The development of a Virtual Sign Language Translator represents a significant advancement in facilitating communication between hearing and non-hearing individuals, particularly in educational and business settings. This initiative aims to bridge the communication gap by providing real-time translation of sign language into text or speech, enhancing accessibility and inclusivity. The system utilizes advanced technologies such as computer vision, machine learning, and natural language processing to accurately interpret sign language gestures. Users can interact through intuitive interfaces, enabling seamless conversion of sign (...) language into readable or audible formats. By integrating this translator into classrooms, universities, institutions, and business environments, we can promote equal participation and understanding, ensuring that information is accessible to all, regardless of hearing ability. (shrink)

Mental health issues among children and adolescents have become a global public health concern, exacerbated by factors like academic pressure, social comparison, and the post-pandemic impact. This project aims to develop a software-based mental health surveillance platform to assess and track the psychological well-being of students, offering early detection and personalized intervention recommendations. Built using Python, HTML, CSS, JavaScript, and Flask, the system leverages AI and machine learning, including XGBoost for classification and KMeans Clustering for grouping students based on mental (...) health data such as academic performance, attendance, behavior, and mood. The platform predicts risk levels (“Low Risk,” “Moderate Risk,” “High Risk”) and suggests timely interventions for counselors, educators, and parents. The system is accessible via a secure web portal and does not require high-end infrastructure, making it suitable for schools with varying economic backgrounds. Data privacy and ethical AI standards are prioritized to ensure secure, anonymized data storage. The ultimate goal is to create a scalable solution for early intervention, contributing to emotionally resilient youth in line with India’s National Education Policy (NEP) and digital health initiatives. (shrink)

Chrome Extension refers to a software program built using simple web technologies like HTML, CSS, JavaScript which provides customization and personalization of browser experience for users. We have built a chrome extension that extends web browsers functionalities & features. Our Extension will provide a list of tech stacks or technographic segmentation and information, a web page has to offer, just with a single click.

Real Estate Management System – is an Estate Agent and Property Management System is a user friendly contact and property manager for real estate professionals. Save time and sell more by empowering to easily keep track of leads, manage listings, and market to new prospects. Real Estate Management System is complete end to end solution to cover all aspects of Estate Agent day to day activity and Property buying selling procedure for small and large organization. The basic objective of developing (...) this project is Maintain client details line contact details, required property details, client type like residential and commercial client. Price limit. Preference. Maintain property details, registration of property for sale includes property address, property description, price, facilities available. Store property floor plan, property documents. Creation of thumbnail of property images for brochure System has powerful logical access management in place, each user must be identified by login id and strict password policy is applied to secure the system. The platform allows sellers to register properties with detailed descriptions and multimedia, while buyers can search, filter, and inquire about available listings based on preferences such as location, price, and type. Administrators manage users, property approvals, feedback, and overall platform integrity. Built using PHP for backend logic and MySQL for database storage, the system features a secure, user-friendly interface developed with HTML, CSS, JavaScript, and Bootstrap. (shrink)

Drought is a critical environmental issue that affects agriculture, water resources, and ecosystems. Traditional drought monitoring methods rely on ground-based meteorological observations, which have limited spatial coverage and do not provide real-time assessments. This project aims to develop a Vegetative Drought Prediction System by integrating Vegetation Condition Index (VCI) data from the ISRO VEDAS VCI Dashboard, remote sensing indices (NDVI), meteorological drought indicators (SPI, PDSI), and machine learning algorithms (Random Forest, SVM, LSTM) to accurately detect and predict drought conditions. The (...) system utilizes VCI data from the ISRO VEDAS portal, which provides high-resolution drought monitoring for India. Additionally, satellite imagery from MODIS, Sentinel-2, and Landsat-8 is used to compute NDVI (Normalized Difference Vegetation Index) to assess vegetation health. Meteorological indices, such as the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), are used to evaluate precipitation deficits and long-term drought trends. Machine learning models further enhance drought prediction. Random Forest (RF) and Support Vector Machine (SVM) classify drought severity based on VCI, NDVI, temperature, and rainfall data. Long Short-Term Memory (LSTM) networks analyze time-series data to forecast future drought conditions. These models are trained on historical drought records and meteorological data to improve accuracy. A web-based application is developed to visualize drought conditions, enabling users to select a region, analyze VCIbased drought indices, and receive real-time predictions. The frontend is built using HTML, CSS, and JavaScript (React.js), while the backend is implemented using Flask/Django, with PostgreSQL/MongoDB for data storage. This project provides an efficient, scalable, and cost-effective solution for drought monitoring using ISRO VEDAS VCI data, benefiting farmers, policymakers, and researchers in making informed decisions and enhancing drought preparedness. (shrink)

