Numerous agricultural wastes are impractically discarded every day, and one of these is rice bran. This study investigated the production of a biodegradable straw made of cassava starch and powdered rice bran. It aimed to determine the effectiveness of the different treatments of Cassava Starch-Rice Bran in terms of water resistance, tensile strength, and biodegradability. An experimental design was used in conducting the study. There were three treatments made in making CSRB straws: the first, with more rice bran; the second, (...) with the same cassava starch and rice bran ratio; and the third, with more cassava starch. These treatments produced three replicates each. The straws were placed in a dehydrating machine for 24 hours at a temperature of 60° Celsius. The straws were then coated with beeswax after being removed from their respective molders. The CSRB straws were found to be water resistant due to the beeswax coating applied, although it had a weak tensile strength even with the presence of beeswax. The rate of biodegradability of the CSRB straw was fast due to it being made up of organic materials. The results revealed that straws made from Cassava Starch and Powdered Rice Bran are effective in terms of water resistance and biodegradability. (shrink)

Some philosophers claim that perception immediately and prima facie justifies belief in virtue of its phenomenal character (Huemer, Skepticism and the veil of perception. Rowman & Littlefield, Lanham, 2001; Pryor, There is immediate justification. In: Steup M, Sosa E (eds) Contemporary debates in epistemology. Blackwell, London (2014), pp. 181–202, 2005). To explain this special justificatory power, some appeal to perception’s presentational character: the idea that perceptual experience presents its objects as existing here-and-now (Chudnoff, Intuition. Oxford University Press, Oxford, 2013; Berghofer, (...) Husserl Stud 34(2):145–170, 2018). As some philosophers have noted, if perception justifies in virtue of its presentational character alone, the kind of content perception has should not matter for perceptual justification; more precisely, it should not matter whether perceptual content is propositional or not (e.g., Smithies, The epistemic role of consciousness. Oxford University Press, Oxford, 2019; Kriegel, Australas J Philos, 2021). However, most philosophers tend to resist this conclusion, holding that perception must be propositional in order to justify, on the ground that all our model of justification are propositional (e.g., Gauker, Philos Perspect 26(1):19–50, 2012). This paper challenges this claim. The paper consists of a negative and a positive part. In the negative part, I discuss and reject the master argument for the propositionality of perception; the conclusion is that propositional content is neither sufficient nor necessary to explain perception’s justificatory power. In the positive part, I take this conclusion seriously and outline an objectual model of perceptual justification. I define objectual attitudes as mental states whose content is not a full proposition, but a sub-propositional representational item, such as the representation of objects, properties, and kinds (Grzankowski and Montague, Non-propositional intentionality. Oxford University Press, Oxford, 2018), and show that it is plausible that some perceptions and some beliefs are attitudes of this kind. I then argue that objectual perceptual experiences have the right kind of phenomenal character and the right kind of structure to serve for immediate prima facie justification. I conclude by defending my objectual model from three objections. (shrink)

Open peer commentary on the article “Varela’s Radical Proposal: How to Embody and Open Up Cognitive Science” by Kristian Moltke Martiny. Upshot: I examine Varela’s relationship with Husserl’s phenomenology, highlighting Varela’s acknowledgment of the pragmatic dimension of its phenomenological reduction. I argue that Varela sees, in some developments of phenomenology, a deconstruction of the subject-object duality and an embodied view of the mind. I also highlight the existential dimension of Varela’s radical proposal, which contributes to further opening up (...) and embodying cognitive science. (shrink)

This paper explores the scope and limits of rational consensus through mutual respect, with the primary focus on the best known formal model of consensus: the Lehrer–Wagner model. We consider various arguments against the rationality of the Lehrer–Wagner model as a model of consensus about factual matters. We conclude that models such as this face problems in achieving rational consensus on disagreements about unknown factual matters, but that they hold considerable promise as models of how to rationally resolve non-factual disagreements.

We consider emergence from the perspective of dynamics: states of a system evolving with time. We focus on the role of a decomposition of wholes into parts, and attempt to characterize relationships between levels without reference to whether higher-level properties are “novel” or “unexpected.” We offer a classification of different varieties of emergence, with and without new ontological elements at higher levels. -/- Submitted to a volume on Real Patterns (Tyler Milhouse, ed.), to be published by MIT Press.

