Tootfinder

Asking the audience where they are on their data journey, on a scale from Junk Shop -> Amazon
#cdl25 #ConnectedData

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Anastasija Nikiforova, Martin Lnenicka, Ulf Melin, David Valle-Cruz, Asif Gill, Cesar Casiano Flores, Emyana Sirait, Mariusz Luterek, Richard Michael Dreyling, Barbora Tesarova
https://arxiv.org/abs/2510.09634

Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. …

DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
Haoran Feng, Dizhe Zhang, Xiangtai Li, Bo Du, Lu Qi
https://arxiv.org/abs/2510.11712 https://

DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training
In this work, we propose DiT360, a DiT-based framework that performs hybrid training on perspective and panoramic data for panoramic image generation. For the issues of maintaining geometric fidelity and photorealism in generation quality, we attribute the main reason to the lack of large-scale, high-quality, real-world panoramic data, where such a data-centric view differs from prior methods that focus on model design. Basically, DiT360 has several key modules for inter-domain transformation a…

The QIC-Index: A Novel, Data-Centric Metric for Quantifying the Impact of Research Data Sharing
Martin G. Frasch
https://arxiv.org/abs/2510.03307 https://a…

The QIC-Index: A Novel, Data-Centric Metric for Quantifying the Impact of Research Data Sharing
We introduce the QIC-Index, a novel metric to address the failure of publication-centric metrics to value research data sharing. The QIC-Index quantifies the impact of individual data objects by calculating a score based on their Quality (Q), Impact (I), and Collaboration (C). By rewarding the sharing of high-quality, impactful, and collaborative data, our framework aligns individual incentives with the goals of open science and aims to foster a more transparent and efficient research culture.

TCDRM: A Tenant Budget-Aware Data Replication Framework for Multi-Cloud Computing
Santatra Hagamalala Bernardin (IRIT-PYRAMIDE, IRIT), Riad Mokadem (IRIT-PYRAMIDE, IRIT), Franck Morvan (IRIT-PYRAMIDE, IRIT), Hasinarivo Ramanana, Hasimandimby Rakotoarivelo
https://arxiv.org/abs/2510.07833

TCDRM: A Tenant Budget-Aware Data Replication Framework for Multi-Cloud Computing
Multi-cloud computing systems face significant challenges in ensuring acceptable performance while adhering to tenant budget requirements. This paper proposes a tenant budget-aware (tenant-centric) data replication framework for Multi-Cloud Computing (TCDRM). The proposed strategy dynamically creates data replicas based on predefined thresholds for response time, economic budget of the tenant and data popularity. TCDRM employs a heuristic replica placement algorithm that leverages the diverse p…

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou, Shuyi Pei, Yangwook Kang, Yufei Ding, Po-An Tsai
https://arxiv.org/abs/2510.05497

Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting
Large Language Models (LLMs) with Mixture of Experts (MoE) architectures achieve remarkable performance improvements, but their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit serving systems. To forecast the patterns underlying this data movement, we conduct comprehensive data-movement-centric profiling across three state-of-the-art large-scale MoE models (200B- 671B) using over 24,000 requests spanning diverse …

Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO
Fabian G\"ottsch, Max Franke, Arash Pourdamghani, Giuseppe Caire, Stefan Schmid
https://arxiv.org/abs/2510.06734 …

Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO
We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the front…

Hi Bluesky! 👋 #datascience, broadly defined, like network science, computational social science, geospatial data science.

KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments
Walid Abdela
https://arxiv.org/abs/2510.10325 https://

KG-MAS: Knowledge Graph-Enhanced Multi-Agent Infrastructure for coupling physical and digital robotic environments
The seamless integration of physical and digital environments in Cyber-Physical Systems(CPS), particularly within Industry 4.0, presents significant challenges stemming from system heterogeneity and complexity. Traditional approaches often rely on rigid, data-centric solutions like co-simulation frameworks or brittle point-to-point middleware bridges, which lack the semantic richness and flexibility required for intelligent, autonomous coordination. This report introduces the Knowledge Graph-En…

Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Asim Ersoy, Enes Altinisik, Husrev Taha Sencar, Kareem Darwish
https://arxiv.org/abs/2509.20957 https://

Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer…

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Hexin Liu, Haoyang Zhang, Qiquan Zhang, Xiangyu Zhang, Dongyuan Shi, Eng Siong Chng, Haizhou Li
https://arxiv.org/abs/2509.24310

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching data further compounds these challenges. In this paper, we systematically analyze CS-ASR from both model-centric and data-centric perspectives. By comparing state-of-the-art algor…

ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Chenyu Liu, Yuqiu Deng, Tianyu Liu, Jinan Zhou, Xinliang Zhou, Ziyu Jia, Yi Ding
https://arxiv.org/abs/2509.22556 h…

ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) address this by pretraining encoder-centric architectures on large-scale unlabeled data to extract universal representations. While effective, these models lack decoders of comparable capacity, limiting the full utilization of the learned features. To address this issue, we introduce ECHO, a novel decoder-centric LEM p…

