Science, Logic, and Mathematics

This project aims to develop a predictive system capable of identifying crime types and predicting their occurrences based on historical crime data. The system uses advanced machine learning techniques to analyze factors such as geographic location, time, and other socio-economic variables, enabling authorities to better understand crime patterns and trends. By training models on vast datasets of past criminal activities, the system predicts not only the likely occurrence of specific crime types but also identifies high-risk locations and times, empowering law (...) enforcement to allocate resources more effectively and proactively prevent crime. Various machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), are employed to ensure accuracy and robustness. The system's predictions can significantly enhance crime management and law enforcement strategies by offering insights into crime prevention and mitigation efforts. This approach facilitates informed decision-making and fosters safer communities by providing accurate forecasts based on data-driven insights. The project underscores the potential of machine learning in public safety and law enforcement, transforming crime prediction and management from reactive to proactive. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...) as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

Usually in logic, proof systems are defined having in mind proving properties like validity and semantic consequence. It seems worthwhile to address the problem of having proof systems where satisfiability is a primitive notion in the sense that a formal derivation means that a finite set of formulas is satisfiable. Moreover, it would be useful to cover within the same framework as many logics as possible. We consider Kripke semantics where the properties of the constructors are provided by valuation constraints (...) as the common ground of those logics. This includes for instance intuitionistic logic, paraconsistent Nelson’s logic ${\textsf{N4}}$, paraconsistent logic ${\textsf{imbC}}$ and modal logics among others. After specifying a logic by those valuation constraints, we show how to induce automatically and from scratch an existential proof system for that logic. The rules of the proof system are shown to be invertible. General results of soundness and completeness are proved and then applied to the logics at hand. (shrink)

In this paper, we argue that the usual approach to modelling knowledge and belief with the necessity modality $\Box $ does not produce intuitive outcomes in the framework of the Belnap–Dunn logic ($\textsf{BD}$, alias $\textbf{FDE}$—first-degree entailment). We then motivate and introduce a nonstandard modality $\blacksquare $ that formalizes knowledge and belief in $\textsf{BD}$ and use $\blacksquare $ to define $\bullet $ and $\blacktriangledown $ that formalize the unknown truth and ignorance as not knowing whether, respectively. Moreover, we introduce another modality (...) $\textbf{I}$ that stands for factive ignorance and show its connection with $\blacksquare $. We equip these modalities with Kripke-frame-based semantics and construct a sound and complete analytic cut system for $\textsf{BD}^{\blacksquare }$ and $\textsf{BD}^{\textbf{I}}$—the expansions of $\textsf{BD}$ with $\blacksquare $ and $\textbf{I}$. In addition, we show that $\Box $ as it is customarily defined in $\textsf{BD}$ cannot define any of the introduced modalities, nor, conversely, neither $\blacksquare $ nor $\textbf{I}$ can define $\Box $. We also demonstrate that $\blacksquare $ and $\textbf{I}$ are not interdefinable and establish the definability of several important classes of frames using $\blacksquare $. (shrink)

We show that both the $n$-density and the bounded $n$-width of Kripke frames can be modally defined not only with natural and well-known Sahlqvist formulae containing a linear number of different propositional variables but also with formulae of polynomial length with a logarithmic number of different propositional variables and then we prove that this exponential decrease in the number of variables leads us outside the class of Sahlqvist formulae.

In this paper, we introduce the variety of $I$-modal ririgs. We characterize the congruence lattice of its members by means of $I$-filters, and we provide a description of $I$-filter generation. We also provide an axiomatic presentation for the variety generated by chains of the subvariety of contractive $I$-modal ririgs. Finally, we introduce a Hilbert-style calculus for a logic with $I$-modal ririgs as an equivalent algebraic semantics and we prove that such a logic has the parametrized local deduction-detachment theorem.

This paper is intended first for the formal argumentation community (see https://comma.csc.liv.ac.uk/). This community develops logics and systems modelling argumentation and dialogues. The community is in search of major applications areas for their models. One such application area e.g. is Law. The message of this paper is that there is another major application area for formal argumentation. There is an international community of sex offender therapist that is well established and well funded, and their therapy methods use (methods that can (...) be modelled by) formal argumentation and logic. This community presents a natural application area for formal argumentation. We thus describe in this paper how the sex offender therapists work, to give the formal argumentation researcher a view of this application area. What is especially important about this application area is that in order to model it and learn from it, the formal argumentation community have to evolve their formal methods and adapt to this new application. Part of this enhancement is to modify and import certain methods from other areas of Logic e.g. from Non-Monotonic logic. The members of the formal argumentation community are not familiar, on average, with other areas of logic, and so we also describe in this paper, what we need from neighbouring logics. This makes this paper of interest also to sex offender therapist as well. They may be already familiar with their own practices, but the additional logics described will be of interest to them. (shrink)

