Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic (...) Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. (shrink)

In this paper, we explore the properties and optimization techniques related to polyhedral cones and energy numbers with a focus on the cone of positive semidefinite matrices and efficient computation strategies for kernels. In Part (a), we examine the polyhedral nature of the cone of positive semidefinite matrices, , establishing that it does not form a polyhedral cone for due to its infinite dimensional characteristics. In Part (b), we present an algorithm for efficiently computing the kernel function on-the-fly, leveraging a (...) polyhedral description of the convex hull generated by the feature mappings and . By restructuring the problem and using gradient-based optimization techniques, our approach minimizes memory usage and computational overhead, thus enabling scalable computation. Through examples and visualizations, we demonstrate the practical applications and efficiency of the proposed algorithm in optimizing these kernel computations. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and confidentiality (...) of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single (...) server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. Future enhancements may include integrating machine learning algorithms to predict and adapt to new security threats in real time, further solidifying the reliability of cloud storage solutions. (shrink)

We recall, "a priori," numeric energy expression: -/- Energy Numbers -/- $\begin{gathered}\mathcal{V}=\left\{f \mid \exists\left\{e_1, e_2, \ldots, e_n\right\} \in E \cup R\right\} \\ \mathcal{V}=\left\{f \mid \exists\left\{e_1, e_2, \ldots, e_n\right\} \in E, \text { and }: E \mapsto r \in R\right\} \\ \mathcal{V}=\left\{E \mid \exists\left\{a_1, \ldots, a_n\right\} \in E, E \not \neg r \in R\right\}\end{gathered}$ -/- We now introduce the set of optimized energy numbers: -/- ($H_a \in \mathcal{H}$ or $P^n = NP$ or $(P,\mathcal{L},F) = NP$). -/- Based on our formulation (...) of the bi-objective optimization task, we can make the following mathematical inferences: -/- 1. If the optimized energy numbers set $\mathcal{N}_H$ is equal to the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H = \mathcal{E}$, then the maximum optimization score is achieved, i.e. the bi-objective optimization task is solved. This implies that there exists at least one solution to the optimization problem and $H_a \in \mathcal{H}$, where $H_a$ is the hypothesis that states the existence of an efficient algorithm to solve the problem. -/- 2. If the optimized energy numbers set $\mathcal{N}_H$ is a subset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \subset \mathcal{E}$, then the optimization score is less than the maximum score. This indicates that there may exist more efficient algorithms to solve the problem, and the hypothesis $H_a$ is still possible. -/- 3. If the optimized energy numbers set $\mathcal{N}_H$ is a superset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \supset \mathcal{E}$, then the optimization score is higher than the maximum score. This implies that the optimization problem may be easier than initially thought, and $P^n = NP$, or at least some form of $NP$-completeness. -/- 4. If the optimized energy numbers set $\mathcal{N}_H$ is a strict subset of the original energy numbers set $\mathcal{E}$, i.e. $\mathcal{N}_H \subsetneq \mathcal{E}$, and $P^n \neq NP$, then it can be concluded that the optimization problem is complex but there may exist algorithms that can efficiently approximate the solution. -/- 5. If the optimized energy numbers set $\mathcal{N}_H$ is empty, i.e. $\mathcal{N}_H = \emptyset$, then it can be inferred that the optimization problem is infeasible, i.e. no efficient algorithm exists to solve it, and $H_a$ is false. -/- 6. Comparing the two objectives in the bi-objective optimization task, we can make the following statements: -/- - The first objective, $\frac{\delta_{v(f)}(\mathbf{v}, \mathbf{w_{max}})} {\langle \mathbf{v_f}, \mathbf{1_f}\rangle}$, measures the efficiency of the algorithm and its ability to find low energy numbers. - The second objective, $\rho(\mathcal{N}_H)$, measures the accuracy of the algorithm in terms of loss and perplexity on the HyperLanguageModel. - Therefore, by optimizing both objectives simultaneously, we aim to find an efficient algorithm that also minimizes the loss and perplexities on the HyperLanguageModel. - If the optimization task is successfully solved, then the algorithm achieves both high efficiency and high accuracy. This would imply that the algorithm is able to find low energy numbers effectively and also generalize well on the HyperLanguageModel. -/- The optimized energy numbers aim to find a set of numbers $\mathbf{v}$ that maximize the bi-objective optimization task, while also minimizing the loss and perplexity of the HyperLanguageModel. This is achieved by finding the set of numbers that have the highest delta value and the lowest perplexity, resulting in a more optimized and efficient set of energy numbers. -/- By comparing the optimized energy numbers to the original set, we can see that the optimized set may have a higher delta value and a lower perplexity, indicating that it is a better set of numbers for the given task. This shows that the optimized energy numbers have successfully achieved their goal of maximizing efficiency while minimizing loss and perplexity. (shrink)

