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RequestAtlas: Supporting the Slow and Iterative Process of Requesting Public Records

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Proceedings of the ACM on Human-computer Interaction 2025年第2期9卷 1-35页

作者： Warren, Rachel B. Pickoff-White, Lisa Parameswaran, Aditya G. Salehi, Niloufar University of California Irvine School of Informatics & Computer Sciences IrvineCA United States KQED San Francisco United States University of California Berkeley Department of Electrical Engineering and Computer Sciences Berkeley United States University of California Berkeley School of Information BerkeleyCA United States

Public records requests are a central mechanism for government transparency. In practice, they are slow, complex processes that require analyzing large amounts of messy, unstructured data. In this paper, we introduce RequestAtlas, a system that helps investigative journalists review large quantities of unstructured data that result from submitting many public records requests. RequestAtlas was developed through a yearlong participatory design collaboration with the California Reporting Project (CRP), a journalistic collective researching police use of force and police misconduct in California. RequestAtlas helps journalists evaluate the results of public records requests for completeness and negotiate with agencies for additional information. RequestAtlas has had significant real-world impact. It has been deployed for more than a year to identify missing data in response to public records requests and to facilitate negotiation with public records request officers. Through the process of designing and observing the use of RequestAtlas, we explore the technical challenges associated with the public records request process and the design needs of investigative journalists more generally. We argue that public records requests represent an instance of an adversarial technical relationship in which two entities engage in a prolonged, iterative, often adversarial exchange of information. Technologists can support information-gathering efforts within these adversarial technical relationships by building flexible local solutions that help both entities account for the state of the ongoing information exchange. Additionally, we offer insights on ways to design applications that can assist investigative journalists in the inevitably significant data cleaning phase of processing large documents while supporting journalistic norms of verification and human review. Finally, we reflect on the ways that this participatory design process, despite its success, lays bare some of the l

关键词： adversarial technical relationships participatory design public records requests

Exploring Seasonal Coastal Water Quality Parameter Interactions Through the Self-Organizing Map Neural Network in Eastern Mediterranean: Lebanon as a Case Study

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SSRN

SSRN 2025年

作者： Hadjisolomou, Ekaterini El Rahman Hassoun, Abed Fakhri, Milad Ghanem, Abeer Mina, Roula Herodotou, Herodotos Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus National Center for Marine Sciences P.O. Box 189 Jounieh Lebanon GEOMAR Helmholtz Centre for Ocean Research Kiel Kiel Germany

Coastal water pollution poses significant environmental challenges, leading to severe ecological and public health consequences. Thus, new assessment tools should be developed and refined to manage water quality more efficiently. In this paper, we present the results of Self-Organizing Map (SOM) neural network models utilized to simulate several water quality parameter interactions monitored from several coastal stations in the South-Eastern Mediterranean Sea, namely in Lebanon. Four seasonal SOM models were developed and calibrated based on the available dataset. The SOM’s visualization of the Component Planes (CPs) enabled the examination of water quality parameter interactions and associations on a seasonal basis. Additionally, Pearson correlation analysis between the SOM’s Best Matching Units was calculated for each monitored parameter to augment the CPs analysis. SOM’s clustering of the data samples and assessment of anthropogenic activities nearby the associated monitoring stations, enabled the evaluation of the potential effects from the existing human activities in nearby areas for each monitoring station. Specifically, the seasonal models revealed that specific land uses nearby the monitoring stations like the ANT-2 (near river mouth), SEL-2 (near a chemical plant), BEY- 6 (public beach) are responsible for the coastal water quality degradation in Lebanon. Overall, this paper demonstrates that the SOM model is not only capable of revealing the parameters’ interactions, but also to identify sources of anthropogenic pollution. Therefore, the SOM models might be used as valuable tools in coastal water quality management. © 2025, The Authors. All rights reserved.

关键词： Eutrophication

RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering

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arXiv 2025年

作者： Liang, Sichu Zhang, Linhai Zhu, Hongyu Wang, Wenwen He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Department of Informatics King’s College London United Kingdom School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China School of Electrical and Computer Engineering Carnegie Mellon University United States The Alan Turing Insitute United Kingdom

Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers. However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. Through extensive evaluation across three factual-aware medical question answering benchmarks, RGAR establishes a new state-of-the-art performance among medical RAG systems. Notably, the Llama-3.1-8BInstruct model with RGAR surpasses the considerably larger, RAG-enhanced GPT-3.5. Our findings demonstrate the benefit of extracting factual knowledge for retrieval, which consistently yields improved generation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

Joint System Latency and Data Freshness Optimization for Cache-enabled Mobile Crowdsensing Networks

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arXiv 2025年

作者： Shi, Kexin Fu, Yaru Guo, Yongna Wang, Fu Lee Zhang, Yan School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Informatics University of Oslo Norway

Mobile crowdsensing (MCS) networks enable large-scale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cache-enabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond

