Journal of Artificial Intelligence & Cloud Computing
Editor-In-Chief Xinchao Zhao Professor School of Science Beijing University of Posts and Telecommunications China Journal of Artificial Intelligence & Cloud Computing is an open-access, peer-reviewed journal dedicated to research and developments in Artificial Intelligence, Cloud Computing, Machine Learning, and emerging digital technologies. The journal provides a platform for researchers, academicians, technology professionals, and industry experts to share innovative research, practical applications, and emerging trends in AI and cloud-based technologies. The journal welcomes submissions in the form of Letter-to-Editor, Conference Proceedings, Research, Review, Editorials, Opinions, Short Communications, Mini-Reviews, Book Reviews, Video Articles, and PowerPoint Presentations (PPTs). Call for Papers: Special Issue on Agentic AI and Intelligent Cloud Systems: New Frontiers in Autonomous Computing Journal of Artificial Intelligence & Cloud Computing invites researchers, scientists, academics, technology professionals, and industry experts to contribute original and high-quality manuscripts to our upcoming Special Issue on “Agentic AI and Intelligent Cloud Systems: New Frontiers in Autonomous Computing.” This Special Issue focuses on emerging developments in Agentic AI, autonomous AI agents, Large Language Models, multi-agent systems, intelligent cloud computing, autonomous cloud orchestration, AI-driven resource management, edge-cloud intelligence, cloud security, and next-generation autonomous computing systems. Articles can be submitted through online from https://srcpublishers.com/ai-cloud-computing/user/register or can send through an e-mail with attachment for artificialintelligence@onlinescientificresearch.net or jaicc@onlinescientificresearch.info 0.5 Impact Factor 84.5% Citations 65% Acceptance
Artificial Intelligence and Indigenous Language Revitalization: A Gadé Language Digitization Model
The marginalization of low-resource indigenous languages within contemporary Artificial Intelligence (AI) and Natural Language Processing (NLP) ecosystems poses a significant threat to global linguistic diversity and cultural heritage preservation. This study presents a comprehensive AI-driven Gadé Language Digitization Model designed to support the revitalization, preservation, and technological integration of the Gadé language of North-Central Nigeria. As a tone-rich Niger-Congo language characterized by complex morphosyntactic structures, nuanced semantic expressions, and an indigenous philosophical framework rooted in Gaboism, Gadé presents substantial challenges to conventional Large Language Models (LLMs), which often fail to adequately represent tonal distinctions and culturally embedded meanings. To address these limitations, the study proposes an integrated computational framework incorporating a Tone-Aware Automatic Speech Recognition (ASR) system that maps fundamental frequency (f0) contours to native orthographic markers, alongside a Neural Machine Translation (NMT) architecture enhanced by custom subword tokenization optimized for Niger-Congo linguistic patterns. Guided by principles of community-led data sovereignty, linguistic authenticity, and ethical AI development, the model utilizes a high-quality annotated corpus developed collaboratively with native speakers, community elders, and the Institute of Gade and Classical Studies. Experimental evaluations indicate notable reductions in Word Error Rate (WER) and significant improvements in translation quality and semantic preservation when compared with baseline multilingual models. Beyond technological innovation, the proposed framework establishes a scalable digital repository, educational infrastructure, and language technology ecosystem for Gadé. The study demonstrates how AI can serve as a transformative instrument for indigenous language revitalization and provides a replicable blueprint for preserving endangered African languages in the digital age.
Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
End-To-End Framework for Real-Time Drone Detection and Alerting Using Lightweight Deep Learning
Real-time drone detection is a significant issue in airspace security and privacy because of the increasing use of UAVs; this issue affects airport security directly as it results in various inclusions such as airport incursions and unauthorized surveillance. We proposed end-to-end framework for real time drone detection and alerting integrating multiple YOLO architectures, our system trained on a largescale composite dataset of over 77,000 labeled images for drones, expanded to approximately 90,000 through targeted data augmentation. The Yolo models first trained and evaluated independently, then combined using ensemble learning strategies to reduce false detections while preserving real-time performance. The final ensemble of (YOLOv8, YOLOv11, and YOLOv12) achieves 96.1% accuracy and 93.9% mAP@0.5 Index Terms—Drone Detection, YOLO, Ensemble Learning, Real-Time Alert Systems, Unmanned Aerial Vehicles (UAVs), Lightweight DL Architecture.
