Artificial Intelligence (AI) has been enhancing data analysis efficiency and accuracy during plant phenotyping, which is vital for tackling global agricultural and environmental challenges. Designing a reliable AI system to assist precise plant phenotyping begins with high-quality phenotypic feature annotation, which usually involves collaboration between plant scientists and AI specialists. However, due to the high level of diversity in these researchers’ backgrounds, it is likely that they have differing user needs from a fine-grained plant feature annotation system. We conducted semi-structured interviews with eight experienced annotators from diverse backgrounds, and observed how they interact with their preferred annotation system, to elucidate the challenges faced when annotating plant features and identify user needs. We collected qualitative responses to the interview questions, and conducted a quantitative evaluation of the agreement of their annotations on the given images. By analyzing the participants’ behaviors and the collected data, we identified common user needs and derived implications for the design of an AI-assisted annotation system, including providing a range of annotation options, the flexibility to adapt annotations, and functions to help addressing uncertainty. Our research contributes to the design of systems that make annotations efficient and reliable, not only benefiting plant phenotyping, but also other interdisciplinary fields that rely on user-driven annotations.

This paper presents Uncertainty-aware Contrastive Learning (UCoL): a fully unsupervised framework for discriminative facial representation learning. Our UCoL is built upon a momentum contrastive network, referred to as Dual-path Momentum Network. Specifically, two flows of pairwise contrastive training are conducted simultaneously: one is formed with intra-instance self augmentation, and the other is to identify positive pairs collected by online pairwise prediction. We introduce a novel uncertainty-aware consistency K-nearest neighbors algorithm to generate predicted positive pairs, which enables efficient discriminative learning from large-scale open-world unlabeled data. Experiments show that UCoL significantly improves the baselines of unsupervised models and performs on par with the semi-supervised and supervised face representation learning methods.

The broadcasting industry has recently begun to adopt statistical multiplexing based network platform in their workflow to support professional live audio/video (AV) transmission instead of the Time Division Multiplexing (TDM) based system. These audio-over-packet switched systems require a carefully designed and managed network to ensure key quality measures of the real-time (RT) media, such as low jitter and low latency. Often the best effort traffic or different types of media are still physically or logically segregated from these dedicated systems, or require large redundant links. The proposed Flexilink architecture is an alternative that combines both circuit switched and best effort features. However, there is no research evaluation that shows the actual performance of this proposed architecture. In this paper, we give a simulation based study and critical evaluation of the performance of the Flexilink network. The simulation results show that Flexilink has a better and more stable RT performance when compared with both Ethernet and priority queueing networks, especially when given a burst of traffic and/or multiple RT traffic sources. In addition, Unlike other networking protocols, jitter in Flexilink is below the audible threshold.