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SUBER: An RL Environment with Simulated Human Behavior for Recommender Systems
(2024)Conference Paper -
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A Formal Treatment of End-to-End Encrypted Cloud Storage
(2024)Lecture Notes in Computer ScienceUsers increasingly store their data in the cloud, thereby benefiting from easy access, sharing, and redundancy. To additionally guarantee security of the outsourced data even against a server compromise, some service providers have started to offer end-to-end encrypted (E2EE) cloud storage. With this cryptographic protection, only legitimate owners can read or modify the data. However, recent attacks on the largest E2EE providers have ...Conference Paper -
Towards Learning Abductive Reasoning using VSA Distributed Representations
(2024)Conference Paper -
Cascade: CPU Fuzzing via Intricate Program Generation
(2024)Generating interesting test cases for CPU fuzzing is akin to generating programs that exercise unusual states inside the CPU. The performance of CPU fuzzing is heavily influenced by the quality of these programs and by the overhead of bug detection. Our analysis of existing state-of-the-art CPU fuzzers shows that they generate programs that are either overly simple or execute a small fraction of their instructions due to invalid control ...Conference Paper -
PUZZLES: A Benchmark for Neural Algorithmic Reasoning
(2024)Algorithmic reasoning is a fundamental cognitive ability that plays a pivotal role in problem-solving and decision-making processes. Reinforcement Learning (RL) has demonstrated remarkable proficiency in tasks such as motor control, handling perceptual input, and managing stochastic environments. These advancements have been enabled in part by the availability of benchmarks. In this work we introduce PUZZLES, a benchmark based on Simon ...Conference Paper -
Multimodal Dialog Act Classification for Digital Character Conversations
(2024)CUI '24: Proceedings of the 6th ACM Conference on Conversational User InterfacesDialog act classification is essential for enabling digital characters to understand and respond effectively to user intents, leading to more engaging and seamless interactions. Previous research has focused on classifying dialog acts from transcriptions alone due to missing multimodal data. We close this gap by collecting a new multimodal (i.e., text, audio, video) dyadic dialog dataset from 60 participants. Based on our dataset, we ...Conference Paper