Visual Content Characterization Based on Encoding Rate-Distortion Analysis

Visual content characterization is a fundamentally important but under exploited step in dataset construction, which is essential in solving many image processing and computer vision problems. In the era of machine learning, this has become ever more important, because with the explosion of image and video content nowadays, scrutinizing all potential content is impossible and source content selection has become increasingly difficult. In particular, in the area of image/video coding and quality assessment, it is highly desirable to characterize/select source content and subsequently construct image/video datasets that demonstrate strong representativeness and diversity of the visual world, such that the visual coding and quality assessment methods developed from and validated using such datasets exhibit strong generalizability. Encoding Rate-Distortion (RD) analysis is essential for many multimedia applications. Examples of applications that explicitly use RD analysis include image encoder RD optimization, video quality assessment (VQA), and Quality of Experience (QoE) optimization of streaming videos etc. However, encoding RD analysis has not been well investigated in the context of visual content characterization. This thesis focuses on applying encoding RD analysis as a visual source content characterization method with image/video coding and quality assessment applications in mind. We first conduct a video quality subjective evaluation experiment for state-of-the-art video encoder performance analysis and comparison, where our observations reveal severe problems that motivate the needs of better source content characterization and selection methods. Then the effectiveness of RD analysis in visual source content characterization is demonstrated through a proposed quality control mechanism for video coding by eigen analysis in the space of General Quality Parameter (GQP) functions. Finally, by combining encoding RD analysis with submodular set function optimization, we propose a novel method for automating the process of representative source content selection, which helps boost the RD performance of visual encoders trained with the selected visual contents

Depth and Breadth of Research Area Coverage and Its Impact on Publication Citation: An Analysis of Bibliometric Papers

Many other factors affecting citation of publications, except for research area coverage, have been studied. This study aims to investigate impact of research area coverage. Bibliometric papers and their related papers (referred papers, citing papers and first author's papers) were screened and matched by Python program. Papers' research areas were classified according to Web of Science. Bibliometric parameters of the most cited 5% and the least cited 5% papers were compared. Firstly, coverage of related papers' research areas impacts the citation of their original papers. The impact of references and citing papers are positive and negative, separately, while the first author's papers have no influence. Secondly, high-influence papers tend to cite references from a wider area and are cited by followers from a wider area. Additionally, the pattern of knowledge flow differs significantly between high- and low-influence papers. Low-influence papers narrow knowledge flow, whereas high-influence papers broaden it. This study has shown that both depth and breadth of research area coverage can influence citations. It is recommended that authors should extensively cite high-influence publications, both within and beyond their own area

Nanocomposite for Space Charge Suppression in HVDC Cable Accessory

HVDC cable accessories made of ethylene-vinyl acetate copolymer (EVA) by incorporation of specific fillers have to face the problem of space charge accumulation. The effects of doping contents on the space charge behaviors of EVA/ZnO composite are not completely clear. EVA composites are prepared with the fraction of 0, 1, 5 and 10 wt%, respectively, with which 5 wt% nano-sized plus 5 wt% micro-sized ZnO-doped samples are chosen for comparison. Obtained results show that the particles in EVA composite are in homodisperse. The permittivity is increased by ZnO doping and the dissipation factor of EVA composites with 1 and 5 wt% nanoparticles is lower at the lower frequencies. The homocharge injection occurs in cathode instead of anode when ZnO nanoparticles are introduced and 5 wt% nanoparticle doping performs well in suppressing space charge injection. The electric field in the 5 wt% nanoparticle-doped EVA distributes more uniformly under the high electric stress than that of others. During the depolarization procedure, the total remnant charges of 10 wt% doped samples are the least in the final. The above results are well explained by the DC conduction, apparent mobility and trap distribution characteristics

Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment. Researchers have recently explored using large language models (LLMs) to generate synthetic datasets as an alternative approach. However, the effectiveness of the LLM-generated synthetic data in supporting model training is inconsistent across different classification tasks. To better understand factors that moderate the effectiveness of the LLM-generated synthetic data, in this study, we look into how the performance of models trained on these synthetic data may vary with the subjectivity of classification. Our results indicate that subjectivity, at both the task level and instance level, is negatively associated with the performance of the model trained on synthetic data. We conclude by discussing the implications of our work on the potential and limitations of leveraging LLM for synthetic data generation.Comment: EMNLP 202