edge computing

"XL-I2P: Cross-Layer I2P Darkweb Mapper"

"This dataset contains the application-layer measurement data underlying the comparative validation study \"XL-I2P: Cross-Layer I2P Darkweb Mapper.\" It comprises two eepsite crawl runs conducted sequentially on a single fully integrated I2P vantage node under identical infrastructure conditions: a run of the proposed XL-I2P framework and a run of the established c4i2p baseline of Mag\u00e1n-Carri\u00f3n et al. For each run, the dataset provides the recovered eepsite hyperlink graph as node and edge lists (CSV), the underlying relational tables sites, pages, links, crawl attempts, and seed provenance in both CSV and compressed SQL-dump form, and the computed summary metrics. The XL-I2P run recovered 1,400 reachable eepsites and 366,304 directed hyperlinks; the c4i2p baseline recovered 105 eepsites and 2,337 hyperlinks. The data supports reproduction of every table and figure in the associated paper, direct comparison of the two crawlers' coverage and resilience, and longitudinal extension of I2P application-layer measurement.All eepsite identifiers are I2P .b32.i2p addresses already discoverable through the public subscription feeds documented in the paper. The dataset includes page-level content metadata page titles, hyperlink anchor text, content length, word counts, and content hashes to support content-aware analysis; full page bodies are not stored. Network-layer and cross-layer observation tables are excluded from this release, as the cross-layer pipeline is implemented but not empirically evaluated in the associated study."

基于量子计算的动态拓扑网络优化

This paper presents a novel algorithm for dynamic topology optimization, leveraging the unique properties of quantum computing to dynamically adjust network parameters. The core mechanism utilizes quantum entanglement and superposition to efficiently compute and optimize the topology of a network, leading to improved performance compared to traditional optimization techniques. We propose a quantum-enhanced approach to network design, focusing on the dynamic adjustment of node and edge weights to achieve optimal network behavior. This work explores the potential of quantum computation to address challenges in network routing, congestion control, and resource allocation, offering a promising avenue for enhancing network efficiency and resilience. The proposed algorithm demonstrates improved results through extensive simulations and theoretical analysis, highlighting the potential for significant advancements in network management.

Walk-LCP: Least-cost walking paths over a DEM with Tobler's hiking function

Walk-LCP computes least-cost walking paths and travel times across a digital elevation model using Dijkstra's algorithm with edge costs derived from Tobler's hiking function. It also tests pairwise viewshed overlap and combined visible area against a matched null model of random point pairs drawn from the same terrain and matched on elevation and separation. The package is provided as a reproducibility companion to a journal manuscript and supports both standalone (Python + GDAL + GeoPandas) and QGIS-bound execution.

A Device-Agnostic Framework for Personalized Anomaly Detection in Wearable and Mobile Ecosystems: Applications in Personal Safety and Respiratory Health Surveillance

Abstract—This paper presents a reformulated, device-agnostic architectural framework for two interrelated applications: (i) personalized audio-based personal safety threat detection, and (ii) respiratory pattern anomaly detection for early-stage epidemiological screening. Originating from an ideation dialogue, the proposed concepts are critically re-examined against fundamental constraints in machine learning, embedded systems, privacy engineering, and sensor heterogeneity. We propose a generalized abstraction layer that decouples sensing modality from inference logic, enabling deployment across heterogeneous smart devices (wearables, smartphones, IoT nodes) without hardware-specific dependencies. We address the zero-positive-example training problem through a reformulation as one-class classification and temporal anomaly detection. Privacy is preserved via on-device federated feature extraction with no raw data transmission. We further analyze the practical limitations identified by domain experts and propose mitigations grounded in current literature. The framework is positioned as a foundational architecture rather than a deployable product, inviting interdisciplinary validation. Impact Statement—By introducing a sensing abstraction layer and reformulating personal safety and respiratory monitoring as one-class anomaly detection problems, this work provides a portable, privacy-preserving architecture that can operate across heterogeneous consumer devices without hardware-specific redesign. The framework addresses the zero-positive-example constraint inherent to rare-event detection and offers a structured research agenda for empirical validation, potentially accelerating the development of reliable, edge-deployed health and safety monitoring systems. Index Terms—Anomaly detection, one-class classification, device-agnostic computing, respiratory pattern analysis, personal safety systems, federated learning, wearable computing, edge inference.

A Device-Agnostic Framework for Personalized Anomaly Detection in Wearable and Mobile Ecosystems: Applications in Personal Safety and Respiratory Health Surveillance

Abstract—This paper presents a reformulated, device-agnostic architectural framework for two interrelated applications: (i) personalized audio-based personal safety threat detection, and (ii) respiratory pattern anomaly detection for early-stage epidemiological screening. Originating from an ideation dialogue, the proposed concepts are critically re-examined against fundamental constraints in machine learning, embedded systems, privacy engineering, and sensor heterogeneity. We propose a generalized abstraction layer that decouples sensing modality from inference logic, enabling deployment across heterogeneous smart devices (wearables, smartphones, IoT nodes) without hardware-specific dependencies. We address the zero-positive-example training problem through a reformulation as one-class classification and temporal anomaly detection. Privacy is preserved via on-device federated feature extraction with no raw data transmission. We further analyze the practical limitations identified by domain experts and propose mitigations grounded in current literature. The framework is positioned as a foundational architecture rather than a deployable product, inviting interdisciplinary validation. Impact Statement—By introducing a sensing abstraction layer and reformulating personal safety and respiratory monitoring as one-class anomaly detection problems, this work provides a portable, privacy-preserving architecture that can operate across heterogeneous consumer devices without hardware-specific redesign. The framework addresses the zero-positive-example constraint inherent to rare-event detection and offers a structured research agenda for empirical validation, potentially accelerating the development of reliable, edge-deployed health and safety monitoring systems. Index Terms—Anomaly detection, one-class classification, device-agnostic computing, respiratory pattern analysis, personal safety systems, federated learning, wearable computing, edge inference.