The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the GSM network system. The system uses various sensors to detect fire, smoke, and gas, then transmits the message using GSM module. After the message, send by the module the help arrives in 15 minutes. The (...) system is associated with water pump and a sprinkler. The Water system is associated with a water tank and a sprinkler. The water tank uses a Ultrasonic sensor to monitor the level of water level in the tank for refilling the water tank. Once fire is detected, it automatically sends the emergency message and sprinkles the water. (shrink)

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

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Psychokinesis is the ability to affect objects without using physical forces. In Mulla Sadra's philosophy, this ability is rooted in the imagining of the soul. In quantum physics, the concepts of the observer's role and quantum entanglement provide potential explanations for this phenomenon. In this paper, we consider a comparative analysis of Mulla Sadra's philosophy and Bohmian quantum interpretation, highlighting the affinity and mutuality of these two approaches. Both approaches are based on causality and accept the role of the mind (...) in doing things. While Mulla Sadra posits that the ability to influence objects and their physical movements is linked to the strong imagining of the human soul, Bohmian quantum physics suggests that the entanglement of the observer's mind with the inner level of phenomena (worse) enables the possibility of psychokinesis. It appears that relying solely on a physical approach to explain miracles, which represent a specific subset of psychokinesis, is neither accurate nor effective. (shrink)

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

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Foundational ontologies, central constructs in ontological investigations and engineering alike, are based on ontological categories. Firstly proposed by Aristotle as the very ur- elements from which the whole of reality can be derived, they are not easy to identify, let alone partition and/or hierarchize; in particular, the question of their number poses serious challenges. The late medieval philosopher Dietrich of Freiberg wrote around 1286 a tutorial that can help us today with this exceedingly difficult task. In this paper, I discuss (...) ontological categories and their importance for foundational ontologies from both the contemporary perspective and the original Aristotelian viewpoint, I provide the translation from the Latin into English of Dietrich's De origine II with an introductory elaboration, and I extract a foundational ontology–that is in fact a single-category one–from this text rooted in Dietrich's specification of types of subjecthood and his conception of intentionality as causal operation. (shrink)

Ontologies are some of the most central constructs in today's large plethora of knowledge technologies, namely in the context of the semantic web. As their coinage indicates, they are direct heirs to the ontological investigations in the long Western philosophical tradition, but it is not easy to make bridges between them. Contemporary ontological commitments often take causality as a central aspect for the ur-segregation of entities, especially in scientific upper ontologies; theories of causality and philosophical ontological investigations often go hand-in-hand, (...) and were essentially inseparable in medieval thought. This constitutes the foundation for a bridge, and this article analyzes the causality-based ontology of the late medieval philosopher Dietrich of Freiberg from the viewpoint of today's upper-ontology engineering. In this bridging attempt, it offers a translation into English of the first part of Dietrich's De origine (abbreviated title) that is a compromise between traditional scholarly translations of medieval Latin philosophical texts and contemporary ontology. (shrink)

Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection (...) system's decision-making process. (shrink)

The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

Many say that ontological disputes are defective because they are unimportant or without substance. In this paper, we defend ontological disputes from the charge, with a special focus on disputes over the existence of composite objects. Disputes over the existence of composite objects, we argue, have a number of substantive implications across a variety of topics in metaphysics, science, philosophical theology, philosophy of mind, and ethics. Since the disputes over the existence of composite objects have these substantive implications, they are (...) themselves substantive. (shrink)

The paper also discusses the technical architecture, including the network protocols, data management strategies, and user interface design considerations necessary to implement such a system. Additionally, it addresses the challenges related to data security, privacy, and system interoperability. Finally, the paper outlines potential future enhancements, such as the incorporation of AI-driven predictive analytics and advanced AR features, to further elevate the smart home experience.

