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IEEE Workshop on High Performance Switching and Routing

作者： Zhai Mingyu Zhao Qi Luo Junzhou Gu Guanqun Department of Computer Science and Engineering South East University Nanjing China The Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)0780367111

Currently the research on input-queued ATM switches is one of the most active research fields. Many achievements have been made in the research on scheduling algorithms for unicast input-queued ATM switches and also applied in commerce. But the goal of the research on scheduling algorithms for multicast input-queued ATM switches only focuses on providing high throughput and inadvertently ignoring its undesired effects on QoS of the multicast traffic. In this paper we present a design scheme of input-queued ATM switches supporting multicast and corresponding scheduling algorithm, referred to as multicast longest normalized queue first (MLNQF). The MLNQF algorithm has the characteristics of improving throughput, satisfying QoS requirements and providing service fairly.

关键词： Switches Asynchronous transfer mode

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Achieving fair bandwidth allocation without per-flow state

Achieving fair bandwidth allocation without per-flow state

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International Conference on computer networks and Mobile Computing (ICCNMC)

作者： Zhai Mingyu Gu Guanqun Department of Computer Science and Engineering South-East University Nanjing China The Key Laboratory for Computer Networks and Information Integration Ministry of Education China

In this paper, we present Queue Length based Fair Queueing (QLFQ), a scheme to approximate fair bandwidth allocation without per flow state. Edge routers divide each flow into a set of layers using a linear encoding scheme and insert an appropriate label into each packet header. Core routers maintain a dropping threshold at each router; packets with a label greater than the threshold are dropped. In order to determine the dropping threshold, we only need to know queue occupancy information and do few comparison operations in core routers (so we call our scheme Queue Length based Fair Queueing (QLFQ)). We have evaluated QLFQ together with CSFQ and RFQ with several different configurations and traffic sources. The simulation results show that QLFQ is able to achieve approximately fair bandwidth sharing in all of these scenarios. The performance of QLFQ is comparable to that of CSFQ, and it performs much better than RFQ.

关键词： Channel allocation Encoding computer science Laboratories computer networks computer science education Bandwidth High-speed networks Algorithm design and analysis Scalability

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Fair bandwidth allocations through queue management in core-stateless networks

Fair bandwidth allocations through queue management in core-...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： Zha Mingyu Gu Guanqun Yuan Yuan Department of Computer Science & Engineering South-East University Nanjing China The Key Laboratory for Computer Networks and Information Integration Ministry of Education China

We present queue length based fair queueing (QLFQ), a scheme to approximate fair bandwidth allocation without per-flow state. We have evaluated QLFQ together with CSFQ (core-stateless fair queueing) (see Stoica, I., Proc. ACM SIGCOMM'98, p.119-30, 1998) and RFQ (rainbow fair queueing) (see Cao, Z., Proc. IEEE Infocom, 2000) with several different configurations and traffic sources. The simulation results show that QLFQ is able to achieve approximately fair bandwidth sharing in all of these scenarios. The performance of QLFQ is comparable to that of CSFQ and it performs much better than RFQ. The simulations also show that in an environment with bursty traffic sources, QLFQ provides much better fairness than CSFQ.

关键词： Bandwidth Intelligent networks

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A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

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ACM Computing Surveys 1000年

作者： Md Palash Uddin Yong Xiang Mahmudul Hasan Jun Bai Yao Zhao Longxiang Gao School of Information Technology Deakin University Burwood Australia Deakin University Burwood Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.

关键词： Federated Learning Robustness Attack Countermeasure Defence.

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Compression Approaches for LiDAR Point Clouds and Beyond: A Survey

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Miaohui Wang Runnan Huang Wuyuan Xie Zhan Ma Siwei Ma Guangdong Key Laboratory of Intelligent Information Processing State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China School of Electronic Science and Engineering Nanjing University China School of Computer Science Peking University China

With the widespread use of LiDAR sensors in autonomous driving, LiDAR point cloud compression (LPCC) plays an important role in effectively managing the storage, transmission, and perception of the growing volume of LiDAR data. Despite this need, there has been a noticeable absence of comprehensive investigations specifically dedicated to LPCC methods. To address this issue, this paper presents a systematic survey of existing LPCCs, aiming to summarize recent progress and inspire future research in this field. We begin by providing a general introduction of LPCC fundamentals, covering the latest LiDAR point cloud (LPC) datasets, distinctive attributes, evaluation metrics, and data formats. We then conduct a careful review and comparison of LPCCs, examining image-based, octree-based, deep-learned, and other approaches, offering valuable insights into the strengths and weaknesses of cutting-edge models. Finally, we propose future research directions based on the limitations of recent LPCCs. We believe that the findings presented in this paper will contribute to a deeper understanding of LPCCs and promote further development of LiDAR sensor-based systems.