Formation of Efficient BIM Teams for Integration into ConstructionCompanies
The integration of Building Information Modeling (BIM) into construction companies depends less on software acquisition than on the formation of competent, well-structured teams capable of sustaining collaborative digital workflows across the project lifecycle. The objective of this study was to analyze the roles, competencies, organizational barriers, and emerging technological demands that shape the formation of efficient BIM teams. A qualitative documentary approach, structured as a scoping review inspired by the PRISMA-ScR protocol, was adopted, drawing on 15 sources indexed in Scopus, ScienceDirect, and related academic databases, selected for their direct relevance, methodological rigor, and recency. The findings show that BIM role nomenclature remains highly fragmented, with as many as 35 distinct role titles identified across 36 international guides and standards, while competency frameworks converge on a three-tier structure distinguishing core, domain, and execution competencies. Organizational adoption is shaped by an interacting set of technical, financial, organizational, and human barriers, particularly acute in small and medium-sized enterprises, and is substantially supported by Common Data Environments, whose practical implementation nonetheless remains challenged by tool multiplicity and governance gaps. Industry data indicate that poor communication and inaccessible project information account for up to 52% of rework globally, underscoring the centrality of coordinated team structures. The accelerating integration of artificial intelligence and machine learning into BIM workflows is reshaping the competency profile expected of BIM teams, while structured training models remain unevenly available, especially for site-level operatives. It is concluded that efficient BIM team formation requires the simultaneous alignment of role clarity, competency development, collaborative information infrastructure, and organizational culture, with direct implications for construction management practice and for curricular design in architecture and engineering education.
Construction Management Organizational Adoption
Self-Governing Cloud Performance: an MCP-Orchestrated MultiAgent Blueprint for Autonomous SLA Assurance in Multi-Tenant Systems
This paper presents a practitioner-oriented architectural blueprint for deploying self-governing performance management in multi-tenant cloud systems. Unlike prior theoretical treatments of AI-driven operations, this work provides DevOps Cloud Solutions Architects with concrete implementation patterns, deployment runbooks, infrastructure-as-code templates, and design decisions for constructing a multi-agent system orchestrated through the Model Context Protocol (MCP). The proposed architecture employs five coordinated agents, each bound by SLA-aware governance policies, that autonomously manage the full performance lifecycle from telemetry ingestion through remediation execution. We detail practical integration patterns for connecting agents to existing observability stacks (Prometheus, Grafana, Datadog), container orchestrators (Kubernetes, ECS), and CI/CD pipelines (ArgoCD, GitHub Actions). Based on published industry benchmarks and analytical modeling, the framework projects a 60-75% reduction in Mean Time to Resolution, SLA compliance exceeding 99.95%, and a 30-40% decrease in infrastructure spend. This paper provides the missing implementation bridge between academic agentic AI research and production-grade cloud operations.
Supporting Partnerships in Participatory, User-centered Co-Design of Artificial Intelligence Mediated Devices for People with Disabilities: Identifying Needs and Opportunities
One billion people, fifteen percent of the world’s population, have experienced a disabling condition that impacts their physical or cognitive function according to the World Bank Group. Such disabilities can result in challenges that present major barriers to entering and persisting in the labor force. In the United States, at least 80% of people with disabilities were not part of the 2024 labor force according to the US Bureau of Labor Statistics. This disparity has been particularly pronounced in technology-related employment. Only 3% of the science, technology, engineering, and math (STEM) workforce are people with disabilities and representation in STEM workforces remains relatively unchanged from a decade ago, despite an increase in the number of people with disabilities across professions since 2011. Americans with disabilities earn only 66 cents for every dollar earned by their counterparts without disabilities. While employment opportunities world-wide in technology industries continue to grow, they are largely inaccessible to people with disabilities. This inaccessibility is primarily because standard computing interfaces are nearly impossible to operate for many people with disabilities, especially those with physical challenges. The research presented in this paper explores the challenges that people with physical disabilities have in accessing and using technologies and provides merit for the role that artificial intelligence (AI) and machine learning may play in changing the outcomes for people with disabilities. Such adaptations may provide people with disabilities with full accessibility to technology when it is facilitated by AI powered, machine learning mediated accessibility devices. This research presents a structure and process for obtaining information about the individual and collective needs of people with physical disabilities in designing accessible hardware and associated software wrappers to meet their needs. Furthermore, it highlights promise for working with people with disabilities in co-design of technology that will meet such identified needs. Results of this research indicates that people with disabilities can play a significant role in designing, testing and developing accessibility tools for themselves and others associated with technology use in education as well as in employment.