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arXiv 2025年

作者： Evangelatos, Spyridon Veroni, Eleni Efthymiou, Vasilis Nikolopoulos, Christos Papadopoulos, Georgios Th. Sarigiannidis, Panagiotis Research & Innovation Development Department Netcompany-Intrasoft S.A. Luxembourg Department of Electronic Engineering Hellenic Mediterranean University Greece Department of Informatics and Telematics Harokopio University of Athens Greece Department of Electrical and Computer Engineering University of Western Macedonia Greece

Counterfactual explanations have emerged as a prominent method in Explainable Artificial Intelligence (XAI), providing intuitive and actionable insights into Machine Learning model decisions. In contrast to other traditional feature attribution methods that assess the importance of input variables, counterfactual explanations focus on identifying the minimal changes required to alter a model’s prediction, offering a "what-if" analysis that is close to human reasoning. In the context of XAI, counterfactuals enhance transparency, trustworthiness and fairness, offering explanations that are not just interpretable but directly applicable in the decision-making processes. In this paper, we present a novel framework that integrates perturbation theory and statistical mechanics to generate minimal counterfactual explanations in explainable AI. We employ a local Taylor expansion of a Machine Learning model’s predictive function and reformulate the counterfactual search as an energy minimization problem over a complex landscape. In sequence, we model the probability of candidate perturbations leveraging the Boltzmann distribution and use simulated annealing for iterative refinement. Our approach systematically identifies the smallest modifications required to change a model’s prediction while maintaining plausibility. Experimental results on benchmark datasets for cybersecurity in Internet of Things environments, demonstrate that our method provides actionable, interpretable counterfactuals and offers deeper insights into model sensitivity and decision boundaries in high-dimensional spaces. © 2025, CC BY.

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Tau Pet Harmonization via Surface-Based Diffusion Model

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Tau Pet Harmonization via Surface-Based Diffusion Model

IEEE International Symposium on Biomedical Imaging

作者： Jiaxin Yue Jianwei Zhang Lujia Zhong Yonggang Shi For the Alzheimer's Disease Neuroimaging Initiative, and the Health and Aging Brain Study (HABS-HD) Study Team Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The heterogeneity inherent in tau positron emission tomog-raphy (PET) imaging data across different tracers challenges the integration of multi-site tau PET data, thereby necessitating the trustful harmonization technique for a better utilization of the emerging large-scale datasets. Unlike other imaging modalities, the harmonization among multi-site tau PET data involves more than intensity mapping but contains in-tricate pattern alterations attributed to tracer binding proper-ties, which makes the existing statistical methods inadequate. Meanwhile, the effective data preprocessing is required to eliminate the artifacts caused by off-target binding and partial volume effect for meaningful comparison and harmonization. In this paper, we propose a systematic tau PET harmonization framework that involves the surface-based data preprocessing and diffusion model for generating the vertex-wise mapping between multi-site tau standardized uptake value ra-tio (SUVR) on the cortical surface. In the experiments, using large-scale Alzheimer's Disease Neuroimaging Initiative (ADNI) and Health and Aging Brain Study-Health Dispari-ties (HABS-HD) data with different tracers, we demonstrate our method can successfully achieve harmonization by gen-erating the SUVR maps with consistent pattern distributions and persevering the individual variability.

关键词： Neuroimaging Visualization Systematics Statistical analysis Data preprocessing Positrons Diffusion models Positron emission tomography Alzheimer's disease Biomedical imaging

Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery Systems

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yeongmin Lee Raihan Muhammad Syahran Mael Spangenberger Huigyu Yang Jiyong Oh Hyunseung Choo Dept. of Artificial Intelligence Dept. of Electrical and Computer Engineering Dept. of Informatics Télécom SudParis Évry France Convergence Research Institute Dept. of AI Systems Engineering Sungkyunkwan University Suwon S. Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services.

关键词： Three-dimensional displays Navigation Scalability Surveillance Handover Autonomous aerial vehicles Trajectory Resource management Vehicle dynamics Optimization

Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems

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arXiv 2025年

作者： Stamatelis, George Kanatas, Angelos-Nikolaos Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens16122 Greece School of Electrical and Computer Engineering National Technical University of Athens Zografou Campus Athens15772 Greece

Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Access (DSA). However, deploying deep learning models on resource-constrained edge devices remains challenging due to their high computational cost. To address this challenge, in this paper, we present a novel sparse recurrent MARL framework integrating gradual neural network pruning into the independent actor global critic paradigm. Additionally, we introduce a harmonic annealing sparsity scheduler, which achieves comparable, and in certain cases superior, performance to standard linear and polynomial pruning schedulers at large sparsities. Our experimental investigation demonstrates that the proposed DSA framework can discover superior policies, under diverse training conditions, outperforming conventional DSA, MADRL baselines, and state-of-the-art pruning techniques. © 2025, CC BY.

关键词： Deep reinforcement learning

Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing

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arXiv 2025年

作者： Moghadam, Mehran Shoushtari Aygun, Sercan Najafi, M. Hassan Electrical Computer and Systems Engineering Department Case Western Reserve University ClevelandOH44106 United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States

Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations. © 2025, CC BY.

关键词： Encoding (symbols)