How may we change ourselves and our society so that animals are treated more justly? To answer this question, I turn to the account of moral change developed by the philosopher Iris Murdoch. The chief obstacle to becoming better, she believed, is an attachment to fantasy, from which we are liberated only through a loving attention directed at the reality of other beings. Building on this account, I argue that human supremacy is one such fantasy—that it acts as an impediment (...) to interspecies justice. I end with a meditation on love as liberatory practice. (shrink)

Knowledge representation (KR) is actually more than representation: It involves also inference, namely inference of “new” knowledge, i.e. new facts. Logic programming is a suitable KR medium, but more often than not discussions on this programming paradigm focus on aspects other than KR. In this paper, I elaborate on the general theory of logic programming and give the essentials of two of its main implementations, to wit, Prolog and Datalog, from the viewpoint of deductive computing over knowledge bases, which includes (...) deductive programming. (shrink)

For millennia, knowledge has eluded a precise definition. The industrialization of knowledge (IoK) and the associated proliferation of the so-called knowledge communities in the last few decades caused this state of affairs to deteriorate, namely by creating a trio composed of data, knowledge, and information (DIK) that is not unlike the aporia of the trinity in philosophy. This calls for a general theory of knowledge (ToK) that can work as a foundation for a science of knowledge (SoK) and additionally distinguishes (...) knowledge from both data and information. In this paper, I attempt to sketch this generality via the establishing of both knowledge structures and knowledge systems that can then be adopted/adapted by the diverse communities for the respective knowledge technologies and practices. This is achieved by means of a formal–indeed, mathematical–approach to epistemological matters a.k.a. formal epistemology. The corresponding application focus is on knowledge systems implementable as computer programs. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. (shrink)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

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. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Not focusing on the history of classical logic, this book provides discussions and quotes central passages on its origins and development, namely from a philosophical perspective. Not being a book in mathematical logic, it takes formal logic from an essentially mathematical perspective. Biased towards a computational approach, with SAT and VAL as its backbone, this is an introduction to logic that covers essential aspects of the three branches of logic, to wit, philosophical, mathematical, and computational.

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by modern (...) applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...) is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

Diabetes is a major public health issue that affects the nations of our time to a large extent and is described as a non-communicable epidemic. Diabetes mellitus is a common disease where there is too much sugar (glucose) floating around in your blood. This occurs because either the pancreas can’t produce enough insulin or the cells in body have become resistant to insulin. The concentration in this paper is on diagnosis diabetes by designing a proposed expert system. The main goal (...) of this expert system is to get the appropriate diagnosing of the illness, dealing with it quickly, and tips for permanent treatment whenever possible is given out. SL5 object expert system language was used for designing and implementing the proposed expert system. (shrink)

In recent decades much literature has been produced on disagreement; the puzzling conclusion being that epistemic disagreement is, for the most part, either impossible (e.g. Aumann (Ann Stat 4(6):1236–1239, 1976)), or at least easily resolvable (e.g. Elga (Noûs 41(3):478–502, 2007)). In this paper I show that, under certain conditions, an equally puzzling result arises: that is, disagreement cannot be rationally resolved by belief updating. I suggest a solution to the puzzle which makes use of some of the principles of Hintikka’s (...) Socratic epistemology. (shrink)

Breaking with the orthodoxy, Compassionate Conservationists have taken issue with the way that individual wild animals are routinely sacrificed for the sake of species preservation or for the good of the ecosystem. Though explicitly aligning themselves with virtue ethics, there has been some confusion about what this means in practice. How is the perfectly compassionate person to act when the choice is between intentionally harming animals and protecting biodiversity? And what if the choice is between direct and indirect harm to (...) animals? Some critics suggest that when faced with these kinds of conflicts of value, Compassionate Conservationists will invariably base their choices either on the arbitrary feelings an action elicits in them, or else revert back to the decision-making procedures that characterize Traditional Conservation. In response, I argue (first) that these critics fail to appre- ciate that compassion often plays an essential role in moral deliberation. Second, that being compassionate matters even if it does not end up having a discernable effect on what one chooses to do. When one is faced with a tragic predicament in which harming individual animals is unavoidable, for instance, it matters here that one respond with an appropriate compassion. Third, that even when a situa- tion’s difficulty requires one to appeal to the impersonal norms of justice, here too it is required that one proceed with compassion. I end by illustrating these points through the close consideration of a case study: New Zealand’s controversial preda- tor extermination campaign. (shrink)

