Scholarly Works, Computer Science

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https://hdl.handle.net/10919/24290

Research articles, presentations, and other scholarship

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Linguistically Differentiating Acts and Recalls of Racial Microaggressions on Social Media
Gunturi, Uma Sushmitha; Kumar, Anisha; Ding, Xiaohan; Rho, Eugenia H. (ACM, 2024-04-01)
In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls from in-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.
Zero-Knowledge AI Inference with High Precision
Riasi, Arman; Wang, Haodi; Behnia, Rouzbeh; Vo, Viet; Hoang, Thang (ACM, 2025-11-19)
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers. In this work, we present ZIP, an efficient and precise commit and prove zero-knowledge SNARK for AIaaS inference (both linear and non-linear layers) that natively supports IEEE-754 double-precision floating-point semantics while addressing reliability and privacy challenges inherent in AIaaS. At its core, ZIP introduces a novel relative-error-driven technique that efficiently proves the correctness of complex non-linear layers in AI inference computations without any loss of precision, and hardens existing lookup-table and range proofs with novel arithmetic constraints to defend against malicious provers. We implement ZIP and evaluate it on standard datasets (e.g., MNIST, UTKFace, and SST-2). Our experimental results show, for non-linear activation functions, ZIP reduces circuit size by up to three orders of magnitude while maintaining the full precision required by modern AI workloads.
Ethical AI for Healthcare Systems: Uncertainty-Aware, Fair Federated Learning
Chen, Dian; Zhang, Qi; Kaplan, Lance; Josang, Audun; Jeong, Donghyun; Chen, Feng; Cho, Jin-Hee (ACM, 2025-06-24)
This paper proposes U-FARE, an uncertainty-aware fair federated learning (FL) framework aimed at improving disease prediction in healthcare, with a specific focus on Alzheimer’s disease detection. U-FARE incorporates evidential neural networks (ENN) to quantify uncertainty, enhancing both model fairness and accuracy. The framework ensures group-level fairness, providing consistent model performance across diverse healthcare environments despite data heterogeneity. We evaluate U-FARE on three real-world healthcare datasets—NACC, OASIS, and ADNI—comparing its performance to several state-of-the-art fairness-aware FL methods. Experimental results demonstrate that U-FARE outperforms baseline methods in both prediction accuracy and fairness, effectively balancing these two crucial aspects. The results also reveal the trade-off between fairness and accuracy, where higher fairness levels may compromise prediction accuracy. U-FARE achieves the highest accuracy (0.928) on the NACC dataset, consistently outperforms the competitive baseline q-FedAvg by 46%, particularly when higher fairness constraints are applied, and outperforms methods like Ditto and q-FFL with minimal accuracy variance and loss disparity. This is the first approach to simultaneously optimize fairness and accuracy in FL for Alzheimer’s disease detection, providing a novel solution to the challenge of fair and effective AI in healthcare. The framework demonstrates the potential to address data heterogeneity while ensuring privacy and fairness in real-world applications.
MedLeak: Multimodal Medical Data Leakage in Secure Federated Learning with Crafted Models
Shi, Shanghao; Haque, Md Shahedul; Parida, Abhijeet; Zhang, Chaoyu; Linguraru, Marius George; Hou, Y. Thomas; Anwar, Syed Muhammad; Lou, Wenjing (ACM, 2025-06-24)
Federated learning (FL) allows participants to collaboratively train machine learning models while keeping their data private, making it ideal for collaborations among healthcare institutions on sensitive datasets. However, in this paper, we demonstrate a novel privacy attack called MedLeak, which allows a malicious participant who initiates the FL task as the server to recover highquality site-specific private medical images and text records from the model updates uploaded by clients. In MedLeak, a malicious server introduces an adversarially crafted model during the FL training process. Honest clients, unaware of the insidious changes in the published model, continue to send back their updates as per the standard FL training protocol. Leveraging a novel analytical method, MedLeak can efficiently recover private client data from the aggregated parameter updates. This recovery scheme is significantly more efficient than the state-of-the-art solutions, as it avoids the costly optimization process. Additionally, the scheme relies solely on the aggregated updates, thus rendering secure aggregation protocols ineffective, as they depend on the randomization of intermediate results for security while leaving the final aggregated results unaltered. We implement MedLeak on medical image datasets MedMNIST, COVIDx CXR-4, and Kaggle Brain Tumor MRI datasets, as well as the medical text dataset MedAbstract. The results demonstrate that the proposed privacy attack is highly effective on both image and text datasets, achieving high recovery rates and strong quantitative scores. We also thoroughly evaluate MedLeak across different attack parameters, providing insights into key factors that influence attack performance and potential defenses. Furthermore, we perform downstream tasks, such as disease classification, using the recovered data, showing no significant performance degradation compared to the original training samples. Our findings validate the need for enhanced privacy measures in federated learning systems, particularly for safeguarding sensitive medical data against powerful model inversion attacks.