In this paper we show how a student can learn the basics of the system databases using (W3school CSS) which was built as intelligent tutoring educational system by using the authoring tool called (ITSB). The learning material contains CSS and HTML. We divided the material in a group of lessons for novice learner which combines relational system and lessons in the process of learning. The student can learn using example of CSS, and types of CSS color. Furthermore, the intelligent tutoring (...) system supports not only lessons; but exercises of different difficult levels for each lesson. When a student finish successfully the first difficulty level in a lesson, the student is allowed to move to the next difficulty level of the exercises of the lesson. (shrink)

Now-a-days selection of crop is a significant challenge for farmers it becomes more difficult, if they are having no experience or do not have knowledge to select the best possible viable crop. Introducing the Smart Crop Advisor (SCA) which aids in such recommendations to local conditions. Smart Crop Advisor is an online decision support system that adopts Gradient Boosting algorithm to improve the accuracy of agricultural decisions, and gives low-level farmers a personal crop selection advice. Smart Crop Advisor is Designed (...) using Python, HTML and CSS. Python is used for backend development and machine learning model implementation. The user interface is designed using HTML and CSS, allowing for anyone to just add their conditions easily. This application provides insights based on data by utilizing a large agricultural database. The farmers have to enter the specifications based on their soil test report like PH value, climatic conditions such as humidity, temperature. The system proposes best crops suited for particular region and situations. SCA improves crop yields and minimizes chemical inputs, reduces resource loss by selecting the right crops for a specific region and climate, farmers can improve crop yields and reduce waste. It promotes sustainable farming S which the system promotes sustainable farming practices by recommending crops that are well-suited to the local conditions. (shrink)

In this paper we show how a student can learn the basics of the system databases using (W3school CSS) which was built as intelligent tutoring educational system by using the authoring tool called (ITSB). The learning material contains CSS and HTML. We divided the material in a group of lessons for novice learner which combines relational system and lessons in the process of learning. The student can learn using example of CSS, and types of CSS color. Furthermore, the intelligent tutoring (...) system supports not only lessons; but exercises of different difficult levels for each lesson. When a student finish successfully the first difficulty level in a lesson, the student is allowed to move to the next difficulty level of the exercises of the lesson. (shrink)

In 2013, I've transformed the each_co_uk server side system (aspx / c#) to the client system of a few javascript files ≈400KB potentially webassembly, without plugins / 3rd party / css.. The system maximally reuses functions & data to apply: 1) Linguistic principle of arbitrariness of the sign maximizing repetitions, 2) Multi layer logic of the chess composition, 3) Information Theory minimizing entropy. Some of these ideas were presented at conferences in Santorini, Adelaide, Geneva, Daejon and virtually. Philosophically, any system (...) only organizes the existing reality (information), but can't create it. Its fragmentation easily leads to illusions (entropy): e.g. Zeno's Achilles can't overtake the slower tortoise because as he gets closer, the tortoise gets a bit further, so he gets closer and closer but never reaches her. So the more are logic and data fragmented in various layers (server, javascript, css, sql, frameworks..), the less capable system is. (shrink)

This project is a robust online education platform designed to streamline learning and teaching experiences. It integrates features for course management, user authentication, and interactive learning. Core functionalities include user registration, tutor and student profile management, course search and enrollment, video content streaming, and interactive elements like bookmarks, comments, and likes to enhance user engagement. The backend, powered by PHP, ensures dynamic content delivery, while the frontend employs CSS and JavaScript for a user-friendly interface. The database, structured using SQL, efficiently (...) handles data related to courses, users, and interactions. This platform is scalable and adaptable, making it ideal for educational institutions or independent tutors aiming to offer seamless virtual learning experiences. (shrink)

This essay utilizes the work of the comedy group, Monty Python, as a means of introducing basic concepts in Existentialism, especially as it pertains to the writings of Nietzsche, Sartre, and Camus.