Underwater Wireless Sensor Networks (UWSNs) are pivotal for various applications, including oceanographic data collection, environmental monitoring, and naval operations. However, the harsh underwater environment poses challenges in designing efficient routing protocols, especially concerning energy consumption and data transmission reliability. This paper proposes an optimized depth-based routing protocol for energy-aware data transmission in UWSNs, focusing on minimizing energy usage while ensuring robust data delivery. The protocol dynamically adjusts transmission power based on node depth and residual energy, reducing communication overhead and (...) prolonging network lifetime. (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)

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data.

In the modern digital era, cloud storage has become an indispensable service due to its scalability, accessibility, and cost-effectiveness. However, with the vast amount of sensitive information stored on cloud platforms, ensuring data security and privacy remains a critical challenge. Traditional encryption techniques, while secure, often hinder efficient data retrieval, especially when using keyword searches. To address this, attribute-based keyword search (ABKS) offers a promising solution by allowing secure, fine-grained access control and efficient keyword searches over encrypted data. This paper (...) delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

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)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

Our findings reveal that implementing accelerated computing can achieve substantial improvements, often reducing computation times by more than 60% compared to traditional sequential methods. This paper details the experimental setup, including algorithm selection and parallelization techniques, and discusses the role of memory bandwidth and latency in achieving optimal performance. Based on the analysis, we propose a streamlined methodology to guide the deployment of accelerated computing frameworks in various industries. Concluding with a discussion on future directions, we highlight potential advancements in (...) hardware architectures and software optimizations that could further augment computational efficiency and scalability in accelerated computing. (shrink)

The accuracy levels of VGG19 and 3D CNN are compared using the performance metrics. This comparison helps in identifying which model performs better in the task of facial reconstruction from distorted images. Visualizing the results in the form of a graph provides a clear and concise way to understand the comparative performance of the algorithms. The ultimate goal of this project is to develop a system that can accurately reconstruct distorted faces, which can be invaluable in identifying accident victims or (...) assisting in medical treatments. By providing a reliable method for facial reconstruction, this technology can potentially save lives and improve outcomes for individuals involved in accidents. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We (...) examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. T. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

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.

t: Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission.

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as Genetic (...) Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

: Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This (...) study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

We propose a systematic workflow for automating the storage and retrieval process, starting from the identification of the stock to its precise placement and retrieval within the storage facility. The design also addresses potential challenges such as robot mobility, collision avoidance, and space optimization. Performance metrics, including accuracy, time efficiency, and system scalability, are measured using simulation-based experiments in a controlled environment. The results show significant improvements in operational efficiency compared to traditional stock management approaches. This integration paves the way (...) for future advancements in fully automated warehouses, reducing the need for human labor and increasing reliability. Finally, we discuss potential enhancements, including AI-based decision-making algorithms, multi-robot collaboration, and integration with Internet of Things (IoT) for real-time data analysis and continuous system improvement. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)

The world's leading cause of morbidity and death is cardiovascular diseases (CVD), which makes early detection essential for successful treatments. This study investigates how optimization techniques can be used with machine learning (ML) algorithms to forecast cardiovascular illnesses more accurately. ML models can evaluate enormous datasets by utilizing data-driven techniques, finding trends and risk factors that conventional methods can miss. In order to increase prediction accuracy, this study focuses on adopting different machine learning algorithms, including Decision Trees, Random Forest, Support (...) Vector Machines, and Neural Networks, that have been tuned using strategies including hyper parameter selection, crossvalidation, and feature selection. (shrink)

This study examines how ABKS might optimize search efficiency and data security in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements (...) a. (shrink)

