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  1. Abstract

    Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose aunified, end-to-end, programmable graph representation learning(PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision.

     
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  2. Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using lp norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the R2ET algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy. 
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    Free, publicly-accessible full text available July 28, 2024
  3. Online reviews provide product evaluations for customers to makedecisions. Unfortunately, the evaluations can be manipulated us-ing fake reviews (“spams”) by professional spammers, who havelearned increasingly insidious and powerful spamming strategiesby adapting to the deployed detectors. Spamming strategies arehard to capture, as they can be varying quickly along time, differentacross spammers and target products, and more critically, remainedunknown in most cases. Furthermore, most existing detectors focuson detection accuracy, which is not well-aligned with the goal ofmaintaining the trustworthiness of product evaluations. To addressthe challenges, we formulate a minimax game where the spammersand spam detectors compete with each other on their practical goalsthat are not solely based on detection accuracy. Nash equilibria ofthe game lead to stable detectors that are agnostic to any mixeddetection strategies. However, the game has no closed-form solu-tion and is not differentiable to admit the typical gradient-basedalgorithms. We turn the game into two dependent Markov Deci-sion Processes (MDPs) to allow efficient stochastic optimizationbased on multi-armed bandit and policy gradient. We experimenton three large review datasets using various state-of-the-art spam-ming and detection strategies and show that the optimization al-gorithm can reliably find an equilibrial detector that can robustlyand effectively prevent spammers with any mixed spamming strate-gies from attaining their practical goal. Our code is available athttps://github.com/YingtongDou/Nash-Detect. 
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