It is well-known that intuitionistic logics can be formalized by means of Heyting algebras, i.e. relatively pseudocomplemented semilattices. Within such algebras the logical connectives implication and conjunction are formalized as the relative pseudocomplement and the semilattice operation meet, respectively. If the Heyting algebra has a bottom element |$0$|⁠, then the relative pseudocomplement with respect to |$0$| is called the pseudocomplement and it is considered as the connective negation in this logic. Our idea is to consider an arbitrary meet-semilattice with |$0$| (...) satisfying only the Ascending Chain Condition (these assumptions are trivially satisfied in finite meet-semilattices) and introduce the operators formalizing the connectives negation |$x^{0}$| and implication |$x\rightarrow y$| as the set of all maximal elements |$z$| satisfying |$x\wedge z=0$| and as the set of all maximal elements |$z$| satisfying |$x\wedge z\leq y$|⁠, respectively. Such a negation and implication is ‘unsharp’ since it assigns to one entry |$x$| or to two entries |$x$| and |$y$| belonging to the semilattice, respectively, a subset instead of an element of the semilattice. Surprisingly, this kind of negation and implication still shares a number of properties of these connectives in intuitionistic logic, in particular the derivation rule Modus Ponens. Moreover, unsharp negation and unsharp implication can be characterized by means of five, respectively seven simple axioms. We present several examples. The concepts of a deductive system and of a filter are introduced as well as the congruence determined by such a filter. We finally describe certain relationships between these concepts. (shrink)

The product of two $\textbf {Alt}$ logics possesses the polynomial product finite model property and its membership problem is $\textbf {coNP}$-complete. Using a reduction from an undecidable domino-tiling problem, we prove that its admissibility problem is undecidable.

Howson (2000) contends that the No-Miracles argument fails as an argument in support of scientific realism because it commits the base-rate fallacy. Psillos (2009) has defended the argument by appealing to cases that involve conditional probabilities but where base-rate information can properly be ignored. I show that Psillos’s defense of the No-Miracles argument is inadequate and that the prospects for a purely probabilistic formulation of the argument are dim. I end by considering whether interpreting the argument as an inference to (...) the best explanation might better serve the scientific realist, concluding that such an approach would limit the realist’s ability to effectively counter the Pessimistic Induction. (shrink)

In the field of Science and Technology Studies (STS), scientific controversies (SCs) are often viewed as essential steps in the development of knowledge and technoscientific artifacts. This paper addresses the lack of an orchestrated epistemological effort to assess the quality of knowledge claims (KCs) articulated during SCs. This study proposes a framework that bridges ontology with appropriate methodological techniques to evaluate KCs, considering them as ontological hybrids in which the social and epistemic are interwoven. This paper argues for the rehabilitation (...) of the existential autonomy of the natural world, which has been overshadowed by an overreaction to positivism. By employing methodological bracketing, specifically that of positive and normative analyses, this study aims to assess KCs in terms of their proximity to intransitive reality and the socio-political dynamics that influence their emergence respectively. The second part of the paper applies this framework to the SC on the origins of the COVID-19 pandemic, suggesting a nuanced understanding that goes beyond the dichotomy of zoonosis and genetic engineering. This study highlights the impact of social factors on scientists’ epistemic stances, and the importance of public deliberation in policy formation. The contributions of this study are both epistemological and policy-oriented. (shrink)

Museums and exhibitions of science and technology have received considerable attention in recent historiography. However, little has been done to look beyond individual localities and national borders. Using Yehuda Elkana’s concept of “images of knowledge,” this article shows how a comparison of four interwar projects located across Europe - in Czechoslovakia, Germany, the Soviet Union, and Switzerland - helps to highlight commonalities in the understanding of science at the time. Although these exhibition projects were located in different political systems and (...) their proponents superficially presented irreconcilable ideas about science, their ideas about progress or the need to overcome national and disciplinary boundaries show a preponderance of similarities. However, the political-ideological divide is visible in the way they conceive of the relationship between science and society. Finally, by looking at how images of knowledge were co-produced at the interface of science, architecture, and politics, this article argues for a broadening of the source base to include non-scientific and non-human actors. (shrink)

Appealing to science is a popular suggestion for separating projectible predicates. According to this suggestion, we can expect science, eventually, to separate such predicatesfor us, rendering it unnecessary to make further attempts to explicate the criteria for pro-jectibility. In this essay, I address three theoretical challenges to this suggestion. The firststems from the inductive character of science, which casts doubts on its efficacy in separat-ing projectible predicates, since induction itself requires this separation. The second is theinferential externalism implied by this (...) suggestion, whereas the problems of induction havebite only if inferential internalism is presumed. The third challenge appears when a strongrelationship between projectibility and kindhood, on the one hand, and kindhood and sim-ilarity, on the other, is posited, such that a more projectible predicate is believed to be apredicate which tracks better similarities in nature. Now the question appears: whether andhow science enables us to track better similarities? I distinguish two conceptions of similarity,one intuitive, the other theoretical, and I argue that the theoretical one is to be preferred,showing that how scientific practice involves shifting from an intuitive idea of similarity to atheoretical one. Through answering these three challenges, I attempt to support the appealto natural science as far as Goodman’s problem of induction is concerned. (shrink)

