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20162025
most citedUnderstanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

39 citations · 50 across the 21 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL20231 cited

BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues

Haodong Duan, Jueqi Wei, Chonghua Wang +5

Interacting with human via high-quality multi-turn dialogues is a key feature of large language models (LLMs). However, human-based evaluation of such capability involves intensive…

cs.LG20233 cited

Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testing

Jinwen He, Kai Chen, Guozhu Meng +2

While enjoying the great achievements brought by deep learning (DL), people are also worried about the decision made by DL models, since the high degree of non-linearity of DL mode…

cs.SE2023

ConFL: Constraint-guided Fuzzing for Machine Learning Framework

Zhao Liu, Quanchen Zou, Tian Yu +4

As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) f…

cs.SD2023

Local spectral attention for full-band speech enhancement

Zhongshu Hou, Qinwen Hu, Kai Chen +1

Attention mechanism has been widely utilized in speech enhancement (SE) because theoretically it can effectively model the inherent connection of signal both in time domain and spe…

cs.SD2023

Attention does not guarantee best performance in speech enhancement

Zhongshu Hou, Qinwen Hu, Kai Chen +1

Attention mechanism has been widely utilized in speech enhancement (SE) because theoretically it can effectively model the long-term inherent connection of signal both in time doma…