The Anti-Enlightenment Mentality in the Law
If Europe imagined the Enlightenment, it was America that realized it [1]. In one of his many letters to Thomas Jefferson, John Adams wrote that "the 18th century, notwithstanding all its errors and vices, has been, of all that are past, the most honorable to human nature [2]." The period from the last quarter of the 16th Century through the end of the 18th Century has often been characterized as "The Enlightenment." During this period, various philosophers began the intellectual detachment from medieval scholasticism and its mystical base [3]. The result was an emphasis on an empiricism that supported what was viewed as a healthy skepticism. The leaders of this movement included such writers as Copernicus, Galileo, John Locke, David Hume, Voltaire, Montesquieu, and scientists such as Benjamin Franklin. Collectively, particularly in the 18th Century, they were known as philosophes and were acknowledged as the intellectual leaders of the time. Their work began with inquiry into the natural sciences and its extension into politics and economics that were at least as impactful. While it is easy to suggest that they were anti-clerical, indeed to some extent hostile to religion, that sells them short. It is true that they did not concern themselves with extensive religious debates, but they never completely divorced thought from its traditional religious origins. They simply went beyond it in their thought process, driven as it was with an emphasis on the distinction between what is "material" and "mental or ideal". Put another way, the "empirical" view of the philosophes was grounded in observation of factually definable phenomena. Samuel Johnson pointed this out in the act of kicking a stone as a means to refute the arguments of Bishop Berkeley as to what exists and what does not [4].
Honorable Xiąże John McClellan Marshall John McClellan Marshall
From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations
A framework for the scalable analytics of diesel engine performance across global fleet operations is presented. Key performance metrics from raw sensor data are defined, demonstrating a clear link to business objectives such as fuel consumption and maintenance tackling times. Modelling such metrics requires appropriate sensor data (estimated here at 25 to 35 signals) underpinned by data processing and feature engineering activities, supported by both real-time and batch solutions. Performance from the analytics framework is achieved through a breadth of modelling approaches: time-series modelling of performance drift, classification modelling of engine failure avoidance, and popular machine-learning performance-influence exploration. Supporting infrastructure is within the Azure Databricks ecosystem, alongside a dialogue with fleet operations through an Microsoft Azure application. The solution, wholly designed and implemented by a single data scientist, provides all components required for production-ready analytics. Results are being deployed within a multi-Cloud platform that extends from raw sensor data through to business summary dashboards, encompassing a broad range of analytics beyond those for diesel engines.
Data Quality Engineering Frameworks for Regulatory-Grade AML Transaction Monitoring Systems
Anti-Money Laundering (AML) systems function within tightly enforced compliance architecture defined by global regulators. The increasing scale and sophistication of financial fraud and money laundering have made robust AML controls critically important. Breakdowns in AML controls can expose institutions to reputational damage, regulatory penalties, and systemic financial risk. These challenges necessitate the adoption of systematic, data-driven approaches for effective risk detection and mitigation. Contemporary AML platforms integrate rule engines with machine learning models to evaluate transactional risk patterns, various risk scoring models demonstrating data integrity checks across multiple stages of data lifecycle. Despite these advancements, financial crime incidents continue to rise, indicating gaps in current system effectiveness. Still a lot needs to be explored in data engineering to help in data analysis and resolution. This paper proposes data quality engineering (DQE) model for regulatory grade AML environments. DQE framework focusses on 5 data quality dimensions - completeness, accuracy, consistency, timeliness, and lineage integrity. This architecture uses Microsoft Azure, Azure Databricks and data will be stored in Delta Lake. Technical validations performed using above model also aligns with the regulatory compliances and obligations under the Bank Secrecy Act (BSA), Financial Action Task Force (FATF) recommendations, and Office of Foreign Assets Control (OFAC). This study hypothesizes that DQE implementation can reduce false negatives and can improve financial crime detection.
Copyright (c) 2022 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
On the Physical Chemistry of the Greenhouse Effect
In the present, a looming catastrophe is feared due to climate change. People are looking for ways to prevent the anticipated warming of the climate. The primary approach promoted is the reduction of carbon dioxide, CO2, emissions from human activities. This would require a major transformation of existing ways of life and production. There are legitimate doubts about the reasoning that human-generated CO2 is the main cause of global warming. We examined the physico-chemical properties of CO2 and the so-called greenhouse gases to determine their influence on the heat exchange between the Earth's atmosphere and space. Their role as converters of infrared radiation from the sun into heat is a fact; conversely, they are equally indispensable as transmitters of heat from the Earth's atmosphere into infrared radia-tion to balance the Earth's radiation equilibrium. The balances of CO2 exchange with the plant world and the oceans, in comparison with anthropogenic emissions, are critically assessed. The anthropogenic energy flow is four orders of magnitude smaller than that of daily solar radiation. The planetary regulatory system of ice ages and warm periods, which has been operating for hundreds of thousands of years, is being discussed. It must be taken into account in the climate discussion of the past centuries. The CO2 as the driving force in the CO2-T correlation is being questioned.