Deep learning has emerged as a transformative technology, enabling advancements in fields such as computer vision, natural language processing, and autonomous systems. Python, with its comprehensive libraries and frameworks, has become the primary language for developing deep learning models. This paper explores the latest advancements in Python-based deep learning, focusing on key frameworks, algorithms, and innovations. It also discusses the challenges associated with implementing deep learning solutions, such as computational cost, data quality, and model interpretability. Furthermore, it addresses sustainable approaches (...) to deep learning, emphasizing energy-efficient techniques, model optimization, and the adoption of green computing practices. By understanding these advancements and challenges, we can push forward towards more efficient and sustainable deep learning solutions using Python. (shrink)

Purpose. The purpose of this study is to explore vectorization as an engineering technique to improve the performance and readability of Python code, particularly in data processing tasks. We aim to demonstrate the benefits of vectorization through practical examples involving the handling of missing data. Design / Method / Approach. To achieve the research goals, we performed a comparative analysis between loop-based and vectorized implementations. Specifically, two versions of a function were developed to identify columns containing missing values within a (...) dataset. These implementations were tested on two real-world datasets. We compared execution time and code readability. Findings. The findings showed that vectorization resulted in substantial performance improvements, reducing execution time by hundreds of times compared to traditional loop-based methods. Additionally, the vectorized code was more compact, leading to greater readability and ease of maintenance. Theoretical Implications. Vectorization provides a higher level of abstraction for performing operations on data structures. This allows developers to focus on algorithmic logic rather than managing iterative control structures, contributing to broader discussions on optimizing computational efficiency in Python. Practical Implications. For data engineers and analysts, vectorization represents a highly effective solution for optimizing Python code. It significantly accelerates data-intensive tasks, such as missing data imputation, data analysis, and machine learning, making it an essential tool for enhancing productivity in data-driven environments. Originality / Value. This study presents a practical approach to optimizing Python code through vectorization. It is valuable for professionals seeking to improve efficiency in their workflows. Research Limitations / Future Research. The limitation of this research lies in its focus on a single problem – missing data imputation. Future research should expand the scope to other computational areas, such as image processing and simulation modeling, or examine the use of vectorization alongside Just-In-Time (JIT) compilation using tools like Numba to further boost Python's performance. Paper Type. Practitioner Paper. (shrink)

In the constantly evolving landscape of Artificial Intelligence (AI), programming language selection has a defining role in the generation of innovation. This paper examines the harmonious dance between Python and AI, a language that has emerged to be the spine of intelligent systems. In a comprehensive review, we bring forth Python's heterogeneity and dominance in AI design, with applications in real life in machine learning, deep learning, natural language processing, robotics, and more. A deeper look into the strengths of Python—simplicity, (...) rich libraries, and supportive community—offers a sufficient reason why it is now the choice of AI researchers and developers. No technology is flawless, though; hence, we also mention the shortcomings of Python, such as performance bottlenecks and runtime issues, to present an overall picture. Since a tremendous amount of programming languages are used in AI, the paper also provides a comparative summary of popular AI-guided languages in terms of their efficiency, scalability, and usability for various AI applications. By providing an overview of Python's prevalence and placement among other AI languages, the research seeks to offer developers, researchers, and professionals decision-making information for AI-project deployment. (shrink)

Python has become the de facto language for AI and machine learning development, significantly contributing to the democratization of AI. Through open-source libraries and frameworks such as TensorFlow, PyTorch, and Scikit-Learn, Python enables developers and researchers to build and deploy sophisticated AI models, irrespective of their computational resources. However, with the growing concerns regarding the environmental impact of large-scale AI models, Python’s role also extends into the realm of eco-conscious development. This paper explores how Python, through its open-source community, facilitates (...) both AI accessibility and sustainability. We investigate Python’s contribution to eco-conscious development, such as model optimization, energy-efficient algorithms, and the integration of green AI practices. By reviewing case studies, the paper highlights the benefits of Python’s ecosystem in building sustainable AI systems that are both high-performing and energy-efficient. (shrink)

