198 research outputs found
Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study
Recently, ChatGPT has drawn great attention from both the research community
and the public. We are particularly curious about whether it can serve as a
universal sentiment analyzer. To this end, in this work, we provide a
preliminary evaluation of ChatGPT on the understanding of opinions, sentiments,
and emotions contained in the text. Specifically, we evaluate it in four
settings, including standard evaluation, polarity shift evaluation, open-domain
evaluation, and sentiment inference evaluation. The above evaluation involves
18 benchmark datasets and 5 representative sentiment analysis tasks, and we
compare ChatGPT with fine-tuned BERT and corresponding state-of-the-art (SOTA)
models on end-task. Moreover, we also conduct human evaluation and present some
qualitative case studies to gain a deep comprehension of its sentiment analysis
capabilities.Comment: Technical Repor
Health condition assessment of ball bearings using TOSELM
The health condition assessment of Electric Multiple Unit (EMU) traction motor ball bearing is one of the key issues of high-speed train running safety. In order to assess health condition of EMU traction motor ball bearing, an online-sequential extreme learning machine algorithm based on TensorFlow (TOSELM) is proposed. Samples data set is divided into normal condition and fault condition using vibration data of ball bearings. This paper uses health condition accuracy rate index to evaluate TOSELM algorithm performance. The proposed approach is verified by public data set and private data set. The experiment results show the proposed method is an effective method for ball bearing health status assessment
Efficient Link Prediction in Continuous-Time Dynamic Networks using Optimal Transmission and Metropolis Hastings Sampling
Efficient link prediction in continuous-time dynamic networks is a
challenging problem that has attracted much research attention in recent years.
A widely used approach to dynamic network link prediction is to extract the
local structure of the target link through temporal random walk on the network
and learn node features using a coding model. However, this approach often
assumes that candidate temporal neighbors follow some certain types of
distributions, which may be inappropriate for real-world networks, thereby
incurring information loss. To address this limitation, we propose a framework
in continuous-time dynamic networks based on Optimal Transmission (OT) and
Metropolis Hastings (MH) sampling (COM). Specifically, we use optimal
transmission theory to calculate the Wasserstein distance between the current
node and the time-valid candidate neighbors to minimize information loss in
node information propagation. Additionally, we employ the MH algorithm to
obtain higher-order structural relationships in the vicinity of the target
link, as it is a Markov Chain Monte Carlo method and can flexibly simulate
target distributions with complex patterns. We demonstrate the effectiveness of
our proposed method through experiments on eight datasets from different
fields.Comment: 11 pages, 7 figure
S3E: A Large-scale Multimodal Dataset for Collaborative SLAM
With the advanced request to employ a team of robots to perform a task
collaboratively, the research community has become increasingly interested in
collaborative simultaneous localization and mapping. Unfortunately, existing
datasets are limited in the scale and variation of the collaborative
trajectories, even though generalization between inter-trajectories among
different agents is crucial to the overall viability of collaborative tasks. To
help align the research community's contributions with realistic multiagent
ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset
captured by a fleet of unmanned ground vehicles along four designed
collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor
sequences that each exceed 200 seconds, consisting of well temporal
synchronized and spatial calibrated high-frequency IMU, high-quality stereo
camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous
attempts regarding dataset size, scene variability, and complexity. It has 4x
as much average recording time as the pioneering EuRoC dataset. We also provide
careful dataset analysis as well as baselines for collaborative SLAM and single
counterparts. Data and more up-to-date details are found at
https://github.com/PengYu-Team/S3E
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