More Like this

1. Long-Context Language Modeling with Parallel Context Encoding

   https://doi.org/10.18653/v1/2024.acl-long.142  

   Yen, Howard; Gao, Tianyu; Chen, Danqi (August 2024, Association for Computational Linguistics)
2. Context-AwareAttentiveKnowledgeTracing

   Ghosh, Aritra; Heffernan, Neil; Lan, Andrew S (August 2020, ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
3. Putting a Context in Context: Investigating the Context of Pencil Puzzles in Multiple Academic Environments

   https://doi.org/10.1145/3545945.3569848  

   Butler, Zack; Bezáková, Ivona; Brilliantova, Angelina (March 2023, ACM)
4. μSwitch: Fast Kernel Context Isolation with Implicit Context Switches

   https://doi.org/10.1109/SP46215.2023.10179284  

   Peng, Dinglan; Liu, Congyu; Palit, Tapti; Fonseca, Pedro; Vahldiek-Oberwagner, Anjo; Vij, Mona (May 2023, 2023 IEEE Symposium on Security and Privacy (SP))

   Isolating application components is crucial to limit the exposure of sensitive data and code to vulnerabilities in the untrusted components. Process-based isolation is the de facto isolation used in practice, e.g., web browsers. However, it incurs significant performance overhead and is typically infeasible when frequent switches between isolation domains are expected. To address this problem, many intra-process memory isolation techniques have been proposed using novel kernel abstractions, recent CPU extensions (e.g., Intel ® MPK), and software-based fault isolation (e.g., WebAssembly). However, these techniques insufficiently isolate kernel resources, such as file descriptors, or do so by incurring high overheads when resources are accessed. Other work virtualizes the kernel context inside a privileged user space domain, but this is ad-hoc, error-prone, and provides only limited kernel functionalities. We propose μSwitch, an efficient kernel context isolation mechanism with memory protection that addresses these limitations. We use a protected structure, shared by the kernel and the user space, for context switching and propose implicit context switching to improve its performance by deferring the kernel resource switch to the next system call. We apply μSWITCH to isolate libraries in the Firefox web browser and an HTTP server, and reduce the overhead of isolation by 32.7% to 98.4% compared with other isolation techniques. 

   more » « less
5. Detecting Out-Of-Context Objects Using Graph Context Reasoning Network

   Acharya, M; Roy, A; Koneripalli, K; Jha, S; Kanan, C; Divakaran, A (July 2022, IJCAI)

   This paper presents an approach to detect out-of-context (OOC) objects in an image. Given an image with a set of objects, our goal is to determine if an object is inconsistent with the scene context and detect the OOC object with a bounding box. In this work, we consider commonly explored contextual relations such as co-occurrence relations, the relative size of an object with respect to other objects, and the position of the object in the scene. We posit that contextual cues are useful to determine object labels for in-context objects and inconsistent context cues are detrimental to determining object labels for out-of-context objects. To realize this hypothesis, we propose a graph contextual reasoning network (GCRN) to detect OOC objects. GCRN consists of two separate graphs to predict object labels based on the contextual cues in the image: 1) a representation graph to learn object features based on the neighboring objects and 2) a context graph to explicitly capture contextual cues from the neighboring objects. GCRN explicitly captures the contextual cues to improve the detection of in-context objects and identify objects that violate contextual relations. In order to evaluate our approach, we create a large-scale dataset by adding OOC object instances to the COCO images. We also evaluate on recent OCD benchmark. Our results show that GCRN outperforms competitive baselines in detecting OOC objects and correctly detecting in-context objects. 

   more » « less