Data driven approaches in nanophotonics: A review of AI-enabled metadevices
Huanshu Zhang, Lei Kang, Sawyer D. Campbell, Jacob T. Young, Douglas H. Werner
https://arxiv.org/abs/2510.00283

Data driven approaches in nanophotonics: A review of AI-enabled metadevices
Data-driven approaches have revolutionized the design and optimization of photonic metadevices by harnessing advanced artificial intelligence methodologies. This review takes a model-centric perspective that synthesizes emerging design strategies and delineates how traditional trial-and-error and computationally intensive electromagnetic simulations are being supplanted by deep learning frameworks that efficiently navigate expansive design spaces. We discuss artificial intelligence implementati…

Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete
Adithya Rajan, Xiaoyu Liu, Prateek Verma, Vibhu Arora
https://arxiv.org/abs/2510.01574 https://…

Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete
We introduce a data-centric approach for mitigating presentation bias in real-time neural query autocomplete systems through the use of synthetic prefixes. These prefixes are generated from complete user queries collected during regular search sessions where autocomplete was not active. This allows us to enrich the training data for learning to rank models with more diverse and less biased examples. This method addresses the inherent bias in engagement signals collected from live query autocomp…

Percepta: High Performance Stream Processing at the Edge
Clarisse Sousa, Tiago Fonseca, Luis Lino Ferreira, Ricardo Ven\^ancio, Ricardo Severino
https://arxiv.org/abs/2510.05149

Percepta: High Performance Stream Processing at the Edge
The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents …

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality
Conghao Zhou, Jie Gao, Shisheng Hu, Nan Cheng, Weihua Zhuang, Xuemin Shen
https://arxiv.org/abs/2509.25905

User-Centric Communication Service Provision for Edge-Assisted Mobile Augmented Reality
Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric…

One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework
Lorenzo Bianchi, Giacomo Pacini, Fabio Carrara, Nicola Messina, Giuseppe Amato, Fabrizio Falchi
https://arxiv.org/abs/2510.02898 …

One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework
Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually decoding a text-aligned image feature, but they limit their scope to global representations and whole-image captions. We present \frameworkName{}, a unified framework for zero-shot captioning that shifts from an image-centric to a patch-centric paradigm, enabling the captioning of a…

What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review
Sangkeun Jung, Goun Pyeon, Inbum Heo, Hyungjin Ahn
https://arxiv.org/abs/2509.25701 https://

What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review
Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence of open platforms such as OpenReview has transformed peer review into a transparent and interactive process, recording not only scores and comments but also rebuttals, reviewer-author exchanges, reviewer disagreements, and meta-reviewer decisions. This provides unprecedented process-level data for understanding how m…

Machine-interpretable Engineering Design Standards for Valve Specification
Anders Gjerver, Rune Frostad, Vedrana Barisic, Melinda Hodkiewicz, Caitlin Woods, Mihaly Fekete, Arild Braathen Torjusen, Johan Wilhelm Kluwer
https://arxiv.org/abs/2510.01736

Machine-interpretable Engineering Design Standards for Valve Specification
Engineering design processes use technical specifications and must comply with standards. Product specifications, product type data sheets, and design standards are still mainly document-centric despite the ambition to digitalize industrial work. In this paper, we demonstrate how to transform information held in engineering design standards into modular, reusable, machine-interpretable ontologies and use the ontologies in quality assurance of the plant design and equipment selection process. We…

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Evan Heus, Rick Bookstaber, Dhruv Sharma
https://arxiv.org/abs/2510.01115 https://

Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis
Large Language Models (LLMs) struggle with the complex, multi-modal, and network-native data underlying financial risk. Standard Retrieval-Augmented Generation (RAG) oversimplifies relationships, while specialist models are costly and static. We address this gap with an LLM-centric agent framework for supply chain risk analysis. Our core contribution is to exploit the inherent duality between networks and knowledge graphs (KG). We treat the supply chain network as a KG, allowing us to use struc…

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
Shiju Wang, Yujie Wang, Ao Sun, Fangcheng Fu, Zijian Zhu, Bin Cui, Xu Han, Kaisheng Ma
https://arxiv.org/abs/2509.21275

Data-Centric Elastic Pipeline Parallelism for Efficient Long-Context LLM Training
Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) reduces this cost, but its effectiveness hinges on partitioning granularity. Batch-level PP dividing input samples exhibits high memory consumption in long-context scenario, whereas token-level PP splitting sequences into slices alleviates memory overhead but may incur hardware under-utilization. This trade-off motivate…

EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Sen Yang, Yu Bao, Yu Lu, Jiajun Chen, Shujian Huang, Shanbo Cheng
https://arxiv.org/abs/2509.19770 htt…

EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation. This work addresses this limitation through a synthetic data generation framework that leverages models' established English-to-x (en2x) capabilities. By extending English parallel corpora into omnidirectional datasets and developing an English-referenced quality evaluation proxy, we enable effective collection of hig…