Person re-identification (Re-ID) aims to match person images across different camera views, with occluded Re-ID addressing scenarios where pedestrians are partially visible. While pretrained vision-language models have shown effectiveness in Re-ID tasks, they face significant challenges in occluded scenarios by focusing on holistic image semantics while neglecting fine-grained attribute information. This limitation becomes particularly evident when dealing with partially occluded pedestrians or when distinguishing between individuals with subtle appearance differences. To address this limitation, we propose Attribute-Guide ReID (AG-ReID), a novel framework that leverages pre-trained models’ inherent capabilities to extract fine-grained semantic attributes without additional data or annotations. Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism that combines holistic and fine-grained attribute information to enhance image feature extraction. Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets, showing significant improvements in handling occlusions and subtle attribute differences while maintaining competitive performance on standard Re-ID scenarios.

Reflections on the water surface hinder the extraction of valuable information from water surface images. To remove reflections from water surface images, we construct a synthetic dataset and propose a multi-task network for water surface reflection detection and removal. Specifically, we first use a U-Net-based reflection detection module to generate a reflection mask, followed by a GAN-based network to remove the reflection. To extract multi-level features from the images, we design a color feature extraction network and a detail feature extraction network. Finally, to enhance the model's ability to remove large-area reflections, we pre-train the reflection removal network on an image restoration dataset. Experimental results on the proposed synthetic dataset and real water surface reflection images from the Internet show that our method significantly outperforms other methods in water surface reflection detection and removal.

Modern industrial datacenter networks carry latency-sensitive and throughput-oriented applications with diverse requirements. Recent load balancing mechanisms effectively reduce latency and improve throughput for heterogeneous traffic. However, deadline-sensitive flows still often miss deadlines due to being blocked. In this article, we introduce an adaptive routing system (ARS) to avoid missing deadlines. Specifically, an ARS computes a heuristic function using three influence factors, derives the probability of choosing the next node, and finds optimal (re)routing path. The experimental results show that an ARS enhances the throughput for long flows and decreases the average flow completion time and the deadline miss rate by 24% and 55%, respectively, compared to state-of-the-art load balancing schemes.

In modern datacenter networks (DCNs), the booming online data-intensive applications generate mix-flows with or without deadlines. Balancing these heterogenous flows among parallel equal-cost paths to meet the tight deadlines is crucial. However, due to the unaware of deadlines, the existing load balancing mechanisms cannot choose suitable (re)routing path for mix-flows to meet their respective stringent requirements. In this paper, we propose a deadline-aware rerouting scheme called DAR, which applies different routing strategies for mix-flows. Specifically, DAR first perceives the deadline flows and then categorizes them based on the urgency of the deadline, and employs different (re)routing strategies to ensure that flows with more urgent deadlines are completed earlier. The NS-3 simulation results show that DAR effectively balances mix-flows. For example, compared to the state-of-the-art load balancing schemes, DAR reduces the deadline miss rate and the average flow completion time (AFCT) by up to 38% and 35.5%, respectively.

Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn.

This paper introduces Crescendo, a self-paced programming practice environment that combines the block-based and visual, interactive programming of Snap!, with the structured practices commonly found in Drill-and-Practice Environments. Crescendo supports students with Parsons problems to reduce problem complexity, Use-Modify-Create task progressions to gradually introduce new programming concepts, and automated feedback and assessment to support learning. In this work, we report on our experience deploying Crescendo in a programming camp for middle school students, as well as in an introductory university course for non-majors. Our initial results from field observations and log data suggest that the support features in Crescendo kept students engaged and allowed them to progress through programming concepts quickly. However, some students still struggled even with these highly-structured problems, requiring additional assistance, suggesting that even strong scaffolding may be insufficient to allow students to progress independently through the tasks.

Parsons problems are program puzzles, where students piece together code fragments to construct a program. Similar to block-based programming environments, Parsons problems eliminate the need to learn syntax. Parsons problems have been shown to improve learning efficiency when compared to writing code or fixing incorrect code in lab studies, or as part of a larger curriculum. In this study, we directly compared Parsons problems with block-based programming assignments in classroom settings. We hypothesized that Parsons problems would improve students' programming efficiency on the lab assignments where they were used, without impacting performance on the subsequent, related homework or the later programming project. Our results confirmed our hypothesis, showing that on average Parsons problems took students about half as much time to complete compared to equivalent programming problems. At the same time, we found no evidence to suggest that students performed worse on subsequent assignments, as measured by performance and time on task. The results indicate that the effectiveness of Parsons problems is not simply based on helping students avoid syntax errors. We believe this is because Parsons problems dramatically reduce the programming solution space, letting students focus on solving the problem rather than having to solve the combined problem of devising a solution, searching for needed components, and composing them together.

Research in a variety of domains has shown that viewing worked examples (WEs) can be a more efficient way to learn than solving equivalent problems. We designed a Peer Code Helper system to display WEs, along with scaffolded self-explanation prompts, in a block-based, novice programming environment called \snap. We evaluated our system during a high school summer camp with 22 students. Participants completed three programming problems with access to WEs on either the first or second problem. We found that WEs did not significantly impact students' learning, but may have impacted students' intrinsic cognitive load, suggesting that our WEs with scaffolded prompts may be an inherently different learning task. Our results show that WEs saved students time on initial tasks compared to writing code, but some of the time saved was lost in subsequent programming tasks. Overall, students with WEs completed more tasks within a fixed time period, but not significantly more. WEs may improve students' learning efficiency when programming, but these effects are nuanced and merit further study.

Empirical studies show that worked examples (WEs) are effective in improving students' learning efficiency in a variety of domains. I aim to create and evaluate data-driven intelligent WEs for novices using the Snap! block-based programming environment. First, I will design and evaluate WEs with self-explanation prompts in Snap!. Then I will develop a data-driven method to generate WEs based on student solutions and compare it with manually-curated WEs.

Instructional supports (Supports) help students learn more effectively in intelligent tutoring systems and gamified educational environments. However, the implementation and success of Supports vary by environment. We explored Support design in an educational programming game, BOTS, implementing three different strategies: instructional text (Text), worked examples (Examples) and buggy code (Bugs). These strategies are adapted from promising Supports in other domains and motivated by established educational theory. We evaluated our Supports through a pilot study with middle school students. Our results suggest Bugs may be a promising strategy, as demonstrated by the lower completion time and solution code length in assessment puzzles. We end reflecting on our design decisions providing recommendations for future iterations. Our motivations, design process, and study's results provide insight into the design of Supports for programming games.