Result reproduction of computer networking research is challenging as the scarcity of open-source implementations and the complexity of heterogeneous system architectures. Even though Large Language Models have demonstrated potential in code generation, existing code generation frameworks often fail to address the long-context constraints and intricate logical dependencies, which are vital in reproducing network systems from academic papers. Thus, we introduce RepLLM, an end-to-end multi-agent framework designed to automate code reproduction from paper content. RepLLM features a collaborative architecture comprising four specialized agents—Content Parsing, Architecture Design, Code Generation, and Audit & Repair, which are coordinated through Shared Memory mechanism to ensure global context consistency. With the enhancement of Structured Chain-of-Thought LLM reasoning and a sandbox-isolated static-dynamic debugging methodology, our framework effectively resolves semantic discrepancies and runtime errors, thereby improving reliable reproductions. Extensive evaluations on representative papers in top conferences demonstrate that RepLLM outperforms state-of-the-art system-level LLM frameworks in generating compile-ready and logically correct systems. Our results show that, with the aid of RepLLM, we can reproduce 95% of the original benchmarks within approximately two hours while reducing token consumption by up to 10% compared with state-of-the-art baselines.

Teleportation is widely used in Virtual Reality (VR) applications, especially for enabling efficient navigation in large-scale virtual environments. Leveraging foot movements for teleportation frees the hands for parallel tasks and helps maintain spatial orientation, making it particularly suitable for seated and multitasking VR scenarios. This work explores feasible foot actions for teleportation in confined seating conditions, such as traveling on an airplane or train, where users' leg motion is feasible, but its range is constrained. Such scenarios are common but are underexplored. We propose seven metaphor-inspired teleportation techniques based on foot interactions and evaluate their effectiveness through a user study. The findings provide valuable insights for designing effective teleportation techniques in VR scenarios where users are seated in constrained spaces.

With the exponential growth of digital information, ensuring text security, a fundamental component of information security, becomes increasingly paramount. While authentication remains a primary focus for data access control and protection, the rich sensor ecosystem and immersive experiences of virtual reality (VR) environments introduce new privacy risks, particularly with inconspicuous sensors like motion and location sensors. In this context, protecting the security of text entered by users poses a unique challenge. This paper explores the feasibility of enhancing text security by introducing variability in virtual input tools during typing processes. Specifically, we investigate the impact of introducing successive and random intermittent variations to the virtual ray (start point and direction) with controller-based raycasting techniques on text security and typing experience. The results demonstrate that introducing variability in virtual ray effectively protects regular text and passwords. Random intermittent introducing variability balances security and user experience for regular text. These findings provide insights into enhancing text security beyond authentication and defending against the potential risks in VR environments.

SimpleConvolution is the most important and time-consuming part of convolutional neural networks (CNN) for image processing. Each slide of the window in two-dimensional convolution will cause repeated memory access. To solve above problems, we propose an algorithm integrated with the hardware level, parallel in kernel Convolution(PIK-Convolution), which reduces the granularity of a SingleConvolution and avoid the problem of storage access pressure caused by spatial locality. Unlike Image to column(im2col) algorithm, we avoid the problem of non-adjacent data in memory directly from hardware perspective. With MGPUSim, a multi-GPU architecture platform, we implement our algorithm. A large number of experimental results show that the proposed algorithm performs well on the new architecture. Compared with the unoptimized algorithm, the overall efficiency of the new algorithm is optimized by 2.2×-7.45×, and the memory access efficiency is an average of 30× of the unoptimized algorithm. Compared to the state-of-the-art convolution acceleration, our algorithm has a performance optimization of 1.5×-2.2.

Authentication in digital security relies heavily on text-based passwords, even with other available methods like biometrics and graphical passwords. While virtual reality (VR) keyboards are typically invisible to onlookers, the presence of inconspicuous sensors, including accelerometers, gyroscopes, and barometers, poses a potential risk of unauthorized observation and recording. Traditional defense shoulder-surfing attack methods typically involve breaking apart the Qwerty layout, which destroys the user's inherent familiarity with the layout. This research addresses the need for secure password entry in VR environments while retaining the Qwerty layout. We explore three keyboard-related position alteration strategies to ensure security while mitigating the decline in user experience. These strategies involve moving the entire keyboard, cursor, and keys. Our theoretical study assesses the effectiveness of these strategies against shoulder-surfing attacks. Two user studies, employing ray-based and position-based text entry methods, respectively, evaluate the practical effectiveness of the three strategies in resisting shoulder-surfing attacks, as well as their impact on typing performance and user experience. Our findings demonstrate that the three strategies achieve shoulder-surfing attack resistance comparable to a random layout keyboard. Moreover, compared to a random layout, the two strategies involving the movement of the entire keyboard and the repositioning of keys support faster entry rates and enhanced user experience.