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pro vyhledávání: '"MA, Shuai"'
Massive communication is one of key scenarios of 6G where two magnitude higher connection density would be required to serve diverse services. As a promising direction, unsourced multiple access has been proved to outperform significantly over orthog
Externí odkaz:
http://arxiv.org/abs/2409.13398
With the exponential surge in traffic data and the pressing need for ultra-low latency in emerging intelligence applications, it is envisioned that 6G networks will demand disruptive communication technologies to foster ubiquitous intelligence and su
Externí odkaz:
http://arxiv.org/abs/2408.04825
Autor:
Ma, Shuai, Zhang, Chuanhui, Shen, Bin, Wu, Youlong, Li, Hang, Li, Shiyin, Shi, Guangming, Al-Dhahir, Naofal
With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature divis
Externí odkaz:
http://arxiv.org/abs/2407.08424
Autor:
Zhu, Qian, Wang, Dakuo, Ma, Shuai, Wang, April Yi, Chen, Zixin, Khurana, Udayan, Ma, Xiaojuan
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where
Externí odkaz:
http://arxiv.org/abs/2405.14107
By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust representations of the target inference. However, extending the idea t
Externí odkaz:
http://arxiv.org/abs/2405.04144
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the
Externí odkaz:
http://arxiv.org/abs/2403.16812
Migration and aging-related dilemmas have limited the opportunities for late-life migrants to rebuild social connections and access support. While research on migrants has drawn increasing attention in HCI, limited attention has been paid to the incr
Externí odkaz:
http://arxiv.org/abs/2403.10172
In AI-assisted decision-making, it is crucial but challenging for humans to achieve appropriate reliance on AI. This paper approaches this problem from a human-centered perspective, "human self-confidence calibration". We begin by proposing an analyt
Externí odkaz:
http://arxiv.org/abs/2403.09552
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct. However, recent studies suggest this may diminish human analytical thinking and l
Externí odkaz:
http://arxiv.org/abs/2403.01791
Autor:
Zheng, Chengbo, Yuan, Kangyu, Guo, Bingcan, Mogavi, Reza Hadi, Peng, Zhenhui, Ma, Shuai, Ma, Xiaojuan
The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students' AI
Externí odkaz:
http://arxiv.org/abs/2401.14915