Control designs and reinforcement learning-based management for software defined networks

In this thesis, we focus our investigations around the novel software defined net- working (SDN) paradigm. The central goal of SDN is to smoothly introduce centralised control capabilities to the otherwise distributed computer networks. This is achieved by abstracting and concentrating network control functionalities in a logically centralised control unit, which is referred to as the SDN controller. To further balance between centralised control, scalability and reliability considerations, distributed SDN is introduced to enable the coexistence of multiple physical SDN controllers. For distributed SDN, networking elements are grouped together to form various domains, with each domain managed by an SDN controller. In such a distributed SDN setting, SDN controllers of all domains synchronise with each other to maintain logically centralised network views, which is referred to as controller synchronisation. Centred on the problem of SDN controller synchronisation, this thesis specifically aims at addressing two aspects of the subject as follows. First, we model and analyse the performance enhancements brought by controller synchronisation in distributed SDN from a theoretical perspective. Second, we design intelligent controller synchronisation policies by leveraging existing and creating new Reinforcement Learning (RL) and Deep Learning (DL)-based approaches. In order to understand the performance gains of SDN controller synchronisation from a fundamental and analytical perspective, we propose a two-layer network model based on graphs to capture various characteristics of distributed SDN net- works. Then, we develop two families of analytical methods to investigate the performance of distributed SDN in relationship to network structure and the level of SDN controller synchronisation. The significance of our analytical results is that they can be used to quantify the contribution of controller synchronisation level, in improving the network performance under different network parameters. Therefore, they serve as fundamental guidelines for future SDN performance analyses and protocol designs. For the designs of SDN controller synchronisation policies, most existing works focus on the engineering-centred system design aspect of the problem for ensuring anomaly-free synchronisation. Instead, we emphasise on the performance improvements with respect to (w.r.t.) various networking tasks for designing controller synchronisation policies. Specifically, we investigate various scenarios with diverse control objectives, which range from routing related performance metric to other more sophisticated optimisation goals involving communication and computation resources in networks. We also take into consideration factors such as the scalability and robustness of the policies developed. For this goal, we employ machine learning techniques to assist our policy designs. In particular, we model the SDN controller synchronisation policy as serial decision-making processes and resort to RL-based techniques for developing the synchronisation policy. To this end, we leverage a combination of various RL and DL methods, which are tailored for handling the specific characteristics and requirements in different scenarios. Evaluation results show that our designed policies consistently outperform some already in-use controller synchronisation policies, in certain cases by considerable margins. While exploring existing RL algorithms for solving our problems, we identify some critical issues embedded within these algorithms, such as the enormity of the state-action space, which can cause inefficiency in learning. As such, we propose a novel RL algorithm to address these issues, which is named state action separable reinforcement learning (sasRL). Therefore, the sasRL approach constitutes another major contribution of this thesis in the field of RL research.Open Acces

The Impact of Warmth and Competence on Job Candidates: Racial Stereotype Management Strategies for Asian Americans on Social Media

This study investigated the online stereotype management strategies utilized by Asian Americans during the job application process when seeking employment with Caucasian employers. Guided by the Stereotype Content Model, this study aimed to assess how visual cues related to warmth and competence in social media profiles influence the likelihood of Asian Americans of different gender and racial subgroups being hired. Toward this goal, this study posed nine research questions covering a variety of factors that may influence hiring decisions. The findings provide valuable insights into the complex role of social media in the hiring process, as well as the impact of political ideology and beliefs about COVID-19 on hiring intentions. By delving into these nuances, this study contributes to a more comprehensive understanding of the challenges Asian Americans face in the job market and the strategies they employ to address them

A Method to Judge the Style of Classical Poetry Based on Pre-trained Model

One of the important topics in the research field of Chinese classical poetry is to analyze the poetic style. By examining the relevant works of previous dynasties, researchers judge a poetic style mostly by their subjective feelings, and refer to the previous evaluations that have become a certain conclusion. Although this judgment method is often effective, there may be some errors. This paper builds the most perfect data set of Chinese classical poetry at present, trains a BART-poem pre -trained model on this data set, and puts forward a generally applicable poetry style judgment method based on this BART-poem model, innovatively introduces in-depth learning into the field of computational stylistics, and provides a new research method for the study of classical poetry. This paper attempts to use this method to solve the problem of poetry style identification in the Tang and Song Dynasties, and takes the poetry schools that are considered to have a relatively clear and consistent poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic school and Tongguang poetic school, as the research object, and takes the poems of their representative poets for testing. Experiments show that the judgment results of the tested poetry work made by the model are basically consistent with the conclusions given by critics of previous dynasties, verify some avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry style recognition in the Tang and Song dynasties.Comment: 4 pages, 2 figure

PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning

Contrastive Language-Image Pre-training (CLIP) has shown promising open-world performance on 2D image tasks, while its transferred capacity on 3D point clouds, i.e., PointCLIP, is still far from satisfactory. In this work, we propose PointCLIP V2, a powerful 3D open-world learner, to fully unleash the potential of CLIP on 3D point cloud data. First, we introduce a realistic shape projection module to generate more realistic depth maps for CLIP's visual encoder, which is quite efficient and narrows the domain gap between projected point clouds with natural images. Second, we leverage large-scale language models to automatically design a more descriptive 3D-semantic prompt for CLIP's textual encoder, instead of the previous hand-crafted one. Without introducing any training in 3D domains, our approach significantly surpasses PointCLIP by +42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D classification. Furthermore, PointCLIP V2 can be extended to few-shot classification, zero-shot part segmentation, and zero-shot 3D object detection in a simple manner, demonstrating our superior generalization ability for 3D open-world learning. Code will be available at https://github.com/yangyangyang127/PointCLIP_V2

VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts

Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention recently for its transferable visual representation learning. However, due to the semantic gap within datasets, CLIP's pre-trained image-text alignment becomes sub-optimal on downstream tasks, which severely harms its transferring performance. To better adapt the cross-modality embedding space, we propose to enhance CLIP via Visual-guided Texts, named VT-CLIP. Specifically, we guide textual features of different categories to adaptively explore informative regions on the image and aggregate visual features by attention mechanisms. In this way, the texts become visual-guided, namely, more semantically correlated with downstream images, which greatly benefits the category-wise matching process. In few-shot settings, we evaluate our VT-CLIP on 11 well-known classification datasets to demonstrate its effectiveness