In the constantly evolving landscape of Artificial Intelligence (AI), programming language selection has a defining role in the generation of innovation. This paper examines the harmonious dance between Python and AI, a language that has emerged to be the spine of intelligent systems. In a comprehensive review, we bring forth Python's heterogeneity and dominance in AI design, with applications in real life in machine learning, deep learning, natural language processing, robotics, and more. A deeper look into the strengths of Python—simplicity, (...) rich libraries, and supportive community—offers a sufficient reason why it is now the choice of AI researchers and developers. No technology is flawless, though; hence, we also mention the shortcomings of Python, such as performance bottlenecks and runtime issues, to present an overall picture. Since a tremendous amount of programming languages are used in AI, the paper also provides a comparative summary of popular AI-guided languages in terms of their efficiency, scalability, and usability for various AI applications. By providing an overview of Python's prevalence and placement among other AI languages, the research seeks to offer developers, researchers, and professionals decision-making information for AI-project deployment. (shrink)

Evoke is a Python library for evolutionary simulations of signalling games. It offers a simple and intuitive API that can be used to analyze arbitrary game-theoretic models, and to easily reproduce and customize well-known results and figures from the literature.

Python has emerged as one of the most popular programming languages in the fields of Artificial Intelligence (AI) and Machine Learning (ML) due to its simplicity, versatility, and vast ecosystem of libraries and frameworks. This paper aims to explore the essential Python libraries and frameworks that have made AI and ML development more efficient and accessible. It will cover popular libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch, among others, which are extensively used for data manipulation, statistical analysis, and (...) building AI/ML models. Furthermore, the paper will provide an overview of how these tools contribute to the effectiveness and efficiency of AI and ML tasks, ultimately enhancing their scalability and performance. (shrink)

This research paper presents the use of Playwright, JavaScript & Cucumber to perform scalable and efficient functional automation, comparing to traditional tools. The paper shows that Playwright reduces test execution time, improves cross browser support, and gets the testing process more efficient, applying quantitative analysis, geographical distribution, and case studies. It finds Playwright to transform how software quality is built, loading faster releases, robust security validations, and pleasurable user experiences in a variety of environments.

With the growing complexity of AI models and the vast computational resources required for their development, it is becoming increasingly important to optimize machine learning workflows for both efficiency and sustainability. This paper explores the various Python tools and techniques available for improving AI workflows while minimizing energy consumption and reducing carbon footprints. By incorporating best practices in model optimization, hardware acceleration, efficient data handling, and deployment strategies, AI development can be made more energyefficient and eco-friendly. Through the exploration of (...) libraries like TensorFlow, PyTorch, and specialized tools for green computing, this paper emphasizes the role of Python in advancing both efficient and sustainable AI workflows. (shrink)

Many trading desks across the financial industry still rely heavily on Excel-based tools for pricing, risk assessment, and reporting workflows. While these spreadsheets offer flexibility and familiarity, they present challenges in scalability, collaboration, version control, and integration with modern trading infrastructure. This paper presents a cloud-native migration framework that transforms trader-centric Excel tools into robust, scalable applications using Java and Python. The framework incorporates modular microservices, RESTful APIs, AWS services, and real-time data processing. It also addresses migration challenges such as (...) formula translation, data validation, and end-user adoption. The resulting system improves performance, auditability, and maintainability while retaining the usability traders expect. (shrink)

Machine learning (ML) pipelines are essential for automating the workflow involved in model development, from data preprocessing to model evaluation and deployment. A well-structured ML pipeline ensures reproducibility, scalability, and efficiency. This paper offers a comprehensive guide to constructing and optimizing machine learning pipelines in Python, highlighting essential tools, best practices, and common challenges. We discuss the role of various Python libraries like Scikit-learn, TensorFlow, and Apache Airflow in facilitating the automation and deployment of ML workflows. By illustrating pipeline design (...) through practical examples and case studies, this guide provides actionable insights for ML practitioners seeking to improve workflow efficiency and model performance. (shrink)

With the rapid development and adoption of artificial intelligence (AI), the environmental impact of energy consumption during the training and deployment of AI models has become a pressing concern. Energy-efficient AI development is crucial for reducing the carbon footprint of AI technologies. This paper explores various methods and tools used to optimize energy efficiency in AI model development, focusing on Python-based techniques. Case studies are presented to demonstrate practical applications of energy-efficient AI. By analyzing optimization strategies such as model pruning, (...) quantization, hardware acceleration, and energy-aware training, this work highlights how Python libraries and frameworks can contribute to sustainable AI practices. (shrink)

