arXiv subjects

Lan Ni

Publications and source records attributed to Lan Ni.

5 recordsLinked to original sources

arXiv2025

The Study of Human Preference Based on Integrated Analysis of N1 and LPP Components

Human preference research is a significant domain in psychology and psychophysiology, with broad applications in psychiatric evaluation and daily life quality enhancement. This study explores the neural mechanisms of human preference judgments through the analysis of event-related potentials (ERPs), specifically focusing on the early N1 component and the late positive potential (LPP). Using a mixed-image dataset covering items such as hats, fruits, snacks, scarves, drinks, and pets, we elicited a range of emotional responses from participants while recording their brain activity via EEG. Our work innovatively combines the N1 and LPP components to reveal distinct patterns across different preference levels. The N1 component, particularly in frontal regions, showed increased amplitude for preferred items, indicating heightened early visual attention. Similarly, the LPP component exhibited larger amplitudes for both preferred and non-preferred items, reflecting deeper emotional engagement and cognitive evaluation. In addition, we introduced a relationship model that integrates these ERP components to assess the intensity and direction of preferences, providing a novel method for interpreting EEG data in the context of emotional responses. These findings offer valuable insights into the cognitive and emotional processes underlying human preferences and present new possibilities for brain-computer interface applications, personalized marketing, and product design.

q-bio.NC↗

arXiv2022

Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection

The key of Human-Object Interaction(HOI) recognition is to infer the relationship between human and objects. Recently, the image's Human-Object Interaction(HOI) detection has made significant progress. However, there is still room for improvement in video HOI detection performance. Existing one-stage methods use well-designed end-to-end networks to detect a video segment and directly predict an interaction. It makes the model learning and further optimization of the network more complex. This paper introduces the Spatial Parsing and Dynamic Temporal Pooling (SPDTP) network, which takes the entire video as a spatio-temporal graph with human and object nodes as input. Unlike existing methods, our proposed network predicts the difference between interactive and non-interactive pairs through explicit spatial parsing, and then performs interaction recognition. Moreover, we propose a learnable and differentiable Dynamic Temporal Module(DTM) to emphasize the keyframes of the video and suppress the redundant frame. Furthermore, the experimental results show that SPDTP can pay more attention to active human-object pairs and valid keyframes. Overall, we achieve state-of-the-art performance on CAD-120 dataset and Something-Else dataset.

cs.CV↗

arXiv2021

Hate Speech Detection in Clubhouse

With the rise of voice chat rooms, a gigantic resource of data can be exposed to the research community for natural language processing tasks. Moderators in voice chat rooms actively monitor the discussions and remove the participants with offensive language. However, it makes the hate speech detection even more difficult since some participants try to find creative ways to articulate hate speech. This makes the hate speech detection challenging in new social media like Clubhouse. To the best of our knowledge all the hate speech datasets have been collected from text resources like Twitter. In this paper, we take the first step to collect a significant dataset from Clubhouse as the rising star in social media industry. We analyze the collected instances from statistical point of view using the Google Perspective Scores. Our experiments show that, the Perspective Scores can outperform Bag of Words and Word2Vec as high level text features.

cs.LG↗

arXiv2021

Statistical Analysis of Perspective Scores on Hate Speech Detection

Hate speech detection has become a hot topic in recent years due to the exponential growth of offensive language in social media. It has proven that, state-of-the-art hate speech classifiers are efficient only when tested on the data with the same feature distribution as training data. As a consequence, model architecture plays the second role to improve the current results. In such a diverse data distribution relying on low level features is the main cause of deficiency due to natural bias in data. That's why we need to use high level features to avoid a biased judgement. In this paper, we statistically analyze the Perspective Scores and their impact on hate speech detection. We show that, different hate speech datasets are very similar when it comes to extract their Perspective Scores. Eventually, we prove that, over-sampling the Perspective Scores of a hate speech dataset can significantly improve the generalization performance when it comes to be tested on other hate speech datasets.

cs.CL↗

arXiv2010

Derivations of the finite-dimensional special odd Hamiltonian superalgebras

The aim is to determine the derivations of the three series of finite-dimensional Z-graded Lie superalgebras of Cartan-type over a field of characteristic p > 3, called the special odd Hamiltonian superalgebras. To that end we first determine the derivations of negative Z-degree for the restricted and simple special odd Hamiltonian superalgebras by means of weight space decompositions. Then the results are used to determine the derivations of negative Z-degree for the nonrestricted and non-simple special odd Hamiltonian superalgebras. Finally the derivation algebras and the outer derivation algebras of those Lie superalgebras are completely determined.

math.RT↗