Game Connect: A Middleware that Flexibly Connects Games to their Health Goals
Lee, Grace; Julien, Christine (ACM, 2025-06-24)
Gamification, the application of game mechanics in a nongame context to motivate desired behavior, has proven effective in promoting health-related changes. Personalized game elements, such as tailored goals, rewards, and storylines, have been shown to be more effective than one-size-fits-all games. However, existing gamified health interventions are tailored towards a specific outcome, with a fixed combination of sensors tied to a specific storyline, which restricts the ability to change game mechanics without extensive modifications. Likewise, this approach also limits the flexibility to reuse the same game for multiple interventions. We present Game Connect, a middleware that decouples health sensors from game logic, enabling flexible combinations of sensor data sources with multiple game stories. Game Connect provides user interfaces for health domain experts to: (1) identify health data derived from various sensors; (2) define health goals to be met by a user; and (3) define how goals translate into one or more meaningful game construct. Uniquely, Game Connect employs a centralized “point pool” system, allowing different games to consume from the same pool of points earned through achieving real-life goals. We demonstrate the feasibility of Game Connect through an indepth study of 10 individuals building games that rely on the middleware. Our evaluation indicates that Game Connect simplifies the process of integrating health data with existing games, and that its decoupling of health data, game logic, and intervention objectives allows for greater flexibility and code reusability across diverse applications.
Decoding DNSSEC Errors at Scale: An Automated DNSSEC Error Resolution Framework using Insights from DNSViz Logs
Ashiq, Md. Ishtiaq; Hureau, Olivier; Deccio, Casey; Chung, Taejoong (ACM, 2025-10-28)
Low adoption and high misconfiguration rates continue to blunt the security benefits of DNSSEC. Drawing on 1.1M historical diagnostic snapshots covering 319K second-level and their subdomains between 2020 and 2024 from the DNSViz service, this paper delivers the first longitudinal, data-driven taxonomy of real-world DNSSEC failures. The study shows that NSEC3 misconfigurations, delegation failures and missing/expired signatures account for more than 70% of all bogus states, and that 18% of such domains remain broken. Guided by these insights, we introduce DFixer: an offline tool that (i) groups cascaded error codes into root causes, and (ii) autogenerates high-level instructions and corresponding concrete BIND command sequences to repair them. Evaluation with a purposebuilt ZReplicator testbed demonstrates that DFixer remedies 99.99% of observed errors in seconds. The curated error-to-command mapping is openly released to foster wider, more reliable DNSSEC deployment.
Few-Shot Knowledge Graph Completion via Transfer Knowledge from Similar Tasks
Liu, Lihui; Wang, Zihao; Zhou, Dawei; Wang, Ruijie; Yan, Yuchen; Xiong, Bo; He, Sihong; Tong, Hanghang (ACM, 2025-11-10)
Knowledge graphs (KGs) are widely used in many AI applications, but they are often incomplete, which limits their effectiveness. In many cases, most relations have very few examples, making it difficult to learn accurate models. Few-shot learning has emerged as a promising solution by enabling KG completion with only a small number of training triplets. However, many existing methods treat each relation separately and miss the opportunity to share useful information across tasks. In this paper, we propose TransNet, a transfer learning approach for few-shot KG completion that captures relationships between tasks and reuses knowledge from related ones. TransNet also uses meta learning to better handle unseen relations. Experiments on standard benchmarks show that TransNet achieves strong performance compared to prior methods. Code and data will be released upon acceptance.