A Framework for Enterprise Agentic AI: Multi-Agent Architectures and ROI-Driven Adoption
The rapid advancement of agentic artificial intelligence (AI) is transforming how intelligent systems are designed, deployed, and monetized across research and enterprise environments. Unlike traditional generative AI or rule-based automation, AI agents introduce autonomous, goal-driven capabilities that can reason, plan, collaborate, and execute actions across complex workflows. This session presents a focused examination of the latest trends, enabling protocols, and measurable return on investment (ROI) driving the adoption of AI agents. The talk highlights key developments such as multi-agent architectures, tool-augmented reasoning, retrieval-augmented generation (RAG), and scalable agent orchestration frameworks that enable coordinated intelligence. It further explores emerging protocols and standards, including model context exchange, agent communication schemas, and governance mechanisms essential for interoperability, security, and compliance. A central emphasis is placed on the economic and operational impact of AI agents. Drawing from real-world enterprise implementations, the session demonstrates how organizations achieve measurable ROI through productivity gains, cost optimization, cycle-time reduction, and revenue enablement. The presentation concludes with a pragmatic roadmap for responsibly scaling agent-based systems from experimentation to sustainable value creation.
The Foundations and Results of Parallel Computing in Terms of Gaisi Takeutis’ Quantum Set Theory
The foundations and results of parallel computing in terms of Gaisi Takeutis’ quantum set theory are presented in this talk. This approach is based on • The extension of the von Neumann’s uniqueness theorem for the solutions of the canonical commutations relations in quantum mechanics of finitely many degrees of freedom to quantum local field theories of infinitely many degrees of freedom; • The results of the mathematician Gaisi Takeuti about quantum set theory in extension theory; and • The results of the physicist Lev Davidovics Landau about the behavior of the quantum liquids He3 and He4 resulting in a more general approach than the one based on the notion of the quantum bit.
Building Partnerships in Participatory, User-centered Design of Artificial Intelligence Mediated Devices for People with Disabilities
One billion people, fifteen percent of the world’s population, have experienced a disabling condition that impacts their physical or cognitive function according to the World Bank Group. Such disabilities can result in challenges that present major barriers to entering and persisting in the labor force. In the United States, at least 80% of persons with disabilities were not part of the 2024 labor force according to the US Bureau of Labor Statistics. This disparity has been particularly pronounced in technology related employment. Only 3% of the science, technology, engineering, and math (STEM) workforce are people with disabilities and representation in STEM workforces remain relatively unchanged from a decade ago despite an increase in the number of people with disabilities across professions since 2011. Americans with disabilities earn only 66 cents for every dollar earned by their counterparts without disabilities. While employment opportunities world-wide in technology industries continue to grow, they are largely inaccessible to people with disabilities. This inaccessibility is primarily because standard computing interfaces are nearly impossible to operate for many people with disabilities. This presentation will discuss the results of research on the role that artificial intelligence (AI) and machine learning plays on changing the outcomes on people with disabilities, by providing them with full accessibility to technology when it is mediated by AI powered, machine learning mediated accessibility devices. It presents a structure and process for obtaining information about the individual and collective needs of people with disabilities in designing accessible hardware and associated software wrappers to meet their needs and then working with them in technology design and use. Results reveal that people with disabilities play a significant role in designing, testing and developing accessibility tools for themselves and others associated with technology use in both education and employment.
Network Function Virtualization (NFV) and Software-Defined Networking (SDN) Integration for Network Management
Modern network infrastructures are evolving toward increased automation, scalability, and flexibility. Two key technologies: Network Function Virtualization (NFV) and Software-Defined Networking (SDN) have emerged as critical enablers for this transformation. NFV decouples network functions from proprietary hardware by virtualizing them on general-purpose servers, while SDN introduces programmability by separating the control plane from the data plane. However, when deployed independently, these technologies cannot fully realize the vision of dynamic and intelligent network management. This research explores the integration of NFV and SDN as a unified architecture for automated network management and orchestration. The study investigates architectural frameworks, interoperability challenges, and performance implications through analytical modeling and simulation using open-source platforms such as OpenStack, ONOS, and OPNFV. Results demonstrate that NFV–SDN integration improves service agility, resource utilization, and fault recovery efficiency, thereby advancing the capabilities of next-generation networks. The paper concludes by outlining future research directions, including AI-driven orchestration, security automation, and edge-cloud convergence for intelligent network control.
Network Management and Orchestration (MANO)
AI for Social Good and Responsible Innovation
As artificial intelligence becomes increasingly embedded in the tools we use every day, the way we work, learn, and lead is evolving rapidly. This talk explores how AI can be leveraged as a force for social good—enhancing productivity and decision-making not by replacing people, but by augmenting human potential and promoting inclusion. Drawing on real-world examples, the session highlights strategies for designing responsible, human-centered AI systems that address societal challenges, support equitable access, and create positive social impact. Emphasis is placed on ethical implementation, transparency, and inclusive innovation, demonstrating how AI can drive meaningful results while contributing to sustainable growth and the well-being of diverse communities.
