Author: Elijah Moore

Authors: Md Fahim Sikder、Resmi Ramachandranpillai、Daniel de Leng、Fredrik Heintz Paper: https://arxiv.org/abs/2408.10755 Introduction Background The rapid proliferation of AI-powered applications has brought significant advancements in various fields, such as language translation and healthcare diagnosis. However, the datasets used to train these AI systems often contain biases, either from human or machine sources. These biases can lead to unfair outcomes when the AI models are deployed, affecting certain demographics disproportionately. For instance, the COMPAS case highlighted how risk assessment software could unfairly target African-Americans due to biased training data. Problem Statement Existing bias-mitigation techniques, such as Generative Adversarial Networks (GANs) and Diffusion models,…

Authors: Chen Yang、Sunhao Dai、Yupeng Hou、Wayne Xin Zhao、Jun Xu、Yang Song、Hengshu Zhu Paper: https://arxiv.org/abs/2408.09748 Introduction Reciprocal Recommender Systems (RRS) have become increasingly significant in enhancing matching efficiency in various domains such as online dating, recruitment, and social networks. Unlike conventional recommender systems that focus on user-item interactions, RRS involves bilateral recommendations between two parties, each with unique preferences and requirements. Despite the growing interest in RRS, existing evaluation methods often reuse conventional ranking metrics, assessing each side of the recommendation process independently. This approach overlooks the collective influence of both sides on the effectiveness of the RRS, necessitating a more holistic evaluation…

Authors: Shashank Kotyan、Pin-Yu Chen、Danilo Vasconcellos Vargas Paper: https://arxiv.org/abs/2408.09065 Introduction Background The rapid advancements in computer vision and deep learning have led to the development of powerful vision models capable of extracting intricate features from visual data. These models are central to various applications, from object recognition to image generation. Typically, their generalizability is measured through zero-shot classification performance, making the evaluation of vision models indirect. However, these evaluations often rely on a projection of the learned latent space, which may not fully capture the quality or nuances of the underlying representations and offer little insight into improving them. Problem Statement…

Authors: Xin Wang、Hector Delgado、Hemlata Tak、Jee-weon Jung、Hye-jin Shim、Massimiliano Todisco、Ivan Kukanov、Xuechen Liu、Md Sahidullah、Tomi Kinnunen、Nicholas Evans、Kong Aik Lee、Junichi Yamagishi Paper: https://arxiv.org/abs/2408.08739 Introduction The ASVspoof initiative aims to advance the development of detection solutions, known as countermeasures (CMs) and presentation attack detection (PAD) solutions, to differentiate between genuine and spoofed or deepfake speech utterances. ASVspoof 5, the fifth edition of this challenge, introduces significant changes in track definitions, databases, spoofing attacks, and evaluation metrics. Unlike the 2021 challenge, which had distinct logical access (LA), physical access (PA), and speech deepfake (DF) sub-tasks, ASVspoof 5 combines LA and DF tasks into two tracks: (i) stand-alone…

Authors: Yucheng Sheng、Kai Huang、Le Liang、Peng Liu、Shi Jin、Geoffrey Ye Li Paper: https://arxiv.org/abs/2408.08707 Introduction Millimeter-wave (mmWave) communication is a cornerstone technology for next-generation wireless networks due to its vast bandwidth capabilities. However, mmWave signals suffer from significant path loss, necessitating the use of extensive antenna arrays and frequent beam training to ensure optimal directional transmission. Traditional deep learning models, such as long short-term memory (LSTM) networks, have been employed to enhance beam tracking accuracy. Despite their effectiveness, these models often struggle with robustness and generalization across different wireless environments. In this study, we propose leveraging large language models (LLMs) to improve the…