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)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been (...) proposed by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

According to the standard interpretation of Einstein’s field equations, gravity consists of mass-energy curving spacetime, and an additional physical force or entity—denoted by Λ (the ‘cosmological constant’)—is responsible for the Universe’s metric-expansion. Although General Relativity’s direct predictions have been systematically confirmed, the dominant cosmological model thought to follow from it—the ΛCDM (Lambda cold dark matter) model of the Universe’s history and composition—faces considerable challenges, including various observational anomalies and experimental failures to detect dark matter, dark energy, or inflation-field candidates. This (...) paper shows that Einstein’s Equivalence Principle entails two possible physical interpretations of General Relativity’s field equations. Although the field equations facially appear to support the standard interpretation—that gravity consists of mass-energy curving spacetime—the field equations can be equivalently understood as holding that gravitational effects instead result from mass-energy accelerating the metric-expansion of a second-order spacetime fabric superimposed upon an absolute, first-order Euclidean space, resulting in the observational appearance of spacetime curvature. This alternative interpretation of relativity is shown to be empirically equivalent to the standard interpretation of relativity, albeit with a changing value for Λ (which is similar to how Λ is understood in the conception of Λ as ‘quintessence’, but in this case takes Λ to be gravity). The reconceptualization is then shown to potentially resolve major observational anomalies for the ΛCDM model, including recent observations conflicting with ΛCDM predictions, as well as failures to directly detect dark matter, dark energy, and inflation field/particle candidates. (shrink)

Factory farming continues to grow around the world as a low-cost way of producing animal products for human consumption. However, many of the practices associated with intensive animal farming have been criticized by public health professionals and animal welfare advocates. The aim of this essay is to raise three independent moral concerns with factory farming, and to explain why the practices associated with factory farming flourish despite the cruelty inflicted on animals and the public health risks imposed on people. I (...) conclude that the costs of factory farming as it is currently practiced far outweigh the benefits, and offer a few suggestions for how to improve the situation for animals and people. (shrink)

Many people greet evidence of biologically based race and sex differences with extreme skepticism, even hostility. We argue that some of the vehemence with which many intellectuals in the West resist claims about group differences is rooted in the tacit assumption that accepting evidence for group differences in socially valued traits would undermine our reasons to treat people with respect. We call this theegalitarian fallacy. We first explain the fallacy and then give evidence that self-described liberals in the United States (...) are especially likely to commit it when they reason about topics like race and sex. We then argue that people should not be as worried as they often are about research that finds psychological differences between men and women, or between people of different racial or ethnic groups. We conclude that if moral equality is believed to rest on biological identity, ethnically diverse societies are in trouble. (shrink)

Abstract:Trust is important for a variety of social relationships. Trust facilitates trade, which increases prosperity and induces us to interact with people of different backgrounds on terms that benefit all parties. Trade promotes trustworthiness, which enables us to form meaningful as well as mutually beneficial relationships. In what follows, I argue that when we erect institutions that enhance trust and reward people who are worthy of trust, we create the conditions for a certain kind of moral progress.

On most accounts, beliefs are supposed to fit the world rather than change it. But believing can have social consequences, since the beliefs we form underwrite our actions and impact our character. Because our beliefs affect how we live our lives and how we treat other people, it is surprising how little attention is usually given to the moral status of believing apart from its epistemic justification. In what follows, I develop a version of the harm principle that applies to (...) beliefs as well as actions. In doing so, I challenge the often exaggerated distinction between forming beliefs and acting on them.1 After developing this view, I consider what it might imply about controversial research the goal of which is to yield true beliefs but the outcome of which might include negative social consequences. In particular, I focus on the implications of research into biological differences between racial groups. (shrink)

Antibiotic resistance is one of the most pressing public health problems humanity faces. Research into new classes of antibiotics and new kinds of treatments – including risky experimental treatments such as phage therapy and vaccines – is an important part of improving our ability to treat infectious diseases. In order to aid this research, we will argue that we should permit researchers to pay people any amount of money to compensate for the risks of participating in clinical trials, including ‘challenge (...) studies’ that involve deliberately infecting patients. We think that standard worries about paying for participation in risky research are reducible to concerns that can be addressed with the right screening mechanisms. (shrink)

Governments across the globe have squandered treasure and imprisoned millions of their own citizens by criminalising the use and sale of recreational drugs. But use of these drugs has remained relatively constant, and the primary victims are the users themselves. Meanwhile, antimicrobial drugs that once had the power to cure infections are losing their ability to do so, compromising the health of people around the world. The thesis of this essay is that policymakers should stop wasting resources trying to fight (...) an unwinnable and morally dubious war against recreational drug users, and start shifting their attention to the serious threat posed by our collective misuse of antibiotics. (shrink)

See "What's Wrong with Factory Farming?" (2015) for an updated treatment of these issues.

ABSTRACT: This essay examines and criticizes a set of Kantian objections to Parfit's attempt in Reasons and Persons to connect his theory of personal identity to practical rationality and moral philosophy. Several of Parfit's critics have tried to sever the link he forges between his metaphysical and practical conclusions by invoking the Kantian thought that even if we accept his metaphysical theory of personal identity, we still have good practical grounds for rejecting that theory when deliberating about what to do. (...) The argument between Parfit and his opponents illuminates broader questions about the relationship between our metaphysical beliefs and ourpractical reasons.RÉSUMÉ: Cet article examine et critique un ensemble d'objections kantiennes à la tentative de Parfit, dans Reasons and Persons, d'ajuster sa théorie de l'identité personnelle à la rationalité pratique et à la philosophie morale. Plusieurs des critiques de Parfit ont essayé de rompre le lien qu'il tisse entre ses conclusions métaphysiques et pratiques en évoquant l'idée kantienne selon -laquelle, même si nous acceptons sa théorie métaphysique de l'identité personnelle, il existe cependant de bonnes raisons pratiques de rejeter cette théorie lorsque nous délibérons à propos de ce que nous devons faire. Le débat entre Parfit et ses adversaires nous éclaire sur un questionnement plus large à propos du rapport entre croyance métaphysique et raison pratique. (shrink)

In recent years, bioethical discourse around the topic of ‘genetic enhancement’ has become increasingly politicized. We fear there is too much focus on the semantic question of whether we should call particular practices and emerging bio-technologies such as CRISPR ‘eugenics’, rather than the more important question of how we should view them from the perspective of ethics and policy. Here, we address the question of whether ‘eugenics’ can be defended and how proponents and critics of enhancement should engage with each (...) other. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated (...) by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

In recent years, bioethical discourse around the topic of ‘genetic enhancement’ has become increasingly politicized. We fear there is too much focus on the semantic question of whether we should call particular practices and emerging bio-technologies such as CRISPR ‘eugenics’, rather than the more important question of how we should view them from the perspective of ethics and policy. Here, we address the question of whether ‘eugenics’ can be defended and how proponents and critics of enhancement should engage with each (...) other. (shrink)

A Troublesome Inheritance, by Nicholas Wade, should be read by anyone interested in race and recent human evolution. Wade deserves credit for challenging the popular dog­ma that biological differences between groups either don't exist or cannot ex­plain the relative success of different groups at different tasks. Wade's work should be read alongside another re­cent book, The 10,000 Year Explosion: How Civilization Accelerated Human Evolution, by Gregory Cochran and Henry Harpending. Together, these books represent a ma­jor turning point in the public (...) debate about the speed with which relatively isolated groups can evolve: both books suggest that small genetic differences between members of different groups can have large impacts on their abilities and propensities, which in turn affect the outcomes of the societies in which they live. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)