HuggingGraph: Understanding the Supply Chain of LLM Ecosystem
Rahman, Mohammad Shahedur; Gao, Peng; Ji, Yuede (ACM, 2025-11-10)
Large language models (LLMs) leverage deep learning architectures to process and predict sequences of words, enabling them to perform a wide range of natural language processing tasks, such as translation, summarization, question answering, and content generation. As existing LLMs are often built from base models or other pre-trained models and use external datasets, they can inevitably inherit vulnerabilities, biases, or malicious components that exist in previous models or datasets. Therefore, it is critical to understand these components’ origin and development process to detect potential risks, improve model fairness, and ensure compliance with regulatory frameworks. Motivated by that, this project aims to study such relationships between models and datasets, which are the central parts of the LLM supply chain. First, we design a methodology to systematically collect LLMs’ supply chain information. Then, we design a new graph to model the relationships between models and datasets, which is a directed heterogeneous graph, having 402,654 nodes and 462,524 edges. Lastly, we perform different types of analysis and make multiple interesting findings.
VideoAVE: A Multi-Attribute Video-to-Text Attribute Value Extraction Dataset and Benchmark Models
Cheng, Ming; Wu, Tong; Hu, Jiazhen; Gong, Jiaying; Eldardiry, Hoda (ACM, 2025-11-10)
Attribute Value Extraction (AVE) is important for structuring product information in e-commerce. However, existing AVE datasets are primarily limited to text-to-text or image-to-text settings, lacking support for product videos, diverse attribute coverage, and public availability. To address these gaps, we introduce VideoAVE, the first publicly available video-to-text e-commerce AVE dataset across 14 different domains and covering 172 unique attributes. To ensure data quality, we propose a post-hoc CLIP-based Mixture of Experts filtering system (CLIP-MoE) to remove the mismatched video-product pairs, resulting in a refined dataset of 224k training data and 25k evaluation data. In order to evaluate the usability of the dataset, we further establish a comprehensive benchmark by evaluating several state-of-the-art video vision language models (VLMs) under both attribute-conditioned value prediction and open attribute-value pair extraction tasks. Our results analysis reveals that video-to-text AVE remains a challenging problem, particularly in open settings, and there is still room for developing more advanced VLMs capable of leveraging effective temporal information. The dataset and benchmark code for VideoAVE are available at: https://github.com/gjiaying/VideoAVE.
PlateMate: Structured Guidance Design in Relatedness Technology for Remote Joint Activity
Wang, Wei-Lu; Yan, Zeren; Zheng, Xian; Zhang, Wuyou; Cao, Yufei; McCrickard, D. Scott (ACM, 2025-11-29)
Maintaining remote intimate relationships is essential for emotional well-being. However, remote partners often face challenges such as schedule conflicts or unequal kinkeeping responsibility when engaging in joint activities. Current relatedness technology designs require high synchronization or place the burden of initiation on one party, limiting long-term engagement. In this study, we explore how structured guidance can address these challenges. We present PlateMate, a game that combines joint cooking activities with prompts from a shared virtual pet, Kinny, to sustain balanced participation and a sense of togetherness. We conducted a twoweek user study with 14 participants to examine the effectiveness of the mechanism across various aspects. Our early findings highlight design opportunities for relatedness technologies that support remote joint activities to improve engagement through structured guidance, turn everyday events into shared rituals, spark ongoing dialogue beyond the activity, and enrich prompt design to sustain long-term use.
A Modern Edge-based Design for Cellular Roaming
Ukyab, Tenzin; Hasan, Shaddi; Ratnasamy, Sylvia; Shenker, Scott (ACM, 2025-11-17)
Support for roaming in today’s cellular architecture involves operating a private network that sits right next to the mainstream Internet. This network is called the IP Exchange Network (IPX) and it provides Mobile Network Operators (MNOs) with a mechanism to form roaming partnerships, resolve billing and QoS, and set up tunnels as necessary. We propose a design which we call the CPX (Consolidated IPX/IXP) where the roaming network converges with the Internet by leveraging existing edge providers to provide all the benefits that the IPX network provides. In addition to convergence, the CPX architecture provides lower latency for roaming users; in our simulation of a global CPX deployment, we demonstrate an average of 318% improvement in roaming connection latency.
Anyone, Anywhere, not Everyone, Everywhere: Starlink Doesn't End the Digital Divide
Woo, Wesley; Fraire, Juan A.; Ratnasamy, Sylvia; Shenker, Scott; Hasan, Shaddi (ACM, 2025-11-17)
Low Earth Orbit (LEO) satellite constellations, such as Starlink, are increasingly promoted as a solution to the digital divide in rural and underserved communities. In this paper, we take a closer look at the limits of this approach. Using the insight that capacity limitations of LEO-based access networks are driven by peak demand density, we introduce a simple analytical model that brings together real-world demand data with the physical and regulatory limits of LEO satellite networks. Applying our model to broadband demand across the United States,we find that serving the current Starlink constellation size is likely insufficient for covering all un- and underserved locations in the US and we find diminishing returns that disincentivize scaling the constellation to serve the long-tail of these un(der)served locations. We also identify that Starlink’s current pricing is likely unaffordable for the majority of these locations, even with existing government subsidies. We argue that LEO constellations, while technologically impressive, are just another piece of the solution, rather than a panacea. New, innovative approaches are still required to end the digital divide.