Automatic Detection and Analysis of Extremist Video Content on Social Media Using Text
Our prior work on radicalization focuses on Increasing our understanding of online radicalization efforts from analyzing right and left-leaning group videos in social media including groups from outside the USA. We studied what viewers found the videos appealing, what techniques were employed in producing radical content, and how we could computationally identify these techniques. More recently we have worked to identify potentially effective de-radicalizing techniques and worked to build a unified multimodal processing pipeline toolkit to help us create conversational systems on social media that are useful in de-radicalizing viewers.
Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments
Blockchain-Enhanced AI: Securing Data Pipelines in Hybrid Cloud Environments: The data is vulnerable to tampering and breaches, which would completely undermine the accuracy of AI-driven insights, when running data pipelines in hybrid cloud setups.Blockchain Enhanced AI is a framework that combines the unchangeable records of blockchain technology with AI to check the accuracy of real-time data in multi-cloud systems. This can be done with the help of smart contracts that automatically flag anomalies, using sophisticated machine learning models such as LSTM networks. We can virtually eliminate the possibility of man-in-the-middle attacks by hashing the data blocks and validating them via consensus algorithms. In software engineering, the process streamlines the DevOps pipeline and reduces the risk of breaches by 30% on simulations in AWS and Azure. At the heart of the framework lies a robust core architecture and also includes privacy-preserving zero-knowledge proofs, and we’ve put this system to the test with empirical results in prototypes processing enormous petabyte-scale datasets. Attendees will be able to learn about the real-world implementation of this technology, challenges to scaling, and the ethical concerns around decentralized AI governance, and in doing so will help in developing more secure cloud-native applications.
AI and ML Techniques for Machine Health Condition Monitoring
Reliable machine condition monitoring systems are critically needed in industries to recognize equipment defects at their earliest stage so as to improve production quality, operation efficiency and safety. An AI and machine-learning (ML)-based monitoring system consists of modules such as data acquisition, signal processing, fault diagnosis and prognosis. Smart sensor-based data acquisition systems are used to collect signals wirelessly. Signal processing is a procedure to extract representative features from measurement for system analysis and fault detection in machinery systems. Diagnosis is a procedure to classify features/patterns into different categories corresponding to different equipment health states. Prognosis is a process to predict the remaining useful life of the damaged equipment to schedule predictive maintenance operations. New AI tools such as adaptive neurofuzzy and evolving fuzzy techniques are used in automatic diagnostic and prognostic operations. Appropriate ML algorithms can be used to improve decision-making convergence and adaptive capability to accommodate different machinery conditions.
AI-Driven Data Engineering for Intelligent Debt Resolution and Financial Wellness
Rising global debt presents a significant challenge that affects not only financial stability but also mental well-being and access to opportunities, while traditional debt recovery methods remain reactive and lack personalization. This work proposes an AI-driven data engineering framework that leverages behavioral data, public datasets, and social signals, combined with real-time processing technologies such as Kafka, Spark, and BigQuery, to enable early detection of financial distress. Advanced AI models, including risk classification, sentiment analysis, and recommendation systems, identify at-risk individuals, assess their readiness for support, and deliver personalized interventions. The approach outlines a comprehensive financial empowerment journey that moves from detection and engagement to guidance and long-term support, helping individuals achieve sustainable financial health. Emphasizing privacy, transparency, and ethical AI practices aligned with global standards, this framework aims to improve early intervention outcomes, reduce debt burdens, and shift the focus from reactive recovery to proactive financial empowerment.
DecepNet: A Hybrid NLP Framework for Robust Phishing Detection Under Adversarial Conditions
Phishing attacks continue to evolve with increasing sophistication, particularly with the emergence of AI-generated content that closely mimics legitimate communication. Traditional phishing detection systems, which rely on static text classification models, often struggle to generalize across diverse datasets and fail under subtle adversarial modifications. In this work, we propose DecepNet, a hybrid phishing detection framework that integrates natural language processing with structural and behavioral feature analysis to enhance detection performance and robustness. The proposed system combines TF-IDF-based representations and transformer-based semantic modeling with a learned deception function designed to capture persuasive intent through linguistic, contextual, and behavioral signals such as urgency, authority cues, and threat-reward framing. The model is trained on a multi-source dataset constructed from publicly available corpora, including Apache Spam Assassin and Enron Spam Dataset, along with additional phishing samples derived from URL-based sources and synthetically generated adversarial data. To evaluate robustness, we introduce an adversarial testing setup involving paraphrasing, tone modification, and obfuscation techniques. Experimental results demonstrate that conventional machine learning models experience noticeable performance degradation under such conditions, while the proposed framework maintains improved stability and generalization across datasets. Comparative analysis with baseline models, including Logistic Regression, Random Forest, and transformer-based classifiers, highlights the effectiveness of integrating deception-aware features. This study emphasizes the importance of combining semantic understanding with behavioral modeling in phishing detection and provides a practical, adaptable framework for addressing evolving social engineering threats in real-world environments.