The purpose of this paper is to consider whether it is permissible for a liberal democratic state to deny anti-liberal-democratic citizens and groups the right to run for parliament. My answer to this question is twofold. On the one hand, I will argue that it is, in principle, permissible for liberal democratic states to deny anti-liberal-democratic citizens and groups the right to run for parliament. On the other hand, I will argue that it is rarely wise (or prudent) for ripe (...) democracies to exclude anti-liberal-democrats from parliamentary elections. There are at least two reasons for this. The first is related to the inherent stability of just institutions. The second is that exclusion can lead to group polarization and enclave deliberation that can engender political extremism and impair processes of interpersonal and intrapersonal deliberation in liberal democracies. (shrink)

The van Wijngaarden grammars are two-level grammars that present many interesting properties. In the present article I elaborate on six of these properties, to wit, (i) their being constituted by two grammars, (ii) their ability to generate (possibly infinitely many) strict languages and their own metalanguage, (iii) their context-sensitivity, (iv) their high descriptive power, (v) their productivity, or the ability to generate an infinite number of production rules, and (vi) their equivalence with the unrestricted, or Type-0, Chomsky grammars.

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)

Over the past fifteen years there has been a considerable amount of debate concerning what theoretical population dynamic models tell us about the nature of natural selection and drift. On the causal interpretation, these models describe the causes of population change. On the statistical interpretation, the models of population dynamics models specify statistical parameters that explain, predict, and quantify changes in population structure, without identifying the causes of those changes. Selection and drift are part of a statistical description of population (...) change; they are not discrete, apportionable causes. Our objective here is to provide a definitive statement of the statistical position, so as to allay some confusions in the current literature. We outline four commitments that are central to statisticalism. They are: 1. Natural Selection is a higher order effect; 2. Trait fitness is primitive; 3. Modern Synthesis (MS)-models are substrate neutral; 4. MS-selection and drift are model-relative. (shrink)

Expressiveness and decidability are two core aspects of programming languages that should be thoroughly known by those who use them; this includes knowledge of their metalanguages a.k.a. formal grammars. The van Wijngaarden grammars (WGs) are capable of generating all the languages in the Chomsky hierarchy and beyond; this makes them a relevant tool in the design of (more) expressive programming languages. But this expressiveness comes at a very high cost: The syntax of WGs is extremely complex and the decision problem (...) for the generated languages is generally unsolvable. With this in mind, I provide here a short primer of the syntax of WGs, which includes syntactic restrictions that guarantee decidability for the corresponding generated languages. (shrink)

Abstract: Ensuring the proactive detection of transaction risks is paramount for financial institutions, particularly in the context of managing credit scores. In this study, we compare different machine learning algorithms to effectively and efficiently. The algorithms used in this study were: MLogisticRegressionCV, ExtraTreeClassifier,LGBMClassifier,AdaBoostClassifier, GradientBoostingClassifier,Perceptron,RandomForestClassifier,KNeighborsClassifier,BaggingClassifier, DecisionTreeClassifier, CalibratedClassifierCV, LabelPropagation, Deep Learning. The dataset was collected from Kaggle depository. It consists of 164 rows and 8 columns. The best classifier with unbalanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%,Recall100.0% and the F1-score (...) 100.0%. However, the best classifier with balanced dataset was the LogisticRegressionCV. The Accuracy 100.0%, precession 100.0%, Recall 100.0% and the F1-score 100.0%. (shrink)

The aim of this study was to identify the obstacles facing the application of knowledge management and its impact on performance at Palestine Technical University-Kadoorei from the point of view of employees and to detect the differences between the average views of the study sample on the subject of the study according to some variables such as (gender, nature of work, Education Level, specialization, years of experience). The study followed the descriptive analytical method and the questionnaire as a tool for (...) study. It was distributed to 74 employees. After the questionnaire was distributed, the data was collected and coded and entered into the computer and processed statistically using the SPSS program. The study found that the percentage of approval of the obstacles to the application of knowledge management at Palestine Technical University-Kadoorei differed between few and large, and the relative weight of the axis was complete (68.2). The degree of awareness of the workers of the reality of performance excellence at the University of Palestine Technical-Kadoorei was between medium and very large. There is an impact of the constraints of the application of knowledge management on performance excellence at Palestine Technical University-Kadoorei. In light of the results of the previous study, the researchers recommended that the importance of knowledge management should be taken into account in order to raise the university's reputation and reputation at home and abroad and improve its services to students and the local community. And to promote the exchange of experiences and knowledge with local, regional and international universities in order to enhance the knowledge and preserve it and provide modern and sophisticated scientific techniques and use them in administrative and academic work at the university. (shrink)