Artificial intelligence-driven resource management in edge-cloud computing continuum for internet of things applications: new trends and future directions

The rapid proliferation of Internet of Things (IoT) devices across domains such as smart transportation, healthcare, and smart city infrastructure has intensified the demand for low-latency, energy-efficient, and scalable computing paradigms. While cloud computing has traditionally served as the backbone for IoT data processing, its inherent limitations have catalyzed the emergence of edge/fog computing, forming a distributed edge-cloud continuum. This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: (1) the transition from centralized to distributed and collaborative intelligence, (2) cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and (3) the nascent integration of foundation models into edge environments. We ground our discussion in three complementary case studies: smart transportation as a representative vertical domain, cross-domain heterogeneous IoT application scheduling as a horizontal perspective, and industrial transferred arc plasma monitoring as an emerging industrial IoT scenario, and conclude with forward-looking research directions, including quantum-enhanced edge optimization, edge-native continual learning, digital twin-driven resource orchestration, and neuromorphic computing for ultra-low-power edge AI.

CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology

Context: LLM-based multi-agent systems enable automation and decision support in software development, yet existing studies rely on benchmark datasets offering only binary pass-or-fail results, limiting insight into real-world applicability. Objective: This study empirically investigates the potential and limitations of LLM-based agents in autonomous software development tasks. Method: A two-phase approach was employed: developing a multi-agent system, CodePori, for automated code generation, and conducting participant-based evaluation to assess practical performance. Results: Participant feedback reveals key strengths, challenges, and areas for improvement in LLM-based multi-agent systems, highlighting aspects missed by standard code-generation benchmarks. Conclusions: While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective.

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

Collaborative AI experimentation in industry-academia requires environments that support rapid trials while maintaining controlled access, organisational isolation, and traceable workflows. Although interest in AI sandboxes is increasing, practical guidance on designing and building governance-aware experimentation platforms remains limited. This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders. The sandbox was developed in an industry-academia ecosystem using iteratively validated requirements gathered from industrial partners. The solution adopts a layered reference architecture that separates a multi-tenant presentation layer from a backend control plane and isolates execution and data management concerns into dedicated layers. The sandbox supports governed onboarding, project-based collaboration, controlled access to AI services, and traceable experimentation through approval workflows and audit logging. By structuring experiment context and governance decisions as persistent records, the sandbox enables evaluation evidence to be reused and compared across projects and stakeholders. The development experience yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platforms.

TERE4AI: A Knowledge Graph-Based Tool for Generating EU AI Act Compliant Requirements

AI Sandbox: Technical Report

Collaborative AI experimentation across industry and academia requires platforms that enable rapid prototyping while preserving controlled access, tenant separation, and transparent workflows. Despite growing interest in AI sandboxes, there is still limited practical guidance on how to design and implement platforms that integrate experimentation capabilities with governance requirements. This work presents the design and implementation of a governance-aware, multi-tenant AI sandbox for structured experimentation and the generation of reusable evaluation evidence across projects and stakeholder groups. The sandbox was developed within an industry-academia collaboration based on requirements that were iteratively refined with industrial partners. Its reference architecture separates the multi-tenant user interface from the backend control plane and places execution and data-management functions in dedicated layers. The platform supports governed user onboarding, project-centered collaboration, managed access to AI services, approval workflows, audit logging, and traceable experimentation. Experiment configurations, contextual information, and governance decisions are stored as persistent records, allowing evidence and outcomes to be compared and reused across projects. The development process provides practical lessons for deploying and extending governance-aware AI sandbox platforms in collaborative research and industrial environments.

LLM-Based Multi-Agent Systems for Code Generation: A Multi-Vocal Literature Review

Large Language Models (LLMs) have enabled multi-agent systems to perform autonomous code generation for complex tasks. Despite the recent growth in research and industrial applications in this area, there is little work on synthesizing evidence from both academic and industrial sources to capture the current state of research on LLM-based multi-agent systems for code generation. To this end, we conducted a Multi-Vocal Literature Review (MLR), combining insights from both academia and industry, including peer-reviewed studies and grey literature. The aim of this study is to systematically synthesize and analyze existing knowledge on LLM-based multi-agent systems for code generation. Specifically, the review examines the motivations for their use, employed benchmarks and models, key challenges, proposed solutions, and potential directions for future research. We selected and reviewed 114 studies, and the key findings are: 1) the identified reasons for adopting multi-agent systems for code generation were classified into nine categories; 2) the models and evaluation benchmarks utilized across the studies were systematically analyzed to provide a structured overview of commonly adopted LLM configurations and assessment practices; 3) the reported challenges and corresponding solutions were synthesized into six main categories and 26 subcategories; and 4) future research directions were identified and organized into six main categories and 18 subcategories. The results of this MLR will assist researchers and practitioners in pursuing further studies and supporting the real-world adoption of multi-agent systems in industrial settings.