A Study on the Curriculum Setting and Characteristics of the Undergraduate Philosophy Major at Oxford University

The philosophy faculty at Oxford University is ancient and stately, with profound cultural background and a good tradition of philosophical concept of education and training target, which influences the philosophical education in Britain and even in the whole world. By cultivating the students’ ability of reading, logical thinking and critical thinking, it encourages students to correctly understand the world and use the knowledge effectively to solve various practical problems. This article tries to sort out the development of undergraduates’ education of philosophy at Oxford University, to analyze the curriculum setting of philosophy in the latest ten years, and to summarize the characteristics of philosophy education

Analyzing the Distribution and Trends of Research in Double Top-University Construction in China: A Knowledge Mapping Analysis of CSSCI Literature

This research study, titled "Analysis of Double Top-University Construction in Domestic Academia: A CSSCI Literature Review (2016-2019) Using CiteSpace," provides an analysis of relevant literature on Double Top-University construction in China. The study utilizes the CiteSpace visual tool to examine the distribution characteristics of Double Top-University Construction in China. It is found that The authors, institutions, journals, and focus themes related to Double Top-University construction were remain the key component of research in recent years. Challenges and potential problems exist in the development of China's "double first class" initiative, necessitating greater scholarly attention. Specifically, efforts are required to strengthen the connection between academic research and policy implementation, conduct further research on international experiences and emerging issues, and improve interdisciplinary collaboration among related fields. Given the interdisciplinary nature and complexity of this initiative, effective coordination and integration across disciplines are essential to meet long-term strategic objectives. The findings of the analysis provide valuable insights that can guide and enrich future investigations towards the construction of Double Top-Universities

Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition

Considering the balance of performance and efficiency, sampled point and voxel methods are usually employed to down-sample dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn the representation for event data. Although good performance can be obtained, however, their results are still limited mainly due to two issues. (1) Existing event GNNs generally adopt the additional max (or mean) pooling layer to summarize all node embeddings into a single graph-level representation for the whole event data representation. However, this approach fails to capture the importance of graph nodes and also fails to be fully aware of the node representations. (2) Existing methods generally employ either a sparse point or voxel graph representation model which thus lacks consideration of the complementary between these two types of representation models. To address these issues, in this paper, we propose a novel dual point-voxel absorbing graph representation learning for event stream data representation. To be specific, given the input event stream, we first transform it into the sparse event cloud and voxel grids and build dual absorbing graph models for them respectively. Then, we design a novel absorbing graph convolutional network (AGCN) for our dual absorbing graph representation and learning. The key aspect of the proposed AGCN is its ability to effectively capture the importance of nodes and thus be fully aware of node representations in summarizing all node representations through the introduced absorbing nodes. Finally, the event representations of dual learning branches are concatenated together to extract the complementary information of two cues. The output is then fed into a linear layer for event data classification

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks

Recently, many profiling side-channel attacks based on Machine Learning and Deep Learning have been proposed. Most of them focus on reducing the number of traces required for successful attacks by optimizing the modeling algorithms. In previous work, relatively sufficient traces need to be used for training a model. However, in the practical profiling phase, it is difficult or impossible to collect sufficient traces due to the constraint of various resources. In this case, the performance of profiling attacks is inefficient even if proper modeling algorithms are used. In this paper, the main problem we consider is how to conduct more efficient profiling attacks when sufficient profiling traces cannot be obtained. To deal with this problem, we first introduce the Conditional Generative Adversarial Network (CGAN) in the context of side-channel attacks. We show that CGAN can generate new traces to enlarge the size of the profiling set, which improves the performance of profiling attacks. For both unprotected and protected cryptographic algorithms, we find that CGAN can effectively learn the leakage of traces collected in their implementations. We also apply it to different modeling algorithms. In our experiments, the model constructed with the augmented profiling set can reduce the required attack traces by more than half, which means the generated traces can provide useful information as the real traces