In several recent contributions to the growing literature on slurs, Hedger draws upon Kaplan's distinction between descriptive and expressive content to argue that slurs are expressions with purely expressive content. The distinction between descriptive and expressive content and the view that slurs are expressions with purely expressive content has been widely acknowledged in prior work, and Hedger aims to contribute to this tradition of scholarship by offering novel arguments in support of his "pure expressivist" account of slurs. But the account (...) that PE offers is explanatorily inadequate, resting on suspect a priori intuitions which also commit one to denying many basic facts about slurs, such as that slurs largely display systematic differential application and that slurs can be used non-offensively between in-group speakers. In this article I provide clear reasons for rejecting PE, arguing particularly against Hedger as one of PE’s most explicit and recent proponents. In showing that PE is inadequate in at least 11 ways, I argue in favor of a mixed or hybrid approach. (shrink)

In this article, we identify three key design dimensions along which cryptocurrencies differ -- privacy, censorship-resistance, and consensus procedure. Each raises important normative issues. Our discussion uncovers new ways to approach the question of whether Bitcoin or other cryptocurrencies should be used as money, and new avenues for developing a positive answer to that question. A guiding theme is that progress here requires a mixed approach that integrates philosophical tools with the purely technical results of disciplines like computer science and (...) economics. Note: this article is the second entry within a two-part sequence on the Philosophy, Politics, and Economics of Cryptocurrency. (shrink)

Abstract: The rapid advancement of artificial intelligence (AI) has catalyzed significant developments in autonomous systems, which are increasingly shaping diverse sectors including transportation, robotics, and industrial automation. This paper explores the evolution of AI technologies that underpin these autonomous systems, focusing on their capabilities, applications, and the challenges they present. Key areas of discussion include the technological innovations driving autonomy, such as machine learning algorithms and sensor integration, and the practical implementations observed in autonomous vehicles, drones, and robotic systems. Additionally, (...) the paper addresses critical challenges including safety, ethical concerns, and regulatory issues that influence the deployment and acceptance of autonomous technologies. By examining current trends and future prospects, this research aims to provide a comprehensive overview of how AI is transforming the landscape of autonomous systems and to identify key areas for future research and development. (shrink)

Contemporary studies in unconscious cognition are essentially founded on dissociation, i.e., on how it dissociates with respect to conscious mental processes and representations. This is claimed to be in so many and diverse ways that one is often lost in dissociation. In order to reduce this state of confusion we here carry out two major tasks: based on the central distinction between cognitive processes and representations, we identify and isolate the main dissociation paradigms; we then critically analyze their key tenets (...) and reported findings. (shrink)

The problem of evil is broadly considered to be one of the greatest intellectual threats to traditional brands of theism. And William Rowe’s 1979 formulation of the problem in “The Problem of Evil and Some Varieties of Atheism” is the most cited formulation in the contemporary philosophical literature. In this paper, we explore how the tools and resources of experimental philosophy might be brought to bear on Rowe’s seminal formulation, arguing that our empirical findings raise significant questions regarding the ultimate (...) success of Rowe’s argument. Such a result would be quite notable within philosophy of religion, since this is considered one of the most formidable arguments against theism. However, further testing is needed before any firm conclusions can be drawn. -/- In section 1, we elucidate Rowe’s formulation of the problem of evil and the intuitions that seem to underwrite it. In section 2, we explore how the tools and resources of experimental philosophy might be brought to bear on Rowe’s formulation, outlining our hypotheses and our methods for testing them before showcasing our results. In section 3, we discuss the philosophical import of our results–arguing that our results, when taken together, pose an initial challenge to Rowe’s seminal argument. (shrink)