Impact of Online Learning on International Students’ English Language Concerns

With the onset of COVID-19, U.S. universities have been forced to move many, even all, courses online. At the University of Richmond, many of our international students faced visa restrictions due to COVID and were required to stay in their countries. As a result, the majority of our international students must attend their classes remotely. International students may find language to be a challenge during online learning. The purpose of our study is to learn more about how, if at all, online classes have an impact on international students’ English language concerns

EMO: Episodic Memory Optimization for Few-Shot Meta-Learning

Few-shot meta-learning presents a challenge for gradient descent optimization due to the limited number of training samples per task. To address this issue, we propose an episodic memory optimization for meta-learning, we call \emph{EMO}, which is inspired by the human ability to recall past learning experiences from the brain's memory. EMO retains the gradient history of past experienced tasks in external memory, enabling few-shot learning in a memory-augmented way. By learning to retain and recall the learning process of past training tasks, EMO nudges parameter updates in the right direction, even when the gradients provided by a limited number of examples are uninformative. We prove theoretically that our algorithm converges for smooth, strongly convex objectives. EMO is generic, flexible, and model-agnostic, making it a simple plug-and-play optimizer that can be seamlessly embedded into existing optimization-based few-shot meta-learning approaches. Empirical results show that EMO scales well with most few-shot classification benchmarks and improves the performance of optimization-based meta-learning methods, resulting in accelerated convergence.Comment: Accepted by CoLLAs 202

Fast Butterfly-Core Community Search For Large Labeled Graphs

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search methods need help identifying cross-group communities and suffer from efficiency issues, making them unsuitable for large graphs. This paper presents a fast community search model based on the Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate the importance of vertices within communities, allowing leader vertices to be rapidly updated to maintain cross-group cohesion. Moreover, we devised a more efficient method for updating vertex distances, which minimizes vertex visits and enhances operational efficiency. Extensive experiments on several real-world temporal graphs demonstrate the effectiveness and efficiency of this solution.Comment: 8 pages, 8 figure

Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions

Reconfigurable Intelligent Surface Aided TeraHertz Communications Under Misalignment and Hardware Impairments

TeraHertz (THz) communications are envisioned to help satisfy the ever high data rates demand with massive bandwidth in the future wireless communication systems. However, severe path attenuation, transceiver antenna misalignment, and hardware imperfection greatly alleviate the performance of THz communications. To solve this challenge, we utilize the recently proposed reconfigurable intelligent surface (RIS) technology and provide a comprehensive analytical framework of RIS-aided THz communications. More specifically, we first prove that the small-scale amplitude fading of THz signals can be exactly modeled by the fluctuating two-ray distribution based on recent measurements. Exact statistical characterizations of end-to-end signal-to-noise plus distortion ratio (SNDR) and signal-to-noise ratio (SNR) are derived. Moreover, we propose a novel method of optimizing the phase-shifts at the RIS elements under discrete phase constraints. Finally, we derive analytical expressions for the outage probability and ergodic capacity, respectively. The tight upper bounds of ergodic capacity for both ideal and non-ideal radio frequency chains are obtained. We provided Monte-Carlo simulations to validate the accuracy of our results. It is interesting to find that the impact of path loss is more pronounced compared to others, and increasing the number of elements at the RIS can significantly improve the THz communication system performance