The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be (...) made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as accuracy, precision, (...) recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This (...) study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Plastic waste is a significant environmental problem for almost all countries; therefore, protecting the environment from the problem is crucial. The most sensible solution to these problems is substituting the natural aggregates with substantial plastic waste in various building materials. This study aimed to optimise the mixed design ratio of cement brick containing plastic waste as aggregate replacement. Plastic cement brick mixtures were prepared by the incorporation of four different types of plastic waste such as polyethylene terephthalate (PET), high-density polyethylene, (...) low-density polyethylene and polypropylene into cement bricks with different cement contents (150, 300 and 450 g) and plastic replacement percentages (0, 3 and 6%). Compressive strength and water absorption of the plastic cement bricks were analysed using a statistical model through the response surface methodology. It revealed the optimum cement brick mixed design is C3-1% PET with the compressive strength of 27.50 MPa and water absorption of 1.16%. The optimised plastic cement brick also satisfied the general ASTM C62-17 requirements for building bricks despite the higher porosity observed by the scanning electron microscopy. The results from Fourier transform infrared spectroscopy analysis also showed that the addition of the plastic waste into cement brick was unlikely to modify the chemical compound within the cement brick mixtures. Thus, the proposed mathematical model can predict the required hardened properties of plastic cement bricks and could lead to greater utilisation of plastic waste in building materials. (shrink)

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...) gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus. (shrink)

We address the most fundamental yet routinely ignored issue in economics today: that of distributive impact of the monetary system on the real economy. By re-examining the logical implications of token re-presentation of value and Irving Fisher’s theory of exchange, we argue that producers of value incur incidental expropriation of wealth associated with the deflationary effect that new value supply has on the purchasing power of money. In order to remedy the alleged inequity we propose a value-added negative tax (VANT) (...) based on net individual contribution to the economic output, which is structured as a negative tax function geared to profits derived from eligible productive activities in consideration of their estimated deflationary impact. We show that an adequately optimised VANT can be non-inflationary and have zero net cost to the public. Furthermore, economic output stands to improve due to direct incentivisation of value creation, making the proposed scheme not only politically feasible but economically desirable. The proposal advances the principle of ‘fair money’, where all forms of economic value are attributed to their rightful owners prior to any positive taxation. (shrink)

Humans are uncontroversially better than other species at learning from their peers. A key example of this is imitation, the ability to reproduce both the means and ends of others’ behaviours. Imitation is critical to the acquisition of a number of uniquely human cultural and cognitive traits. However, while authors largely agree on the importance of imitation, they disagree about the origins of imitation in humans. Some argue that imitation is an adaptation, connected to the ‘Mirror Neuron System’ that evolved (...) to facilitate action understanding and the learning of social behaviours. Others argue that imitation is a cultural practice learned in childhood, and that there is no evidence that it evolved genetically. We offer a third alternative that is consistent with both data that the human brain is optimised for imitation, and consistent with its being substantially learned. We hypothesise that at some point in human history humans underwent natural selection for two imitation-relevant abilities – specifically, (i) the tendency to be more attentive to our peers, and (ii) fine motor skill in the manual and oro-facial domains. These changes enabled us to excel at both learning to imitate, and learning by imitating. (shrink)

YouTube has been implicated in the transformation of users into extremists and conspiracy theorists. The alleged mechanism for this radicalizing process is YouTube’s recommender system, which is optimized to amplify and promote clips that users are likely to watch through to the end. YouTube optimizes for watch-through for economic reasons: people who watch a video through to the end are likely to then watch the next recommended video as well, which means that more advertisements can be served to them. (...) This is a seemingly innocuous design choice, but it has a troubling side-effect. Critics of YouTube have alleged that the recommender system tends to recommend extremist content and conspiracy theories, as such videos are especially likely to capture and keep users’ attention. To date, the problem of radicalization via the YouTube recommender system has been a matter of speculation. The current study represents the first systematic, pre-registered attempt to establish whether and to what extent the recommender system tends to promote such content. We begin by contextualizing our study in the framework of technological seduction. Next, we explain our methodology. After that, we present our results, which are consistent with the radicalization hypothesis. Finally, we discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers. (shrink)

Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: Can the (...) fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

This chapter follows Lorenzo Magnani's observation that ongoing commercialization of science and academia impoverishes human potential for discovery. The chapter reviews Magnani on affordance, wonders what is accessible when "good" affordances appear absent, and answers self-affordance. Ecologies optimized for discovery should be optimized for self-affordance. The chapter considers the role of oracle as leading vision for discovery, and proposes a naturalized account of self that is essentially propositional, in pursuit of an inner oracle, seeking salvation through routine and (...) religious ritual. This is self-abduction. Variable biological development may motivate religious self-abduction characterized by self-sacrifice for the collective good over the long term. in conclusion, the chapter affirms Magnani's call to optimize cognitive ecologies for discovery, by prioritizing religious self-abduction. (shrink)