This project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by first capturing (...) an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits. (shrink)

Bird species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising (...) diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. The model architecture is designed to extract intricate features, enabling accurate identification even in challenging scenarios such as varying lighting conditions, occlusions, or similar species appearances. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...) aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled. (shrink)

A function is boundedly finite-to-one if there is a natural number $k$ such that each point has at most $k$ inverse images. In this paper, we prove in $\textsf{ZF}$ (i.e. the Zermelo–Fraenkel set theory without the axiom of choice) several results concerning this notion, among which are the following: (1)For each infinite set $A$ and natural number $n$, there is no boundedly finite-to-one function from $\mathcal{S}(A)$ to $\mathcal{S}_{\leq n}(A)$, where $\mathcal{S}(A)$ is the set of all permutations of $A$ and $\mathcal{S}_{\leq (...) n}(A)$ is the set of all permutations of $A$ moving at most $n$ points. (2) For each infinite set $A$, there is no boundedly finite-to-one function from $\mathcal{B}(A)$ to $\textrm{fin}(A)$, where $\mathcal{B}(A)$ is the set of all partitions of $A$ such that every block is finite and $\textrm{fin}(A)$ is the set of all finite subsets of $A$. (shrink)

A conflict between evidence for causation and the metaphysical requirement of spatiotemporal distinct causal relata arises if the results of experiments commonly described as ‘bottom-up’ and ‘top-down’ are taken to demonstrate ontological determination dependencies between parts and wholes or their respective behaviours. It has been argued that the problem can be circumvented if experimental results are interpreted in terms of relationships between variables measured and manipulated at separating scales. I argue that scale separation fails in the case of biological phenomena (...) due to the small number of molecules and molecular events underlying these phenomena, which makes it impossible to completely disentangle micro and macro-scale behaviours. I defend instead a causal mediation interpretation according to which experiments assess the effects of interventions targeting the composition and exposure of biological systems. Under this interpretation, causal relata are ontologically distinct fractions or parts of biological systems, thus avoiding the oddity of part-whole causation. (shrink)

Sri Harmandir Sahib, commonly known as the Golden Temple, stands as a profound emblem of spiritual and artistic excellence in Amritsar, Punjab, India. This revered Sikh shrine epitomizes the fundamental principles of Sikhism, equality, humility, and unity while showcasing a remarkable blend of architectural innovation and intricate artistry.

The examination of anomaly process detection using negative selection algorithms and classification techniques focuses on enhancing the ability to identify deviations from expected patterns within complex data sets. Negative selection algorithms, inspired by biological immune systems, offer a novel approach to anomaly detection by efficiently distinguishing between normal and anomalous data points. When combined with various classification techniques, these algorithms can improve the accuracy and robustness of anomaly detection systems. This abstract explores the integration of negative selection algorithms with traditional (...) and advanced classification methods to optimize anomaly detection processes. By leveraging these combined approaches, the study aims to address challenges such as false positives, detection latency, and adaptability to diverse data environments. The findings suggest that the synergy of negative selection algorithms and classification techniques can lead to more precise and reliable detection of anomalies, providing valuable insights for applications across cybersecurity, finance, healthcare, and other critical fields. (shrink)

This paper will consider how the account of weak emergence presented by Wilson in the book Metaphysical emergence (2021) can be used to explore the relation between biochemical functions and chemical structure in biochemical molecules, as vitamin B12. The structure of the paper is the following. Section 2 will introduce why biochemical functions are interesting from a philosophical perspective and why their relation to molecular structure can be seen as problematic. In doing so, it will consider the definition of biochemical (...) functions as in Bellazzi (2022) for which they can be seen as sets of chemical dispositional properties that contribute to biological processes. Section 3 will explore how, given this definition of biochemical functions, we can interpret the relation between chemical structure and biochemical structure via weak emergence. Section 4 concludes. (shrink)

This paper presents the design and implementation of a pattern-based J2EE application development environment aimed at streamlining the development process, enhancing code quality, and improving maintainability. We begin with an exploration of the fundamental principles of J2EE and the role of design patterns in software engineering. This is followed by a detailed discussion on the selection and application of various design patterns specifically tailored for J2EE, such as Singleton, Factory, Data Access Object (DAO), and Model-View-Controller (MVC).