With quantum computing nearing practical implementation in 2025, concerns over the vulnerability of classical cryptographic algorithms in cloud networking have intensified. This research addresses the urgent need for quantum-resilient communication protocols in federated and multi-tenant cloud environments. The study explores the integration of post-quantum cryptography (PQC) into cloud-native networking layers, focusing on protocols such as TLS 1.3, IPsec, and gRPC. It also investigates the performance trade-offs, key management challenges, and compliance implications of transitioning to PQC in large-scale, geographically distributed cloud (...) systems. A hybrid implementation model is proposed, blending classical and quantum-safe algorithms to ensure forward secrecy and interoperability during the migration phase. Experimental results suggest that lattice-based and hash-based key exchange mechanisms can be integrated into existing protocols with acceptable performance overhead and minimal disruption. (shrink)

In today’s rapidly evolving business environment, companies increasingly leverage one-time payments—such as holiday bonuses, retention incentives, and spot awards—to reward and motivate employees. However, these payments often involve variable criteria, fluctuating payout amounts, and the necessity for timely, error-free execution. This paper proposes an integrated, modular framework designed to automate and streamline onetime employee compensation. The proposed system encompasses an advanced Employee Eligibility Engine, a flexible Payment Calculation Engine, a robust Payment Execution and Audit Layer, and an innovative Stakeholder Verification (...) and Transparency Layer. Built on a cloud-native architecture, the solution integrates seamlessly with existing enterprise HR and payroll systems while ensuring data integrity, security, and compliance. Through this comprehensive framework, organizations can manage complex compensation scenarios with a high degree of automation and transparency, thereby enhancing operational efficiency and employee trust. (shrink)

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, rule-based systems, and data profiling. (...) The integration of data quality monitoring into different stages of the MLOps pipeline is discussed, emphasizing continuous monitoring at data ingestion, pre-training validation, post- deployment drift detection, and feedback loops for model retraining. The paper also addresses key challenges in implementing automated data quality monitoring, including balancing precision and recall in anomaly detection, handling high-dimensional and unstructured data, managing false positives and alert fatigue, and adapting to evolving data distributions. By providing a comprehensive framework for automating data quality monitoring in MLOps pipelines, this paper aims to equip practitioners with the knowledge and strategies necessary to enhance the reliability and performance of machine learning systems in production environments. (shrink)

In today’s rapidly evolving business environment, companies increasingly leverage one-time payments—such as holiday bonuses, retention incentives, and spot awards—to reward and motivate employees. However, these payments often involve variable criteria, fluctuating payout amounts, and the necessity for timely, error-free execution. This paper proposes an integrated, modular framework designed to automate and streamline one- time employee compensation. The proposed system encompasses an advanced Employee Eligibility Engine, a flexible Payment Calculation Engine, a robust Payment Execution and Audit Layer, and an innovative Stakeholder (...) Verification and Transparency Layer. Built on a cloud-native architecture, the solution integrates seamlessly with existing enterprise HR and payroll systems while ensuring data integrity, security, and compliance. Through this comprehensive framework, organizations can manage complex compensation scenarios with a high degree of automation and transparency, thereby enhancing operational efficiency and employee trust. (shrink)

This paper investigates how large language models (LLMs) can be applied to publicly available financial data to generate automated financial summaries and provide actionable recommendations for investors. We demonstrate how LLMs can process both structured financial data (balance sheets, income statements, stock prices) and unstructured text (earnings calls, management commentary) to derive insights, predict trends, and automate financial reporting. By focusing on a specific publicly traded company, this research outlines the methodology for leveraging LLMs to analyze company performance and generate (...) investor-focused summaries and recommendations. (shrink)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into data integration techniques, performance (...) optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

