https://scholarworks.unist.ac.kr/handle/201301/26 2026-05-13T07:33:59Z 2026-05-13T07:33:59Z Chowdhury, Md Sameer Iqbal Au, Tsz-Chiu https://scholarworks.unist.ac.kr/handle/201301/91611 2026-04-30T05:30:07Z 2025-12-31T15:00:00Z

Title: Assessing Vision-Language Models for Failure Detection in Robotic Manipulation Author(s): Chowdhury, Md Sameer Iqbal; Au, Tsz-Chiu Abstract: Vision-language models (VLMs) offer transformative potential for robotics, but their deployment is constrained by performance limitations. In safety-critical manipulation, a model must recognize its own limitations to prevent a catastrophic failure. We conduct a systematic study of VLMs for robotic failure detection, evaluating six architectures on real-world trajectories. We put forward a decision-making process that allows a VLM to evaluate whether it can successfully complete a task, and if not, pause its operation and hand over the task to human operators. Our results show that well-calibrated VLMs can be trustworthy partners that know exactly when to ask for help.

2025-12-31T15:00:00Z Kim, Joohee Lee, Hyunwook Nguyen, Duc M. Shin, Minjeong Kwon, Bum Chul Ko, Sungahn Elmgyist, Niklas https://scholarworks.unist.ac.kr/handle/201301/91457 2026-04-22T11:00:09Z 2024-12-31T15:00:00Z

Title: DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs Author(s): Kim, Joohee; Lee, Hyunwook; Nguyen, Duc M.; Shin, Minjeong; Kwon, Bum Chul; Ko, Sungahn; Elmgyist, Niklas Abstract: Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs-graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. In this paper, we propose DG COMICS, a novel comic authoring tool for dynamic graphs that allows users to semi-automatically build and annotate comics. The tool uses a newly developed hierarchical clustering algorithm to segment consecutive snapshots of dynamic graphs while preserving their chronological order. It also presents rich information on both individuals and communities extracted from dynamic graphs in multiple views, where users can explore dynamic graphs and choose what to tell in comics. For evaluation, we provide an example and report the results of a user study and an expert review.

2024-12-31T15:00:00Z Lee, Bongwon Jeong, Yunseo Shin, Woongsub Ha, Sangtae Im, Youngbin https://scholarworks.unist.ac.kr/handle/201301/91346 2026-05-08T06:30:32Z 2026-03-31T15:00:00Z

Title: LatScope: End-to-End Latency Decomposition Across the Cloud Network Stack Author(s): Lee, Bongwon; Jeong, Yunseo; Shin, Woongsub; Ha, Sangtae; Im, Youngbin Abstract: Data centers are rapidly scaling and becoming more complex, making it critical to pinpoint where latency arises across the network protocol stack. Existing tools primarily measure RTT or single-layer delays, while multi-layer approaches are often invasive or limited. We present LatScope, a practical multi-layer latency analyzer that (i) matches packets across layers to compute accurate inter-layer delays despite retransmissions and out-of-order delivery, (ii) synchronizes server clocks using XDP to enable precise inter-server delay breakdowns, and (iii) controls overhead through selective data extraction. We validate LatScope across diverse environments and show how its insights can be applied effectively in cloud deployments.

2026-03-31T15:00:00Z Hong, Jaemin Ryu, Sukyoung https://scholarworks.unist.ac.kr/handle/201301/91290 2026-04-07T03:32:30Z 2024-09-30T15:00:00Z

Title: Type-migrating C-to-Rust translation using a large language model Author(s): Hong, Jaemin; Ryu, Sukyoung Abstract: Rust, a modern system programming language, introduces new types that prevent memory bugs and data races. This makes translating legacy system programs from C to Rust a promising approach to enhance their reliability. Since manual code translation is time-consuming, it is desirable to automate the translation. To yield satisfactory results, the translator should have the ability to perform type migration, i.e., removing C types and introducing Rust types in the code. In this work, we aim to automatically port an entire C program to Rust by translating each C function to a Rust function with a signature containing proper Rust types through type migration. This goal is challenging because (1) type migration cannot be achieved through syntactic mappings between type names, and (2) after type migration, function bodies should be correctly restructured based on the precise understanding of the functions' behavior. To address these difficulties, we leverage large language models (LLMs), which possess knowledge of program semantics and programming idioms. However, na & iuml;vely instructing LLMs to translate each function produces unsatisfactory Rust code, containing unmigrated or improperly migrated types and a huge number of type errors. To resolve these issues, we propose three techniques: (1) generating candidate signatures, (2) providing translated callees' signatures to LLMs, and (3) iteratively fixing type errors using compiler feedback. Our evaluation shows that the proposed approach yields a 63.5% increase in migrated types and a 71.5% decrease in type errors compared to the baseline (the na & iuml;ve LLM-based translation) with modest performance overhead.

2024-09-30T15:00:00Z