The ji 己self is a site, storehouse, or depot of individuated allotment associated with the possession of things and qualities: wholesome and unwholesome desires (yu 欲) and aversions, emotions such as anxiety, and positive values such as humaneness and reverence. Each person's allotment is unique, and its "contents" are collected, measured, reflected on, and then distributed to others. The Analects, Mencius, Xunzi, Daodejing, and Zhuangzi each have their own vision for negotiating the space between self and other. Works as seemingly (...) dissimilar as the Analects and Daodejing both agree that positive qualities located within the self should be shared with others, and that the self can be optimized rather than maximized through sharing, emptying, or clearing. Sommer compares the ji self with other terms for body and person current in classical times. This self is strongly individuated, but it exists primarily in relation to other human beings (ren 人 ). These "others" are almost never one's own kind and are usually people who fall outside one's ascribed familial and social relationships. Negotiations between self and other often reflect apprehension regarding degrees of distance, intimacy, worth, recognition, or understanding (zhi 知) between people. (shrink)

The paper continues my earlier Chat with OpenAI’s ChatGPT with a Focused LLM Experiment (FLEX). The idea is to conduct Large Language Model (LLM) based explorations of certain areas or concepts. The approach is based on crafting initial guiding prompts and then follow up with user prompts based on the LLMs’ responses. The goals include improving understanding of LLM capabilities and their limitations culminating in optimized prompts. The specific subjects explored as research subject matter include a) diagonalization techniques as (...) practiced by Cantor, Turing, Gödel, and the advances, such as the Forcing techniques introduced by Paul Cohen and later investigators, b) Knowledge Hierarchies & Mapping Exercises, c) Discussions of IJ Good’s Speculations Concerning the First Ultraintelligent Machine, AGI, and Super- intelligence. Results suggest variability between major models like ChatGPT-4, Llama-3, Cohere, Sonnet and Opus. Results also point to strong dependence on users’ preexisting knowledge and skill bases. The paper should be viewed as ’raw data’ rather than a polished authoritative reference. (shrink)

This chapter follows Lorenzo Magnani's observation that ongoing commercialization of science and academia impoverishes human potential for discovery. The chapter reviews Magnani on affordance, wonders what is accessible when "good" affordances appear absent, and answers self-affordance. Ecologies optimized for discovery should be optimized for self-affordance. The chapter considers the role of oracle as leading vision for discovery, and proposes a naturalized account of self that is essentially propositional, in pursuit of an inner oracle, seeking salvation through routine and (...) religious ritual. This is self-abduction. Variable biological development may motivate religious self-abduction characterized by self-sacrifice for the collective good over the long term. in conclusion, the chapter affirms Magnani's call to optimize cognitive ecologies for discovery, by prioritizing religious self-abduction. (shrink)

Most online platforms are becoming increasingly algorithmically personalized. The question is if these practices are simply satisfying users preferences or if something is lost in this process. This article focuses on how to reconcile the personalization with the importance of being able to share cultural objects - including fiction – with others. In analyzing two concrete personalization examples from the streaming giant Netflix, several tendencies are observed. One is to isolate users and sometimes entirely eliminate shared world aspects. Another tendency (...) is to blur the boundary between shared cultural objects and personalized content, which can be misleading and disorienting. A further tendency is for personalization algorithms to be optimized to deceptively prey on desires for content that mirrors one’s own lived experience. Some specific - often minority targeting -“clickbait” practices received public blowback. These practices show disregard both for honest labeling and for our desires to have access and representation in a shared world. The article concludes that personalization tendencies are moving towards increasingly isolating and disorienting interfaces, but that platforms could be redesigned to support better social world orientation. (shrink)

The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That the HT (...) program must not be created by some set of sentient beings enacting responses to all possible inputs. (3) That in the current state of cognitive science it must be an open possibility that a computational model of the human mind will be developed that accounts for at least its nonphenomenological properties. Given ground rule 3, the HT program could simply be an “optimized” version of some computational model of a mind, created via the automatic application of program-transformation rules [thus satisfying ground rule 2]. Therefore, whatever mental states one would be willing to impute to an ordinary computational model of the human psyche one should be willing to grant to the optimized version as well. Hence no one could dismiss out of hand the possibility that the HT program was intelligent. This conclusion is important because the Humongous-Table Program Argument is the only argument ever marshalled against the sufficiency of the Turing Test, if we exclude arguments that cognitive science is simply not possible. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and confidentiality (...) of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (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)

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)