Energy Supply Infrastructure for the Nechit Monastery: Technical Design and Implementation
This technical documentation outlines the comprehensive engineering design for the modernization and implementation of a reliable electricity supply system for the Monastic House and cell building at the Nechit Monastery, located in Borlești, Neamț County, Romania. The project addresses the necessity for upgraded energy characteristics to support the facility’s 24/7 operating mode, transitioning it from domestic to non-domestic consumer status with a maximum projected power absorption of 210 kW (247.06kVA). The proposed technical solution involves the construction of a new 1960-meter mixed medium-voltage connection. This infrastructure includes a 60-meter Overhead Power Line (OHL/LEA) and a 1900-meter Underground Power Line (LES) utilizing NA2xS(F) 3x(1x150) mm² cabling. Central to the system is the installation of a new pole-mounted transformer substation (PTA 4 Nechit) rated at 20/0.4 kV and 250 kVA. The design meticulously details the placement of concrete pillars (type SC 15014), modular separators, and zinc oxide arresters for surge protection. Furthermore, the documentation specifies strict adherence to Romanian technical norms (NTE, PE) and environmental regulations (GEO 195/2005). It includes detailed safety protocols, quality control measures for earthing installations with dispersion resistance below 1 ohm, and semi-direct electricity metering integrated into the AMR remote reading system. The execution plan, estimated to span five months, ensures that all materials and construction technologies comply with SREN-ISO9001/2001 standards, providing a sustainable and safe energy framework for the monastery’s expansion.
Challenges for Problems in Mean-Field Setting
In this presentation: I will show my previous work about how to deal with the challenges for problems in mean-field setting. One of the biggest challenges lies in the bad properties of the Wasserstein space. For example, it is infinite dimension and it is only a Polish metric space, without any computation properties as Hilbert space. I will present how the challenges can be overcome in some specific problems. One is about solving mean-field control problem in continuous time via reinforcement learning method. Another is about numerically solving mean-field McKean-Vlasov SDE via Euler-Maruyama Scheme. I will show the theoretical results we obtained. At the end, I show the future work.
Professionals’ Perception and Trust in AI Predictions in High-Risk Contexts
This presentation examines how professionals perceive, calibrate, and operationalize trust in AI-driven predictions when decisions carry high stakes and low tolerance for error. We explore the psychological and organizational drivers of trust—such as perceived competence, transparency, accountability, and prior experience—as well as factors that undermine it, including model opacity, uncertainty miscommunication, and automation bias. Building on real-world high-risk scenarios (e.g., healthcare, critical infrastructure, and safety-relevant operations), we discuss how explanation quality, confidence/uncertainty reporting, and humanin- the-loop workflows affect decision quality and responsibility allocation. The talk proposes practical design and governance recommendations to improve appropriate reliance, including uncertainty-aware interfaces, auditability, training for judgment calibration, and socio-technical safeguards that align AI outputs with professional standards and regulatory constraints. The goal is to move beyond “trust vs. distrust” toward measurable, context-sensitive trust calibration that improves outcomes without eroding accountability.
Agentic AI Revolutionizing Software Engineering
Agentic AI marks a paradigm shift in software engineering, empowering autonomous agents to independently pursue goals, reason through complex decisions, and execute multi-step workflows with minimal human intervention. Unlike reactive AI assistants, these systems—powered by advanced large language models—proactively handle code generation, debugging, testing, and deployment, dramatically compressing development cycles while elevating code quality. Agentic AI agents excel in complex tasks like code generation, debugging, and testing. They interpret requirements, generate structured code scaffolds, and self-validate through feedback loops, reducing development cycles significantly. For instance, multiagent systems collaborate—one agent designs architecture while another handles refactoring and compliance checks. In practice, these agents integrate into CI/CD pipelines, managing builds, deployments, and optimizations autonomously. They analyze real-time data from code quality metrics, user feedback, and performance telemetry to prioritize features or detect technical debt preemptively.? However, challenges persist in terms of ensuring ethical decision-making, handling edge cases in open-ended problems, and integrating with legacy systems – necessitating human-AI models.
Non-Replicable Function-Based Multi-Level Random Dynamic Secret Key Algorithm (Extended Version)
Based on the proof that, as an encryption function, the residual function y=(ax) mod m is a non-replicable function, this paper constructs multi-level encryption functions y1=(a1x)mod m1,…, yn=(anyn-1)mod mn. And based on this the paper constructs the multilevel random dynamic secret key algorithm XM-1. XM-1 has three-fold safeguards. First, the encryption function is not replicable. Secondly, the secret keys and the number of secret keys are randomized. Thirdly, when the system does not run, there is no secret key. After slight changes, the four examples for constructing XM-1 given in this paper can be put into practical use.
