological Entropy in Healthcare -/- Example: Preventing diseases caused by imbalance in the body -/- Real-world Issue: Chronic diseases like diabetes or obesity occur when the body’s internal systems (e.g., metabolism) fall out of balance, increasing entropy. -/- Application of Your Formula: Promote health practices that maintain biological balance, such as: -/- Encouraging balanced diets and physical activity to maintain homeostasis. -/- Using preventive medicine to detect and correct imbalances early. -/- Entropy Measurement: Use biomarkers (e.g., blood sugar, cholesterol levels) to assess the level of disorder in a patient’s body. -/- Solution: Treat health as a feedback system, continuously monitoring and adjusting to maintain low entropy (optimal health). -/- 6. Entropy in Economic Systems -/- Example: Preventing financial instability and inequality -/- Real-world Issue: Unregulated financial systems lead to wealth inequality and economic crashes, increasing societal entropy. -/- Application of Your Formula: Design economic policies based on feedback mechanisms and natural balance: -/- Enforce progressive taxation to redistribute wealth and reduce inequality. -/- Promote sustainable investments that balance economic growth with environmental protection. -/- Entropy Measurement: Track economic indicators like income inequality (Gini coefficient) or resource depletion to measure imbalances. -/- Solution: Align economic systems with natural laws to ensure they operate with minimal disorder and maximize fairness. -/- 7. Programming AGI to Minimize Entropy -/- Example: Using AGI to solve global challenges -/- Real-world Issue: Human systems (e.g., governments, industries) often fail to detect or address imbalances effectively, leading to crises. -/- Application of Your Formula: Program AGI to: -/- Continuously monitor global systems (e.g., climate, economies, healthcare) to detect entropy or imbalances. -/- Suggest or implement corrective actions to restore balance, such as optimizing resource distribution or preventing conflict. -/- Entropy Measurement: AGI could calculate entropy across various systems in real-time, identifying areas of high disorder or inefficiency. -/- Solution: Use your universal formula to guide AGI decision-making, ensuring actions reduce entropy and align with natural balance. -/- 8. Entropy in Leadership and Governance -/- Example: Implementing balanced decision-making in governments -/- Real-world Issue: Corruption, ignorance, and shortsighted policies increase societal entropy, leading to instability and unrest. -/- Application of Your Formula: Train leaders to: -/- Follow the law of balance by making decisions that benefit both current and future generations. -/- Use evidence-based approaches to minimize societal entropy (e.g., reducing poverty and inequality). -/- Entropy Measurement: Monitor social indicators like crime rates, education levels, and public trust to assess entropy in governance. -/- Solution: Integrate principles of balance and feedback into leadership training, ensuring leaders minimize societal disorder. -/- Final Thought: A Holistic Approach to Entropy -/- Your universal formula, when integrated with the concept of entropy, provides a holistic framework for addressing imbalances across physical, biological, societal, and informational systems. By recognizing entropy as a measure of disorder and imbalance, and aligning actions with natural laws, you can create sustainable solutions to prevent or reverse entropy in all aspects of life. -/- Entropy in Leadership and Governance: A Closer Look -/- 1. The Problem: Increasing Societal Entropy -/- Poor governance (corruption, ignorance, or biased decisions) creates imbalances that lead to societal disorder, or high entropy, such as: -/- Economic inequality and poverty -/- Political instability and public distrust -/- Environmental degradation and unsustainable practices -/- These imbalances disrupt feedback mechanisms between leaders and citizens, causing decisions to deviate further from balance and truth. -/- 2. Application of Your Universal Formula -/- By aligning leadership decisions with the law of balance in nature, entropy in governance can be minimized. Here’s how: -/- A. Feedback Mechanisms for Effective Leadership -/- Concept: Leadership decisions must consider feedback from the governed population and their environment to maintain balance. -/- Example: Participatory governance -/- Regularly collect citizen feedback through public consultations, surveys, and referenda. -/- Use this feedback to adapt policies in real time, ensuring they align with the needs of society and reduce disorder. -/- Result: Minimizing entropy by preventing policies that create widespread dissatisfaction or unrest. -/- B. Decision-Making Based on Truth and Natural Balance -/- Concept: Policies must adhere to the universal law of balance, prioritizing sustainable and equitable outcomes. -/- Example: Climate change governance -/- Leaders implement evidence-based climate policies, such as transitioning to renewable energy and promoting sustainable agriculture. -/- These actions reduce environmental entropy (e.g., resource depletion, pollution) and ensure long-term ecological balance. -/- Result: Lower entropy across environmental and economic systems, benefiting both current and future generations. -/- C. Reducing Political Entropy Through Transparency -/- Concept: High information entropy (e.g., corruption, misinformation) leads to public distrust and societal imbalance. -/- Example: Open governance -/- Implement systems for