With the increasing demand for machine learning (ML) applications across various industries, the environmental impact of training large models has become a significant concern. Green AI emphasizes the development of machine learning models that are energy-efficient, requiring fewer computational resources while maintaining high performance. This paper explores how lightweight machine learning models, implemented with Python, can contribute to Green AI practices. We review several approaches for designing compact models, including model pruning, knowledge distillation, and efficient architectures such as decision trees, (...) linear models, and lightweight neural networks. By adopting these techniques, organizations can reduce the carbon footprint of AI systems without compromising accuracy. Through practical examples, we demonstrate how Python libraries and tools can facilitate the creation of lightweight models in an energy-efficient manner. (shrink)

The rapid development of machine learning (ML) and artificial intelligence (AI) technologies has raised concerns regarding their environmental impact. Training complex ML models, particularly deep learning models, requires significant computational resources, which often result in substantial energy consumption and carbon emissions. This paper explores techniques and strategies for reducing the ecological footprint of AI and ML using Python. We focus on methods such as model optimization, hardware efficiency, algorithmic improvements, and sustainable data practices that contribute to eco-friendly machine learning. The (...) paper also highlights the importance of green AI and the role of Python’s libraries in facilitating environmentally conscious ML practices. (shrink)

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports on (...) an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Abstract: Dates are the fruit of date palm trees, and it is one of the fruits famous for its high nutritional value. It is a summer fruit spread in the Arab world. In the past, the Arabs relied on it in their daily lives. Dates take an oval shape and vary in size from 20 to 60 mm in length and 8 to 30 mm in diameter. The ripe fruit consists of a hard core surrounded by a papery cover called (...) the tartar that separates the core from the fleshy part that is eaten. Historians disagreed about the place of its origin, so some historians expressed their belief that it had originated around the Arabian Gulf, and some of them say that the oldest known information about palm trees was in Babylon 4 thousand years ago BC. It is known about the ancient Egyptians using dates in wine. Dates contain a high nutritional value and are considered a basic food for man since ancient times. The fruits of dates are considered the highest fruits in containing sugars. These components vary according to the nature of the fruit, whether it is wet, semi-dry or dry, as well as according to the environmental conditions surrounding the trees. The components of the fruits also differ in different varieties and increase the percentage of sugars in dates is 70-78% of the fruit’s components. These sugars are characterized by their rapid absorption and transfer into the blood directly, digestion and burning. 10 dates (about 100 grams) per day provide a person with all his daily needs of magnesium, manganese, copper, sulfur and half of his needs of calcium and potassium. In this paper we presented a system that recognize the nine types of dates based on deep learning using python on Colab, and classifying using a dataset contain 1350 images. Our trained model achieved an accuracy of 99.44% on a proven test set. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...) allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

Causal reasoning and game-theoretic reasoning are fundamental topics in artificial intelligence, among many other disciplines: this paper is concerned with their intersection. Despite their importance, a formal framework that supports both these forms of reasoning has, until now, been lacking. We offer a solution in the form of (structural) causal games, which can be seen as extending Pearl's causal hierarchy to the game-theoretic domain, or as extending Koller and Milch's multi-agent influence diagrams to the causal domain. We then consider three (...) key questions: i) How can the (causal) dependencies in games - either between variables, or between strategies - be modelled in a uniform, principled manner? ii) How may causal queries be computed in causal games, and what assumptions does this require? iii) How do causal games compare to existing formalisms? To address question i), we introduce mechanised games, which encode dependencies between agents' decision rules and the distributions governing the game. In response to question ii), we present definitions of predictions, interventions, and counterfactuals, and discuss the assumptions required for each. Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support. Finally, we highlight possible applications of causal games, aided by an extensive open-source Python library. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

forall x: Calgary is a full-featured textbook on formal logic. It covers key notions of logic such as consequence and validity of arguments, the syntax of truth-functional propositional logic TFL and truth-table semantics, the syntax of first-order (predicate) logic FOL with identity (first-order interpretations), symbolizing English in TFL and FOL, and Fitch-style natural deduction proof systems for both TFL and FOL. It also deals with some advanced topics such as modal logic, soundness, and functional completeness. Exercises with solutions are available. (...) It is provided in PDF (for screen reading, printing, and a special version for dyslexics), HTML, and in LaTeX source code. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