Foundation models, particularly large language models (LLMs), are transforming how data engineering tasks are automated across domains. This paper explores the use of LLMs to automate the creation, management, and optimization of Extract-Transform-Load (ETL) pipelines in a domain-agnostic manner. We provide conceptual frameworks and practical strategies for integrating foundation models into the ETL lifecycle, and we highlight use cases where such models (via platforms like Amazon Bedrock) generate pipeline code, enhance data transformation quality, and adapt pipeline execution. Through a review (...) of related work and an illustrative implementation, we demonstrate that LLMs can translate natural language specifications into pipeline workflows, assist in data harmonization and quality control, and optimize pipeline performance with minimal human intervention. We discuss evaluation metrics for these LLM-driven pipelines and examine the challenges of reliability, scalability, and governance. The results indicate that foundational models offer a promising avenue to drastically reduce development time and improve the robustness of data pipelines, suggesting a paradigm shift in AI-assisted data engineering. (shrink)

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for (...) implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. (shrink)

As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static Code (...) Analysis. The framework provides a systematic approach to identifying, tracking, and managing data dependencies across the entire BI ecosystem using advanced static code analysis techniques. Detailed methodologies, algorithms, and implementation considerations for static code analysis are discussed. This framework offers a robust solution for organizations seeking to enhance data quality, system reliability, and operational efficiency in their BI systems. Future research will expand on dynamic runtime analysis and machine learning- based methods to further enhance the framework. (shrink)

Semantic similarity measures have shown promise in enhancing natural language understanding by quantifying the likeness between textual elements. This paper investigates the application of semantic similarity measures to improve chatbot response relevance. By leveraging word embeddings and similarity metrics, this study aims to bridge the gap between simple keyword-based responses and contextually rich, relevant answers. The proposed approach integrates both traditional lexical measures and advanced vector-based embeddings to enhance user intent interpretation and ensure a more suitable response generation. By refining (...) the candidate response set with these techniques, the chatbot is able to generate highly relevant and contextually accurate replies. The results demonstrate that combining deep learning-based embeddings with traditional semantic metrics can significantly enhance response relevance, leading to more meaningful and human-like user interactions. Additionally, a feedback loop is incorporated to continuously learn from user interactions, further improving the quality of responses over time. (shrink)

The integration of Robotic Process Automation (RPA) into Human Resources (HR) functions represents a significant advancement in organizational efficiency and effectiveness. RPA technology automates repetitive and rule-based tasks, allowing HR professionals to focus on strategic initiatives that add value to the organization. This paper provides a comprehensive introduction to RPA within HR, detailing its applications, benefits, implementation strategies, and how its principles can be transferred to other domains such as finance. By examining the transformative potential of RPA, organizations can better (...) prepare for the future of work, where automation and human ingenuity coexist to drive success. (shrink)

The exponential growth of high-frequency real-time financial transactions necessitates scalable machine learning infrastructures capable of processing and forecasting data in real time. This paper proposes a comprehensive design and implementation strategy for such infrastructures using distributed computing frameworks like Apache Spark and cloud services such as Amazon Web Services (AWS). Emphasizing technical specifics, the paper delves into architectural designs, implementation strategies, and optimization techniques that address critical challenges in data ingestion, real-time processing, model training, and deployment. A proof-of-concept implementation demonstrates (...) the feasibility of the proposed architecture on a small scale, highlighting its potential benefits. The findings suggest that implementing a scalable distributed machine learning infrastructure can enhance computational efficiency and significantly improve the accuracy and timeliness of financial forecasts. Future work will involve deploying the proposed architecture in large-scale industry settings to validate its effectiveness in real-world scenarios. (shrink)

Every year, approximately 800,000 people lose their lives to suicide, underscoring the urgent need to identify individuals at risk. With the widespread use of social media, many individuals openly share their thoughts, including expressions of suicidal ideation, providing an opportunity for timely intervention. This study introduces an advanced system for detecting suicidal content on social media platforms using a combination of Natural Language Processing (NLP), Deep Learning, and Machine Learning techniques. The system leverages keyword-based detection, sentiment analysis, and contextual NLP (...) methods to identify posts indicative of suicidal ideation. A hybrid approach is adopted, integrating Logistic regression,Random Forest, Support Vector Machine, Naive Bayes and Artificial Neural Networks (ANNs) to enhance text classification performance and improve detection accuracy. The model is trained on a carefully curated dataset of labeled posts and evaluated using metrics such as precision, recall, and F1-score, demonstrating significant improvements in the accurate detection of concerning content. This automated system is designed for deployment by social media platforms to flag potentially harmful posts, allowing mental health professionals to review and provide timely support. By enhancing early identification and intervention efforts, this system offers a valuable tool for reducing suicide risks and potentially saving lives. In cases where suicidal content is identified, the system prompts the user with a supportive questionnaire, text box, or chatbot interface to offer immediate assistance or guidance. This project contributes to mental health awareness by providing an automated, accurate, and accessible tool for early detection of suicidal ideation, facilitating timely intervention and support. (shrink)