This treatise introduces a transformative socio-political paradigm that merges empathy, ethics, equality, and the refinement of excellence to address the novel challenges of the 21st century and beyond. By combining ethical bio-genetic enhancement, empathetic governance, and the cautious reconstruction of socio-political structures, it envisions a society capable of achieving unprecedented precision, efficiency, and unity in governance. This interdisciplinary framework integrates modern science, classical philosophy, and historical lessons to propose a genetically optimized, rigorously educated, and ethically inclined sub-population—the “Electi.” Designed (...) to embody human refinement and empathy, the Electi are uniquely qualified to navigate the ethical, environmental, and scientific challenges of the future. -/- Building on the works of Aristotle and Plato while incorporating contemporary concepts like Polygenic Scores, artificial intelligence, and psychometrics, the treatise provides a balanced model that addresses democracy’s inefficiencies without sacrificing equality or representation. Ethical safeguards and public engagement strategies are woven into the paradigm to counter fears of elitism and inequality. -/- This model aims to accelerate societal advancement, improve policy-making efficiency, and respond proactively to crises such as climate change and AI threats. By harmonizing ethical considerations with practical governance, the treatise offers an innovative framework for academics, philosophers, and policymakers to reimagine the future of human progress and governance. (shrink)

The concept of the best of all possible worlds is widely considered to be incoherent on the grounds that, for any world that might be termed the best, there is always another that is better. I note that underlying this argument is a conviction that the goodness of a world is determined by a single kind of good, the most plausible candidates for which are not maximizable. Against this I suggest that several goods may have to combine to determine the (...) goodness of a world, and because of logical conflicts between them, the goods can be optimized to give a genuine best of all possible worlds. (shrink)

The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly (...) enhance the operational efficiency of data centers, paving the way for future advancements in autonomous and self-optimizing data centers. (shrink)

Capacity of conscious agents to perform genuine choices among future alternatives is a prerequisite for moral responsibility. Determinism that pervades classical physics, however, forbids free will, undermines the foundations of ethics, and precludes meaningful quantification of personal biases. To resolve that impasse, we utilize the characteristic indeterminism of quantum physics and derive a quantitative measure for the amount of free will manifested by the brain cortical network. The interaction between the central nervous system and the surrounding environment is shown to (...) perform a quantum measurement upon the neural constituents, which actualize a single measurement outcome selected from the resulting quantum probability distribution. Inherent biases in the quantum propensities for alternative physical outcomes provide varying amounts of free will, which can be quantified with the expected information gain from learning the actual course of action chosen by the nervous system. For example, neuronal electric spikes evoke deterministic synaptic vesicle release in the synapses of sensory or somatomotor pathways, with no free will manifested. In cortical synapses, however, vesicle release is triggered indeterministically with probability of 0.35 per spike. This grants the brain cortex, with its over 100 trillion synapses, an amount of free will exceeding 96 terabytes per second. Although reliable deterministic transmission of sensory or somatomotor information ensures robust adaptation of animals to their physical environment, unpredictability of behavioral responses initiated by decisions made by the brain cortex is evolutionary advantageous for avoiding predators. Thus, free will may have a survival value and could be optimized through natural selection. (shrink)

Quilty-Dunn et al. argue that deep convolutional neural networks (DCNNs) optimized for image classification exemplify structural disanalogies to human vision. A different kind of artificial vision – found in reinforcement-learning agents navigating artificial three-dimensional environments – can be expected to be more human-like. Recent work suggests that language-like representations substantially improves these agents’ performance, lending some indirect support to the language-of-thought hypothesis (LoTH).

The exponential growth of cloud storage has necessitated advanced security measures to protect sensitive data from unauthorized access. Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single (...) server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. (shrink)

Darwin’s claim that Natural Selection, through optimization of fitness, explains complex biological design has not yet been properly formalized. Alan Grafen’s Formal Darwinism Project aims at providing such a formalization and at demonstrating that fitness maximization is coherent with results from Population Genetics, usually interpreted as denying it. We suggest that Grafen’s proposal suffers from some limitations linked to its concept of design as optimized fitness. In order to overcome these limitations, we propose a classification of evolutionary facts based (...) on a bi-dimensional complexity space, which adds robustness to fitness as measure of the quality of a design. In this space, each point represents an organismic architecture, and movement between two points would be an evolutionary fact. Natural Selection explains movement along the fitness axis, while non-selective mechanisms explain movement on the robustness axis. Moreover, we propose a thermodynamic metaphor to draw parallelisms between notions of fitness and entropy, robustness and energy, and movement in this space and reversible and irreversible cycles. We argue that this metaphor illustrates different evolutionary processes usually overlooked by proposals of formalization of Darwinian theory, such Grafen’s Formal Darwinism Project. (shrink)