Automatic Quality Assessment of Lecture Videos Using Multimodal Features

Multimedia Retrieval, eine entwickelte Methodologie, welche aus Information Retrieval stammt, wird in der digitalisierten Gesellschaft weit verbreitet eingesetzt. Bei der Suche nach Videos im Internet, müssen diese nach ihrer Relevanz sortiert werden. Die meisten Ansätze berechnen die Relevanz jedoch nur aus grundlegenden Inhaltsinformationen. Ziel dieser Arbeit ist es, Relevanz in verschiedenen Modalitäten zu analysieren. Für den konkreten Fall von Vortragsvideos, Merkmale von folgenden Modalitäten werden von dementsprechenden Kursmaterialien extrahiert: akustische, linguistische, und visuelle Modalität. Außerdem sind modalitätsübergreifende Merkmale insbesondere in dieser Arbeit zunächst vorgeschlagen und berechnet durch die Verarbeitung von Audio, Bilder, Transkripte und Texte. Eine Benutzerevaluation wurde durchgeführt, um Benutzermeinungen in Bezug auf die erzeugten Merkmale zu erheben. Die Ergebnisse haben gezeigt, dass die meisten Merkmale ein Video in verschiedenen Aspekten widerspiegeln können. Die Art und Weise, wie der Lerneffekt durch diese Merkmale beeinflusst wird, wird ebenfalls berücksichtigt. Für die weitere Forschung baut diese Studie eine solide Basis für die Extraktion der Merkmale auf. Zudem gewinnt die Arbeit ein besseres Verständnis zum Lernen.Mutimedia retrieval, a developed methodology based on information retrieval, is broadly used in the digitalised society. When searching videos online, they need to be sorted according to their relevance. However, most approaches calculate the relevance only from basic content information. This thesis aims to analyse the relevance in multiple modalities. For the specific case of lecture videos, features from following modalities are extracted from corresponding course materials: audio, linguistic, and visual modality. Furthermore, cross-modal features are specifically first proposed in this thesis and calculated by processing audio, images, transcripts, and texts. A user evaluation has been conducted to collect user's opinions with regards to these generated features. The results have shown that most features can reflect a video in multiple aspects. The way the learning effect is influenced by these features is considered as well. For further research, this study builds a solid base for feature extraction and gains a better understanding of learning

Dust-Deficient Palomar-Green Quasars and the Diversity of AGN Intrinsic IR Emission

To elucidate the intrinsic broadband infrared (IR) emission properties of active galactic nuclei (AGNs), we analyze the spectral energy distributions (SEDs) of 87 z<0.5 Palomar-Green (PG) quasars. While the Elvis AGN template with a moderate far-IR correction can reasonably match the SEDs of the AGN components in ~60% of the sample (and is superior to alternatives such as that by Assef), it fails on two quasar populations: 1) hot-dust-deficient (HDD) quasars that show very weak emission thoroughly from the near-IR to the far-IR, and 2) warm-dust-deficient (WDD) quasars that have similar hot dust emission as normal quasars but are relatively faint in the mid- and far-IR. After building composite AGN templates for these dust-deficient quasars, we successfully fit the 0.3-500 {\mu}m SEDs of the PG sample with the appropriate AGN template, an infrared template of a star-forming galaxy, and a host galaxy stellar template. 20 HDD and 12 WDD quasars are identified from the SED decomposition, including seven ambiguous cases. Compared with normal quasars, the HDD quasars have AGN with relatively low Eddington ratios and the fraction of WDD quasars increases with AGN luminosity. Moreover, both the HDD and WDD quasar populations show relatively stronger mid-IR silicate emission. Virtually identical SED properties are also found in some quasars from z = 0.5 to 6. We propose a conceptual model to demonstrate that the observed dust deficiency of quasars can result from a change of structures of the circumnuclear tori that can occur at any cosmic epoch.Comment: minor corrections to match the published versio

Analysis of Zijin Mining's Green Transformation and Effectiveness under ESG Concepts

Based on the ESG concept, this paper discusses the practice and effect of non-ferrous metal industrial enterprises in green transformation. Taking Zijin Mining as a case study, it analyses the motivation of Zijin Mining's green transformation and combs through its green governance path, assesses the effectiveness of the transformation from the three levels of governance, environment and society, and evaluates the effect of governing Zijin Mining's green transformation. The study finds that Zijin Mining's green transformation has significantly improved economic performance, energy saving and emission reduction, and actively promoted social performance, reflecting the corporate environmental and social responsibility. The conclusions of this paper not only provide feedback for Zijin Mining's green governance, but also provide practical references for the green transformation of enterprises in the same industry, helping enterprises to achieve the goal of sustainable development

FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model parameters with others. Each client updates its local model using the average model output/feature of all client models as the target, known as federated KD. However, existing federated KD methods often do not perform well when clients' local models are trained with heterogeneous local datasets. In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients. First, to alleviate the local model output divergence across clients caused by data heterogeneity, the server acts as a discriminator to guide clients' local model training to achieve consensus model outputs among clients through a min-max game between clients and the discriminator. Moreover, catastrophic forgetting may happen during the clients' local training and global knowledge transfer due to clients' heterogeneous local data. Towards this challenge, we design the less-forgetting regularization for both local training and global knowledge transfer to guarantee clients' ability to transfer/learn knowledge to/from others. Experimental results show that FedAL and its variants achieve higher accuracy than other federated KD baselines

Hidden Conformal Symmetry for Vector Field on Various Black Hole Backgrounds

Hidden conformal symmetries of scalar field on various black hole backgrounds have been investigated for years, but whether those features holds for other fields are still open questions. Recently, with proper assumptions, Lunin achieves to the separation of variables for Maxwell equations on Kerr background. In this paper, with that equation, we find that hidden conformal symmetry appears at near region under low frequency limit. We also extended those results to vector field on Kerr-(A)dS and Kerr-NUT-(A)dS backgrounds, then hidden conformal symmetry also appears if we focusing on the near-horizon region at low frequency limit.Comment: 18 pages, no figure, matches the published versio