Evaluating AI models for Autograding Explain in Plain English Questions: Challenges and Considerations
Fowler, Max; Emeka, Chinedu; Chen, Binglin; Smith, David H. IV; West, Matthew; Zilles, Craig (ACM, 2025-12)
Code reading ability has traditionally been under-emphasized in assessments as it is difficult to assess at scale. Prior research has shown that code reading and code writing are intimately related skills; thus being able to assess and train code reading skills may be necessary for student learning. One way to assess code reading ability is using Explain in Plain English (EiPE) questions, which ask students to describe what a piece of code does with natural language. Previous research deployed a binary (correct/incorrect) autograder using bigram models that performed comparably with human teaching assistants on student responses. With a data set of 3,064 student responses from 17 EiPE questions, we investigated multiple autograders for EiPE questions. We evaluated methods as simple as logistic regression trained on bigram features, to more complicated support vector machines (SVMs) trained on embeddings from large language models (LLMs), to GPT-4. We found multiple useful autograders, most with accuracies in the 86-88% range, with different advantages. SVMs trained on LLM embeddings had the highest accuracy; few-shot chat completion with GPT-4 required minimal human effort; pipelines with multiple autograders for specific dimensions (what we call 3D autograders) can provide fine-grained feedback; and code generation with GPT-4 to leverage automatic code testing as a grading mechanism in exchange for slightly more lenient grading standards. While piloting these autograders in a non-major introductory Python course, students had largely similar views of all autograders, although they more often found the GPT-based grader and code generation graders more helpful and liked the code generation grader the most.
Harnessing Page Access Frequency Distribution for Efficient Memory Tiering
Lee, Taehyung; Monga, Sumit; Eom, Young Ik; Min, Changwoo (ACM, 2025)
Advances in memory technologies (e.g., HBM, DRAM, NVM) and interconnects (e.g., CXL) have significantly enhanced the flexibility of utilizing memory resources in modern computer systems. As this trend continues, memory resources are poised to become fully composable in the near future. This increasing flexibility also accelerates the demand for effective tiered memory systems capable of performing well across diverse scenarios and workloads. We present Memtis, a tiered memory system that adopts informed decision-making for page placement and page size determination. Memtis leverages access distribution of allocated pages to optimally approximate the hot data set to the fast tier capacity. Moreover, Memtis dynamically determines the page size that allows applications to use huge pages while avoiding their drawbacks by detecting inefficient use of fast tier memory and splintering them if necessary. To further enhance its practicality across diverse scenarios, Memtis effectively supports the dynamic allocation of fast tier memory if there is a specific performance requirement, such as a target hit ratio. Our evaluation shows that Memtis outperforms existing tiered memory systems under various workloads and tiering configurations by up to 169.0%, showing its robustness.
Tarazu: An Adaptive End-to-end I/O Load-balancing Framework for Large-scale Parallel File Systems
Paul, Arnab K.; Neuwirth, Sarah; Wadhwa, Bharti; Wang, Feiyi; Oral, Sarp; Butt, Ali R. (ACM, 2024-05-01)
The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art.
Building datasets to support information extraction and structure parsing from electronic theses and dissertations
Ingram, William A.; Wu, Jian; Kahu, Sampanna Yashwant; Manzoor, Javaid Akbar; Banerjee, Bipasha; Ahuja, Aman; Choudhury, Muntabir Hasan; Salsabil, Lamia; Shields, Winston; Fox, Edward A. (Springer, 2024-06-01)
Despite the millions of electronic theses and dissertations (ETDs) publicly available online, digital library services for ETDs have not evolved past simple search and browse at the metadata level. We need better digital library services that allow users to discover and explore the content buried in these long documents. Recent advances in machine learning have shown promising results for decomposing documents into their constituent parts, but these models and techniques require data for training and evaluation. In this article, we present high-quality datasets to train, evaluate, and compare machine learning methods in tasks that are specifically suited to identify and extract key elements of ETD documents. We explain how we construct the datasets by manual labeling the data or by deriving labeled data through synthetic processes. We demonstrate how our datasets can be used to develop downstream applications and to evaluate, retrain, or fine-tune pre-trained machine learning models. We describe our ongoing work to compile benchmark datasets and exploit machine learning techniques to build intelligent digital libraries for ETDs.
Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains
Tolokh, Igor S.; Folescu, Dan E.; Onufriev, Alexey V. (American Chemical Society, 2024-06-11)
The current practical "workhorses" of the atomistic implicit solvation-the Poisson-Boltzmann (PB) and generalized Born (GB) models-face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE similar to 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.
A deep dive into enhancing sharing of naturalistic driving data through face deidentification
Thapa, Surendrabikram; Sarkar, Abhijit (Springer, 2025-03-01)
Human factors research in transportation relies on naturalistic driving studies (NDS) which collect real-world data from drivers on actual roads. NDS data offer valuable insights into driving behavior, styles, habits, and safety-critical events. However, these data often contain personally identifiable information (PII), such as driver face videos, which cannot be publicly shared due to privacy concerns. To address this, our paper introduces a comprehensive framework for deidentifying drivers' face videos, that can facilitate the wide sharing of driver face videos while protecting PII. Leveraging recent advancements in generative adversarial networks (GANs), we explore the efficacy of different face swapping algorithms in preserving essential human factors attributes while anonymizing participants' identities. Most face swapping algorithms are tested in restricted lighting conditions and indoor settings, there is no known study that tested them in adverse and natural situations. We conducted extensive experiments using large-scale outdoor NDS data, evaluating the quantification of errors associated with head, mouth, and eye movements, along with other attributes important for human factors research. Additionally, we performed qualitative assessments of these methods through human evaluators providing valuable insights into the quality and fidelity of the deidentified videos. We propose the utilization of synthetic faces as substitutes for real faces to enhance generalization. Additionally, we created practical guidelines for video deidentification, emphasizing error threshold creation, spot-checking for abrupt metric changes, and mitigation strategies for reidentification risks. Our findings underscore nuanced challenges in balancing data utility and privacy, offering valuable insights into enhancing face video deidentification techniques in NDS scenarios.
Examining Age-Bias and Stereotypes of Aging in LLMs
Dewan, Sherwin; Shaikh, Ismail; Shaw, Connie; Sahoo, Abhilash; Jha, Akshita; Pradhan, Alisha (ACM, 2025-10-26)
Large Language Models (LLMs) are increasingly being used across applications, ranging from content generation to decision-making, raising concerns about biases embedded in them. While biases related to gender, race, and culture have been studied extensively, understanding age-bias and stereotypes of aging in LLMs remain underexplored. This study analyzes LLM-generated responses to prompts related to aging, revealing stereotypical biases about aging pertaining to technology proficiency, cognitive and physical decline, and job roles.We noted that even responses without explicit age bias also had mentions of ageist stereotypes. We discuss considerations for involving older adults’ perspectives through human-in-the-loop methodologies yet exercising caution about aspects like internalized ageism.
Interpretive Caption: Real-Time Vocal Emotion Cues for DHH Users
Ubur, Sunday; Adewale, Sikiru; Chandrashekar, Nikitha; Akli, Enoch; Gracanin, Denis (ACM, 2025-10-26)
Deaf and Hard-of-Hearing (DHH) individuals increasingly rely on real-time captioning to access spoken content in educational and professional settings. However, traditional captions omit vocal emotional cues, such as intonation and affect which can hinder comprehension and engagement. This work introduces Interpretive Caption, a machine-learning prototype that augments captions with emotion-aware annotations derived from vocal tone. Using letter-coded tags with hover-based tooltips, the system conveys emotional context on demand, balancing clarity with cognitive accessibility. We conducted a qualitative study with eight DHH participants who interacted with the prototype and shared feedback on usability, emotional clarity, and layout design. Findings highlight the value of hover-based emotional cues, customization features, and segmentation aligned with cognitive load principles. Participants appreciated the non-intrusive emotional insights, while also identifying areas for improvement, including accent-inclusive emotion recognition and better mobile accessibility. Our contributions include a real-time captioning prototype integrating speech emotion recognition, a user-controllable emotion display interface, and design insights for affective accessibility in educational contexts. This work offers a foundation for inclusive, expressive captioning and informs future multimodal caption systems that prioritize interpretability, cultural sensitivity, and user agency.

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