"Philosophy of Happiness: A Critical Introduction" summarizes (a) what philosophy of happiness is, (b) why it should matter to us, (c) what assistance we can draw from philosophy, empiric science, religion, and self-help sources, and (d) why taking an independent approach is both necessary and feasible. -/- The article is in PDF format, 60 pages. The table of contents links directly to the listed captions. Also available in an html version under the phone variant of the referenced philosophy of happiness (...) website. -/- Table of Contents -/- 1. The Definition of Happiness. -/- 2. The Definition of Philosophy of Happiness. -/- 3. Taking Refuge from the Complexity of Happiness. -/- 4. Self-Help Contributions to Philosophy of Happiness. -/- 5. Religious Contributions to Philosophy of Happiness. -/- 6. Philosophical Authorities on Philosophy of Happiness. -/- 7. Happiness Studies by Sciences Beyond Philosophy. -/- 8. Independent Approaches Toward a Philosophy of Happiness. -/- 9. Further Reading. (shrink)

This paper proposes an extensionalist analysis of computer simulations (CSs). It puts the emphasis not on languages nor on models, but on symbols, on their extensions, and on their various ways of referring. It shows that chains of reference of symbols in CSs are multiple and of different kinds. As they are distinct and diverse, these chains enable different kinds of remoteness of reference and different kinds of validation for CSs. Although some methodological papers have already underlined the role of (...) these various relationships of reference in CSs and of cross-validations, this diversity is still overlooked in the epistemological literature on CSs. As a consequence, a particular outcome of this analytical view is an ability to classify existing epistemological theses on CSs according to what their authors choose to select and put at the forefront: either the extensions of symbols, or the symbol-types, or the symbol-tokens, or the internal denotational hierarchies of the CS or the reference of these hierarchies to external denotational hierarchies. Through the adoption of this extensionalist view, it also becomes possible to explain more precisely the reasons why some complete reduction of CSs to classical epistemic paradigms such as “experiment” or “theoretical argument” remains doubtful. On this last point, in particular, this paper is in agreement with what many epistemologists already have acknowledged. (shrink)

Most research on image schema examined the meaning configuration of words connotation. However, previous studies of adjectives are meaningful in cognitive linguistics because they provide insight into how those adjectives are involved with psychological movement. In this sense, from the perspective of cognitive linguistics, one’s conceptualization and cognition are closely associated with their bodily experience and surroundings; adjectives are no exception. The varieties of transformations of image schemas lay the foundation for the conception and perception. Accordingly, this study is an (...) investigation into the perception of adjectives and psychological activities associated with image schema. Data in this study come from adjectives in the novel The Great Gatsby, which were tagged and calculated with the help of software Python and the NLTK package. The results demonstrate that a significant amount of adjectives were used in the novel. Moreover, Nick’s psychological activities changed significantly from the SOURCE to the DESTINATION schematic stage, which is uncovered by his expression of adjectives. Finally, the results further suggest that transformations of image schema as BALANCE, CONTAINER, and PATH work together to motivate one’s linguistic expression of adjectives. The present findings have several implications for research on lexicons and literacy interpretation from cognitive linguistics. (shrink)

Infusing contemporary critical terrorism studies (CTS) with concepts and methodologies from philosophy and critical theory via a Baradian posthumanist agential realist perspective and (counter)terrorist cases and vignettes, this chapter argues for a retheorisation of (counter)terrorism. It does so, firstly, by reconceptualising terrorism and counterterrorism as complex assemblages consisting not only of discursive-material components – an entanglement now largely accepted within CTS and critical security studies (CSS) – but also of affective layers and more-than-human phenomena. Secondly, by analysing European urban (counter)terrorist (...) cases from the UK, Germany, France, and Spain, together with these cases’ surprising spacetime-jumping interlinkages underwriting what we here conceptualise as queer(ing) spacetimematterings, this chapter zooms in on the intra-actions taking place between human and more-than-human agential phenomena and their risk-managed urban environments. Lastly, extra analytical attention is paid to how, in this neoliberal day and age – here rephrased as control society-driven ‘situationscaping times’ – very specific macro- and micropolitical violence-preventing measures and efforts are employed in the fight against various manifestations of urban terror and terrorism. (shrink)

