Evacuation Guidance Design: An Experimental Study Based on Eye Tracking Devices

It is a crucial issue whether evacuees follow the evacuation guidance during evacuation. Good evacuation guidance is necessary to be designed to help the evacuees follow the guidance. In this paper, evacuation experiments based on wearable eye tracking devices were carried out to study the design effect of the evacuation guidance. Three factors were considered in these experiments: 1) the position of the evacuation guidance; 2) follow guidance or follow other evacuees; 3) follow a stranger or a familiar person. The results show that more participants noticed the guidance with low position and ground position than the guidance with up position. The evacuees intend to follow others rather than to follow the guidance, i.e. most evacuees act as “follower”. Eye tracking evacuation experiments can also be used to test the effectiveness of evacuation guidance signs

Trajectory Planning with Pose Feedback for a Dual-Arm Space Robot

In order to obtain high precision path tracking for a dual-arm space robot, a trajectory planning method with pose feedback is proposed to be introduced into the design process in this paper. Firstly, pose error kinematic models are derived from the related kinematics and desired pose command for the end-effector and the base, respectively. On this basis, trajectory planning with pose feedback is proposed from a control perspective. Theoretical analyses show that the proposed trajectory planning algorithm can guarantee that pose error converges to zero exponentially for both the end-effector and the base when the robot is out of singular configuration. Compared with the existing algorithms, the proposed algorithm can lead to higher precision path tracking for the end-effector. Furthermore, the algorithm renders the system good anti-interference property for the base. Simulation results demonstrate the effectiveness of the proposed trajectory planning algorithm

Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility

ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction

Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations for benefiting all test instances. We design demonstrations describing relationships that enable LLMs to understand positional relationships. We introduce formatting demonstrations for easy answer extraction. Additionally, the framework improves diverse demonstrations by updating them iteratively. Our experiments on three widely used benchmark datasets demonstrate that the ICL-D3IE framework enables GPT-3/ChatGPT to achieve superior performance when compared to previous pre-trained methods fine-tuned with full training in both the in-distribution (ID) setting and in the out-of-distribution (OOD) setting