关键词： LiDAR point cloud compression LiDAR data attributes LiDAR coding survey

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How do Large Language Models understand Genes and Cells

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ACM Transactions on Intelligent Systems and Technology 1000年

作者： Chen Fang Yidong Wang Yunze Song Qingqing Long Wang Lu Linghui Chen Guihai Feng Yuanchun Zhou Xin Li State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology Chinese Academy of Sciences Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences China Peking University China University of Liverpool UK Computer Network Information Center Chinese Academy of Sciences Tsinghua University China iFLYTEK Research Oristruct Biotech Co. Ltd China University of Chinese Academy of Sciences Chin

Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation capabilities, have made significant strides across various natural science fields. However, their application in cell biology remains limited and a systematic evaluation of their performance is lacking. To address this gap, in this paper, we select seven mainstream LLMs and evaluate their performance across nine gene-related problem scenarios. Our findings indicate that LLMs possess a certain level of understanding of genes and cells, but still lag behind domain-specific models in comprehending transcriptional expression profiles. Moreover, we have improved the current method of textual representation of cells, enhancing the LLMs’ ability to tackle cell annotation tasks. We encourage cell biology researchers to leverage LLMs for problem-solving while being mindful of the associated challenges. We release our code and data at https://***/epang-ucas/Evaluate_LLMs_to_Genes.

关键词： large language models cell biology gene gene interaction cell annotation

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S3 Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

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ACM Transactions on Multimedia Computing, Communications, and Applications 1000年

作者： Peng Wang Yongheng Zhang Hao Fei Qiguang Chen Yukai Wang Jiasheng Si Wenpeng Lu Min Li Libo Qin School of Computer Science and Engineering Central South University China National University of Singapore Singapore Harbin Institute of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

关键词： Natural language processing Multi-modal sarcasm detection Vision Large language model

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ChaseCharge: Charging Sensors by Chasing in Sensor-Cloud Systems

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ACM Transactions on Sensor networks 1000年

作者： Chi Lin Rui Guo Chaocan Xiang Yi Wang Qiang Lin School of Software Technology Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China The College of Computer Science Chongqing University Chongqing China The DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China School of Maritime Economics and Management Dalian Maritime University Collaborative Innovation Center for Transport Studies Dalian Maritime University The party school of municipal Party committee of Dalian China

The recent breakthrough in wireless power transfer technology enables power to be delivered between transceivers, which is quite helpful for sensor-cloud systems. Traditional charging schemes are only suitable for static sensors, while the issue of charging mobile sensors is ignored. In this paper, we make the first attempt to serve mobile sensors in the sensor-cloud systems in a “chasing” way, where a mobile charger can chase mobile sensors to replenish them. We formalize the charging utility MAximization Problem for dynamic sensors with a mobile charger (MAP) and propose a ChaseCharge algorithm based on RNN to solve it. Theoretical analyses are presented to explore the features of the proposed scheme. We carry out simulations, and the results show that the performance of our algorithm outperforms comparison algorithms by 33% in utility on average. Test-bed experiments are conducted to validate the applicability of the proposed scheme in oceanic monitoring applications.

关键词： Wireless Rechargeable Sensor network Mobile Charging RNN

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Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation

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ACM Transactions on information Systems 1000年

作者： Lei Guo Chenlong Song Feng Guo Xiaohui Han Xiaojun Chang Lei Zhu Shandong Normal University China Liaocheng University China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences) China and Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Tongji University China

Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: 1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. 2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. 3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train & prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released.

关键词： Cross-domain recommendation sequential recommendation non-overlapping cross-domain recommendation

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