Security and privacy are the major concerns in the internet of things (IoT) which are uncertain and unpredictable. Trust aware routing is one of the recent and effective strategies which ensure better resilience for IoT nodes from different security threats. Towards such concern, this paper proposes a new strategy called independent onlooker withstanding trust aware routing (IOWTAR) for IoT. IOWTAR introduced a new compound trust metric by combining three individual metrics namely independent trust, onlooker trust, and withstanding trust (a combination (...) of resilient trust and immovability trust). Independent trust and onlooker trust are assessed based on direct and indirect experiences of nodes about their neighbor nodes. Withstanding trust is assessed based on the stability and resilience of nodes towards dynamic topological changes and network failures respectively. Further, this work adapted the Firefly algorithm (FFA) to optimize the weights of individual trusts and establishes a secure path. Simulation experiments carried out over the proposed method had shown its superiority in terms of packet delivery ratio, delay, and throughput. The proposed method has gained an average improvement in the throughput is of 23.71%, 20.18%, 17.27%, and 2.88% from PSO, GSA, WOA, and CBBMOR-TSM-IOT methods respectively. (shrink)

This volume offers very selected papers from the 2014 conference of the “International Association for Computing and Philosophy” (IACAP) - a conference tradition of 28 years. - - - Table of Contents - 0 Vincent C. Müller: - Editorial - 1) Philosophy of computing - 1 Çem Bozsahin: - What is a computational constraint? - 2 Joe Dewhurst: - Computing Mechanisms and Autopoietic Systems - 3 Vincenzo Fano, Pierluigi Graziani, Roberto Macrelli and Gino Tarozzi: - Are Gandy Machines really local? (...) - 4 Doukas Kapantais: - A refutation of the Church-Turing thesis according to some interpretation of what the thesis says - 5 Paul Schweizer: - In What Sense Does the Brain Compute? - 2) Philosophy of computer science & discovery - 6 Mark Addis, Peter Sozou, Peter C R Lane and Fernand Gobet: - Computational Scientific Discovery and Cognitive Science Theories - 7 Nicola Angius and Petros Stefaneas: - Discovering Empirical Theories of Modular Software Systems. An Algebraic Approach. - 8 Selmer Bringsjord, John Licato, Daniel Arista, Naveen Sundar Govindarajulu and Paul Bello: - Introducing the Doxastically Centered Approach to Formalizing Relevance Bonds in Conditionals - 9 Orly Stettiner: - From Silico to Vitro: - Computational Models of Complex Biological Systems Reveal Real-world Emergent Phenomena - 3) Philosophy of cognition & intelligence - 10 Douglas Campbell: - Why We Shouldn’t Reason Classically, and the Implications for Artificial Intelligence - 11 Stefano Franchi: - Cognition as Higher Order Regulation - 12 Marcello Guarini: - Eliminativisms, Languages of Thought, & the Philosophy of Computational Cognitive Modeling - 13 Marcin Miłkowski: - A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience - 14 Alex Tillas: - Internal supervision & clustering: - A new lesson from ‘old’ findings? - 4) Computing & society - 15 Vasileios Galanos: - Floridi/Flusser: - Parallel Lives in Hyper/Posthistory - 16 Paul Bello: - Machine Ethics and Modal Psychology - 17 Marty J. Wolf and Nir Fresco: - My Liver Is Broken, Can You Print Me a New One? - 18 Marty J. Wolf, Frances Grodzinsky and Keith W. Miller: - Robots, Ethics and Software – FOSS vs. Proprietary Licenses. (shrink)