This is a bibliography of books and articles on consciousness in philosophy, cognitive science, and neuroscience over the last 30 years. There are three main sections, devoted to monographs, edited collections of papers, and articles. The first two of these sections are each divided into three subsections containing books in each of the main areas of research. The third section is divided into 12 subsections, with 10 subject headings for philosophical articles along with two additional subsections for articles in cognitive (...) science and neuroscience. Of course the division is somewhat arbitrary, but I hope that it makes the bibliography easier to use. This bibliography has first been compiled by Thomas Metzinger and David Chalmers to appear in print in two philosophical anthologies on conscious experience . From 1995 onwards it has been continuously updated by Thomas Metzinger, and now is freely available as a PDF-, RTF-, or HTML-file. This bibliography mainly attempts to cover the Anglo-Saxon and German debates, in a non-annotated, fully formatted way that makes it easy to "cut and paste" from the original file. To a certain degree this bibliography also contains items in other languages than English and German - all submissions in other languages are welcome. Last update of current version: July 13th, 2001. (shrink)

ABSTRACT: Neo-sentimentalism is the view that to judge that something has an evaluative property is to judge that some affective or emotional response is appropriate to it, but this view allows for radically different versions. My aim is to spell out what I take to be its most plausible version. Against its normative version, I argue that its descriptive version can best satisfy the normativity requirement that follows from Moore’s Open Question Argument while giving an answer to the Wrong Kind (...) of Reason Objection. Finally, I argue that the circularity that is involved is not vicious: understood epistemically, neo-sentimentalism remains instructive. View HTML Send article to KindleTo send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle. Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply. Find out more about the Kindle Personal Document Service.Valeurs et émotions, les perspectives du néo-sentimentalismeVolume 51, Issue 1Christine Tappolet DOI: https://doi.org/10.1017/S0012217312000212Your Kindle email address Please provide your Kindle email.@free.kindle.com@kindle.com Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Dropbox To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox. Valeurs et émotions, les perspectives du néo-sentimentalismeVolume 51, Issue 1Christine Tappolet DOI: https://doi.org/10.1017/S0012217312000212Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Google Drive To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive. Valeurs et émotions, les perspectives du néo-sentimentalismeVolume 51, Issue 1Christine Tappolet DOI: https://doi.org/10.1017/S0012217312000212Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Export citation Request permission. (shrink)

Berkeley in his Introduction to the Principles of Human knowledge uses geometrical examples to illustrate a way of generating “universal ideas,” which allegedly account for the existence of general terms. In doing proofs we might, for example, selectively attend to the triangular shape of a diagram. Presumably what we prove using just that property applies to all triangles.I contend, rather, that given Berkeley’s view of extension, no Euclidean triangles exist to attend to. Rather proof, as Berkeley would normally assume, requires (...) idealizing diagrams; treating them as if they obeyed Euclidean constraints. This convention solves the problem of representative generalization. View HTML Send article to KindleTo send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle. Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply. Find out more about the Kindle Personal Document Service.Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Your Kindle email address Please provide your Kindle email.@free.kindle.com@kindle.com Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Dropbox To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Dropbox. Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Send article to Google Drive To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about sending content to Google Drive. Berkeley and Proof in GeometryVolume 51, Issue 3RICHARD J. BROOK DOI: https://doi.org/10.1017/S0012217312000686Available formats PDF Please select a format to send. By using this service, you agree that you will only keep articles for personal use, and will not openly distribute them via Dropbox, Google Drive or other file sharing services. Please confirm that you accept the terms of use. Cancel Send ×Export citation Request permission. (shrink)

This paper presents an overview of Carnap, a free and open framework for the development of formal reasoning applications. Carnap’s design emphasizes flexibility, extensibility, and rapid prototyping. Carnap-based applications are written in Haskell, but can be compiled to JavaScript to run in standard web browsers. This combination of features makes Carnap ideally suited for educational applications, where ease-of-use is crucial for students and adaptability to different teaching strategies and classroom needs is crucial for instructors. The paper describes Carnap’s implementation, along (...) with its current and projected pedagogical applications. (shrink)