The Generative AI Sales Paradox (GASP)-Enhancing Sales Scalability at the Cost of Human-Led Relationship Building in B2B Markets
This study examines a growing concern in B2B sales, which it refers to as the Generative AI Sales Paradox (GASP). Generative AI tools help sales teams move faster and reach more clients. Still, they can unintentionally weaken the kind of personal relationships that matter most in high-value, trust-based transactions. This research focuses on industries such as manufacturing, professional services, and enterprise technology, where long-term client trust is essential. Both qualitative and quantitative methods are used in this research. On the qualitative side, six B2B firms using Generative AI in their sales operations were studied. While sales leaders praised the improvements in lead follow-up and speed, they also shared customer concerns about the loss of personal interaction. Clients often felt that AI-generated responses lacked empathy or genuine understanding. Survey data from 250 companies backed this up. While most firms achieved efficiency gains of 25-40%, many also reported a decline in customer satisfaction, particularly those that relied heavily on AI. In these cases, growth in long-term revenue either stalled or declined. The problem appeared to stem from weaker client connections. To help explain these findings, this study used the Scalability vs. Authenticity Trade-Off Theory (SATOT). This framework suggests that while AI is useful for scaling tasks, it struggles with the emotional and social complexity involved in building lasting relationships. In response, this study proposes a blended approach. Let AI handle repetitive tasks, but keep humans in charge of complex, trust-building conversations. This hybrid model can help firms gain efficiency without sacrificing client loyalty. In short, firms that manage AI use carefully, without letting it replace human interaction, are more likely to thrive in the long run.
Audit Ready AI Agentic Coding: ISO 27001 Governance for GitHub Copilot in Financial Services
Fintechs increasingly rely on fast software delivery while facing bank grade expectations for auditability, change control, and data protection. Agentic AI coding is evolving the Software development cycle and in the same time is introducing new governance & compliance challenges. This session presents a practical, ISO/IEC 27001 aligned governance model for deploying GitHub Copilot in GitHub.com (chat and platform features) with “audit ready by design” evidence. We translate ISO 27001 risk treatment and the Statement of Applicability (SoA) into enforceable controls across three layers: (1) Copilot policy governance (enterprise/org policy enforcement for feature availability and model access), (2) agentic visibility and audit trails using GitHub’s AI Controls, including agent session visibility and agentic audit log events to answer “what happened, when, and on whose behalf,” and (3) secure operating practices for regulated SDLC—segregation of duties, mandatory reviews, and evidence collection for audits. Attendees will get a repeatable blueprint: a Copilot-specific SoA pattern, a control to evidence map (policies → logs → audit artifacts), and a minimal governance checklist that preserves developer velocity while strengthening compliance posture. We also discuss how vendor assurance artifacts support third party risk conversations, noting GitHub’s published statement that Copilot Business and Copilot Enterprise are included in the scope of GitHub’s ISMS.
Explainable AI in regulated Environments — Making Compliance a Competitive Advantage
The EU AI Act is coming — and for many medium-sized companies, it feels like a threat rather than an opportunity. But the real challenge isn’t the regulation itself: it’s closing the gap between what AI systems do and what decision-makers can actually explain, audit, and defend. Stephan shows how Explainable AI (XAI) methods can be embedded directly into automation workflows, turning regulatory requirements into a practical framework for trustworthy process automation. The result: AI that mid-market companies can deploy, control, and stand behind — today, not just when the auditor knocks.
From Market Noise to Signal: Machine Learning and Quantitative Alpha in Financial Markets
Theme The convergence of Artificial Intelligence (AI), Quantitative Finance, and Blockchain technologies is reshaping how capital is analyzed, deployed, and optimized across both traditional and decentralized markets. Core Focus • Evolution of Trading: From structured Wall Street quant models to decentralized, data-driven Web3 ecosystems. • AI/ML in Action: Transforming market “noise” into predictive signals through reinforcement learning, deep neural networks, and regime-detection models. • Systematic Risk Control: Application of institutional-grade portfolio management—drawdown control, position sizing, and multi-asset allocation—to highly volatile crypto markets. Methodology & Frameworks • Integration of AI/ML models with advanced backtesting and optimization platforms such as Strategy Quant and LuxAlgo, validated through out-of-sample and Monte Carlo testing. • Use of on-chain analytics, order-flow intelligence, and sentiment data to enhance predictive accuracy. • Emphasis on robustness and transparency—bridging quantitative rigor and explainable AI for practical deployment. Applications • Democratization of institutional-grade tools for retail traders and family offices. • Quantitative wealth-management frameworks for decentralized funds, DAOs, and algorithmic asset allocators. • Emergent AI-agent and blockchain-based ventures building the next generation of autonomous trading, custody, and analytics infrastructure. Key Insight By uniting quantitative discipline, AI innovation, and venture execution, this research proposes a scalable framework for transforming market complexity into actionable clarity—and for capturing sustainable alpha in the next era of intelligent, decentralized finance.