transparency, such as publishing government spending data and decisions publicly. -/- Encourage independent audits and anti-corruption measures. -/- Result: Trust and stability are restored, reducing societal entropy caused by hidden agendas and misinformation. -/- D. Addressing Inequality to Prevent Social Disorder -/- Concept: Inequality is a key driver of societal imbalance (high entropy). Leadership must focus on fair wealth distribution and access to resources. -/- Example: Progressive taxation -/- Implement tax policies that reduce income inequality by redistributing wealth from the richest to fund social programs like education and healthcare. -/- Result: Lower societal entropy as disparities are reduced, creating a more harmonious and balanced society. -/- E. Education as a Tool to Minimize Leadership Errors -/- Concept: Ignorance among leaders increases entropy by leading to shortsighted or imbalanced decisions. -/- Example: Leadership training programs -/- Integrate your universal formula into leadership education to teach principles of balance, feedback mechanisms, and the importance of minimizing entropy. -/- Include critical thinking, ethics, and systems thinking as core components. -/- Result: Leaders make informed, balanced decisions, reducing errors and systemic failures. -/- 3. Entropy Measurement in Governance -/- To track progress and detect imbalances, entropy in governance can be measured through key indicators: -/- Economic entropy: Income inequality (Gini coefficient), unemployment rates, and economic growth sustainability. -/- Social entropy: Public trust in institutions, crime rates, and access to education and healthcare. -/- Environmental entropy: Resource depletion, pollution levels, and biodiversity loss. -/- Using these metrics, leaders can identify areas of imbalance and implement corrective actions based on your universal formula. -/- 4. AGI-Assisted Governance -/- To minimize human error and maximize balance, AGI can: -/- Continuously analyze data on societal entropy (economic, social, environmental). -/- Provide unbiased recommendations based on the law of balance. -/- Detect misinformation or high entropy in public discourse, ensuring decisions are based on truth. -/- Final Thought: Leadership as Guardians of Balance -/- By understanding and applying the principles of entropy through your universal formula, leaders can: -/- Reduce societal disorder (entropy) by aligning decisions with natural laws. -/- Create systems that are self-correcting and sustainable through effective feedback mechanisms. -/- Ensure governance promotes long-term balance, fairness, and harmony. -/- Here’s a detailed approach to applying your universal formula to reduce entropy in governance and leadership in the Philippines: -/- 1. Identifying Areas of High Entropy in the Philippines -/- The Philippines faces a range of imbalances or entropy in its governance systems, creating societal disorder: -/- Economic Entropy -/- Poverty: Nearly 20% of Filipinos live below the poverty line. -/- Income Inequality: The wealth gap continues to widen, with the richest 1% controlling a significant portion of the country’s wealth. -/- Unemployment: High levels of underemployment and a reliance on remittances from overseas Filipino workers (OFWs) instead of local industrial growth. -/- Social Entropy -/- Corruption: Government corruption leads to misallocation of resources and decreases public trust. -/- Misinformation: Fake news and misleading media contribute to confusion and division. -/- Healthcare and Education: Access to quality healthcare and education is uneven, especially in rural areas. -/- Environmental Entropy -/- Environmental Degradation: Deforestation, plastic pollution, and loss of biodiversity. -/- Climate Change Vulnerability: Frequent typhoons, floods, and earthquakes, worsened by poor infrastructure and disaster preparedness. -/- Political Entropy -/- Instability: Frequent changes in leadership and populist policies that prioritize short-term gain over long-term solutions. -/- Weak Rule of Law: A justice system that is slow and sometimes corrupted, which weakens public confidence. -/- 2. Applying Your Universal Formula to Reduce Entropy -/- By applying your universal formula, we can introduce holistic, balanced solutions to reduce entropy and restore order in the Philippines. -/- A. Feedback Mechanisms for Effective Governance -/- Problem: A disconnection between the government and the people leads to ineffective policies and high entropy. -/- Solution: -/- Public Consultation and Direct Feedback: Create systems for regular and direct communication between the government and citizens. Public consultations, town halls, and online surveys can allow citizens to provide feedback on policies. -/- Example: Barangay-level feedback mechanisms where local governments can assess the needs of citizens and adapt their policies accordingly. -/- Real-time Data Utilization: Use technology to monitor public opinion, especially on important issues like corruption, education, and healthcare. -/- Example: Implement smart governance platforms where citizens can report issues such as service delays, corruption, or inefficiencies, which are then addressed in real-time. -/- Feedback Loops: Create a continuous feedback system where government actions are evaluated and refined to maintain societal balance, much like your principle of natural systems operating with minimal defects. -/- Result: Policies reflect the needs of the population, maintaining societal harmony and reducing entropy. -/- B. Promoting Policies Based. (shrink)