In this paper, I ask whether mishpat ivri (Jewish Law) is appropriately conceived as a “legal system”. I review Menachem Elon’s use of a “Sources” Theory of Law (based on Salmond) in his account of Mishpat Ivri; the status of religious law from the viewpoint of jurisprudence itself (Bentham, Austin and Kelsen); then the use of sources (and the approach to “dogmatic error”) by halakhic authorities in discussing the problems of the agunah (“chained wife”), which I suggest points to a (...) theory more radical than the “sources” theory of law, one more akin to the ultimate phase of the thought of Kelsen (the “non-logical” Kelsen) or indeed to some form of Legal Realism (with which that phase of Kelsen’s thought has indeed been compared)? I finally juxtapose an account based on internal theological resources (a “Jurisprudence of Revelation”). Downloadable at at http://www.biu.ac.il/JS/JSIJ/jsij1.html. (shrink)

This is an excerpt from a report on the workshop on multisensory integration at the University of Toronto, on May 9th and 10th, 2014, written by Kevin Connolly, Aaron Henry, Zoe Jenkin, and Andrew MacGregor, and available at: http://networksensoryresearch.utoronto.ca/Events_%26_Discussion.html This excerpt explores the question: What can multisensory processing tell us about multisensory awareness?

http://www.uni-bielefeld.de/ZIF/Publikationen/books/10_Baurmann_NormsAndValues.html.

This paper proposes a novel model for understanding consciousness by envisioning Brahman—the ultimate, universal consciousness in Vedanta—as an infinitely layered JSON (JavaScript Object Notation) structure. In this model, each aspect of reality, from individual thoughts to timelines and entire universes, is represented as a property within this universal JSON object. By integrating the Vedantic concepts of Atman (individual self) and Brahman (universal consciousness) with ideas from quantum mechanics and information theory, this model suggests that all possible experiences, thoughts, and realities (...) exist within Brahman, ready to be accessed by individual consciousness. This framework offers a fresh way to think about reality, creativity, and free will, proposing that every possible thought or action already exists as a “node” within Brahman’s JSON, and that we simply “select” which part to experience. (shrink)

If we had more powerful minds would we be puzzled by less - because we could make better theories - or by more - because we could ask more difficult questions? This paper focuses on clarifying the question, with an emphasis on comparisons between actual and possible species of thinker. A pre-publication version of the paper is available on my website at http://www.fernieroad.ca/a/PAPERS/papers.html .

http://www.uni-bielefeld.de/ZIF/Publikationen/books/10_Baurmann_NormsAndValues.html.

This paper proposes a novel model for understanding consciousness by envisioning Brahman—the ultimate, universal consciousness in Vedanta—as an infinitely layered JSON (JavaScript Object Notation) structure. In this model, each aspect of reality, from individual thoughts to timelines and entire universes, is represented as a property within this universal JSON object. By integrating the Vedantic concepts of Atman (individual self) and Brahman (universal consciousness) with ideas from quantum mechanics and information theory, this model suggests that all possible experiences, thoughts, and realities (...) exist within Brahman, ready to be accessed by individual consciousness. This framework offers a fresh way to think about reality, creativity, and free will, proposing that every possible thought or action already exists as a “node” within Brahman’s JSON, and that we simply “select” which part to experience. (shrink)

http://www.uni-bielefeld.de/ZIF/Publikationen/books/10_Baurmann_NormsAndValues.html.

This paper presents an NLP-driven approach to identifying fake statements made by public figures. The system employs natural language processing (NLP) techniques to analyze textual data and classify statements as true or false. Given the rise of digital media, misinformation has become a global challenge, influencing public opinion and political discourse. Fake news spreads rapidly through social and mainstream media, making fact-checking increasingly difficult. The proposed system processes statements independently, without relying on metadata, using techniques such as tokenization, stemming, and (...) stop-word removal. This method enhances the accuracy of classification by focusing on the linguistic features of statements. Future improvements may incorporate additional contextual data to refine detection accuracy further. Developed using Python and MySQL, this system provides a scalable and efficient solution to combat misinformation, contributing to more reliable information dissemination in the digital age. (shrink)