Designing AI Agents for Content Automation: From Prompt Engineering to Autonomous Workflows
The evolution of large language models (LLMs) has significantly transformed how humans interact with artificial intelligence systems. Moving beyond simple prompt-based interactions, modern AI systems are increasingly capable of operating as autonomous agents that can execute complex, multi-step workflows. This session explores how prompt engineering, structured workflows, and orchestration frameworks such as LangChain and LangGraph can be combined to create reliable and scalable AI systems. The presentation includes a practical case study of an AI agent built using these frameworks, demonstrating end-to-end workflow orchestration and real-world applicability. Objectives • Explain the transition from prompt-based systems to agent-based architectures • Demonstrate reliable AI workflow design using modern frameworks • Highlight real-world challenges such as hallucination, consistency, and control • Provide actionable insights for implementing AI agents in practical scenarios Methodology The presentation introduces a structured framework for building AI agents, focusing on practical implementation. Key elements include: 1. Prompt orchestration and chaining 2. Context management and memory handling 3. Decision-making loops and agent workflows 4. Output validation and error handling These concepts are illustrated through a real-world content automation agent. System Architecture & Implementation Approach The proposed framework is demonstrated through a real-world AI agent designed for content automation. The system leverages orchestration frameworks such as LangChain and LangGraph to structure multi-step workflows and enable controlled execution. The architecture consists of the following components: Input Understanding & Context Setup User inputs (e.g., topic, audience, tone) are transformed into structured prompts. Context is enriched using templates and optional memory components to ensure consistency. Prompt Orchestration & Chaining The system breaks down tasks into sequential steps: • Idea generation • Content structuring • Draft creation • Refinement • This improves control and output quality. Agent Workflow & Decision Logic Using a stateful workflow, each step is executed as part of a structured process. The system can: • Re-run steps if needed • Adapt outputs dynamically • Maintain workflow consistency Output Validation & Quality Control Outputs are evaluated against criteria such as tone, clarity, and relevance. Prompt-based validation reduces hallucinations and improves reliability. Final Output Generation The system produces structured, ready-to-use content (e.g., LinkedIn posts), ensuring alignment with user intent and consistency in messaging. Expected Outcomes • Attendees will gain: • A clear understanding of AI agent architectures • Practical knowledge of implementing agent workflows • Insights into handling real-world AI challenges • Exposure to tools and frameworks used in modern AI systems Relevance This topic aligns with the conference themes of artificial intelligence, software engineering, and applied data science. As organizations increasingly adopt AI-driven automation, understanding how to design reliable agent systems becomes critical. This presentation bridges the gap between theoretical AI capabilities and real-world implementation. Conclusion This session provides a structured and practical approach to designing AI agents. By combining theoretical foundations with real world implementation, it highlights how organizations can leverage agentic AI systems to build scalable and reliable automation solutions.
Using Domain Taxonomy to Analyze Tendencies of its Development
There are several approaches to the computerized analysis of tendencies in research within a domain. One should mention most popular approaches such as intercitation analysis, recognition and analysis of most influential studies, and content analysis. This study follows a different approach involving domain taxonomy (hierarchical classification) as the main source of categories and subjects for further analysis. We illustrate working of our approach over the domain of Data Science. We take relevant parts of the ACM Computing Classification System, in a slightly modified and updated form, to serve as the domain taxonomy, so that its obtained 381 leaf concepts are categories under consideration. We downloaded abstracts of 67,757 research papers published in 59 relevant Springer journals from 2003-2024 as the text sample for our analysis. We developed and validated three mathematical methods forming a backbone of our approach. These are: (i) Method for assessment of extent of relevance between categories and texts involves the currently popular idea of using the conditional probability of the last symbol in a string with respect to its previous substring (ii) Method for computing square topic-to-topic co-relevance matrix with an adequate measure involving the so-called Kulczynski index and (iii) A special additive-spectral fuzzy clustering method, FADDIS. These are implemented in our GOT software. We divided the period 2003-2024 into two subperiods and computed a 381,67757 topic-to-text relevance matrix for each of the subperiods. We found seven fuzzy clusters, pertaining to main areas of current interest in data science, at each of the two subperiods. Our clusters show both stability in the research interests across the 22-year period and changes within them. These results demonstrate effectiveness of our approach. The main issue in extending that to other domains is lack of taxonomies. Joint work with Luis Caneco, Susana Nascimento (both from the New Lisbon University, Portugal) and Yana Gagarina, Dmitry Frolov (both from NRU HSE, RF). Support from the Basic Research Program of HSE University in Moscow including computational resources of HPC facilities in the university is gratefully acknowledged.