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Dynamic Backup Sharing Scheme of Service Function Chains in NFV

China Communications 2022年第5期19卷 178-190页

作者： Dong Zhang Zhifan Zheng Xiang Lin Xiang Chen Chunming Wu College of Computer and Data Science Fuzhou UniversityFuzhou 350108China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China

Service function chains(SFC)mapping takes the responsibility for managing virtual network functions(VNFs).In SFC mapping,existing solutions duplicate VNFs with redundant instances to provide high availability in response to ***,as a compromise,these solutions result in high resource consumption due to device *** this paper,we propose a novel method named dynamic backup sharing(DBS)that allows SFCs to dynamically share backups to reduce resource *** formulates the problem of sharing backups among different VNFs as an integer linear programming(ILP).Thereafter,we design a novel online algorithm based on dynamic programming to solve the *** experimental results indicate that DBS outperforms state-ofthe-art works by reducing resource consumption and improving the number of accepted requests.

关键词： network function virtualization service function chain mapping reliability backup sharing

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Harnessing QGIS and Random Forest Analysis for Precision Storm Drain Reinforcement 2

Harnessing QGIS and Random Forest Analysis for Precision Sto...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramachandran, Ramya Arthi, D. Sudha, D. Anil, Abhirami B Babu, Aravind Md, Haripriya KCG College of Technology Artificial Intelligence and Data Science Chennai India KCG College of Technology Department of Computer Science and Engineering India KCG College of Technology Science Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331534950

Amidst the backdrop of increased urbanization and shifting climate patterns, this research delves into the growing vulnerability of landscapes to floods. The focus is on storm drain placement, emphasizing the identification of suitable locations and the need to reinforce drainage networks. The analysis takes into account parameters linked to flood sensitivity, including total rainfall, decadal growth rate, and soil types in each district within a state. Leveraging Geographic Information Systems (GIS) and relevant Coordinate Reference Systems (CRS), the study integrates a diverse array of datasets to inform precise cartographic projections. Employing a Random Forest Classifier, the study dissects the influence of these parameters on storm drain placement. The findings offer valuable insights for decision-makers to improve stormwater management systems, underscoring the importance of ongoing maintenance. Overcoming the challenge of obtaining official government data for recommendations, the research advocates the adoption of new technologies and data integration methods. By combining various data sources, such as topography, rainfall patterns, historical flood records, land use, and infrastructure data, it lays the groundwork for more advanced urban water management strategies, fortifying cities against flooding. © 2024 IEEE.

关键词： Rain

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SmartZone: Runtime Support for Secure and Efficient On-Device Inference on ARM TrustZone

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IEEE Transactions on computers 2025年第6期74卷 2144-2158页

作者： Jian, Zhaolong Liu, Xu Dong, Qiankun Cheng, Longkai Xie, Xueshuo Li, Tao Nankai University College of Computer Science Tianjin300350 China Tianjin Key Laboratory of Network and Data Science Technology Tianjin300350 China Haihe Lab of ITAI Tianjin300450 China

On-device inference is a burgeoning paradigm that performs model inference locally on end devices, allowing private data to remain local. ARM TrustZone as a widely supported trusted execution environment has been applied to provide confidentiality protection for on-device inference. However, with the rise of large-scale models like large language models (LLMs), TrustZone-based on-device inference faces challenges in migration difficulties and inefficient execution. The rudimentary TEE OS on TrustZone lacks both the inference runtime needed for building models and the parallel support necessary to accelerate inference. Moreover, the limited secure memory resources on end devices further constrain the model size and degrade performance. In this paper, we propose SmartZone to provide runtime support for secure and efficient on-device inference on TrustZone. SmartZone consists three main components: (1) a trusted inference-oriented operator set, providing the underlying mechanisms adapted to the TrustZone’s execution mode for trusted inference of DNN models and LLMs. (2) the proactive multi-threading parallel support, which increases the number of CPU cores in the secure state via cross-world thread collaboration to achieve parallelism, and (3) the on-demand secure memory management method, which statically allocates the appropriate secure memory size based on pre-execution resource analysis. We implement a prototype of SmartZone on the Raspberry Pi 3B+ board and evaluate it on four well-known DNN models and llama2 LLM. Extensive experimental results show that SmartZone provides end-to-end protection for on-device inference while maintaining excellent performance. Compared to the origin trusted inference, SmartZone accelerates the inference speed by up to 4.26× and reduces energy consumption by 65.81%. © 1968-2012 IEEE.

关键词： Memory management

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Efficient Algorithms for Maximizing Group Influence in Social Networks

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Tsinghua science and Technology 2022年第5期27卷 832-842页

作者： Peihuang Huang Longkun Guo Yuting Zhong College of Mathematics and Data Science Minjiang UniversityFuzhou 350108China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350116China

In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s *** imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated *** GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider *** on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes *** the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of ***,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for ***,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.

关键词： complementary maximum coverage(CMC) improved reverse influence sampling(IRIS) group influence maximization(GIM) independent cascade(IC)model

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Addressing Healthcare-related Racial and LGBTQ+ Biases in Pretrained Language Models

Addressing Healthcare-related Racial and LGBTQ+ Biases in Pr...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Xie, Sean Hassanpour, Saeed Vosoughi, Soroush Department of Computer Science Dartmouth College United States Department of Biomedical Data Science Dartmouth College United States

ISBN: (纸本)9798891761193

Recent studies have highlighted the issue of Pretrained Language Models (PLMs) inadvertently propagating social stigmas and stereotypes, a critical concern given their widespread use. This is particularly problematic in sensitive areas like healthcare, where such biases could lead to detrimental outcomes. Our research addresses this by adapting two intrinsic bias benchmarks to quantify racial and LGBTQ+ biases in prevalent PLMs. We also empirically evaluate the effectiveness of various debiasing methods in mitigating these biases. Furthermore, we assess the impact of debiasing on both Natural Language Understanding and specific biomedical applications. Our findings reveal that while PLMs commonly exhibit healthcare-related racial and LGBTQ+ biases, the applied debiasing techniques successfully reduce these biases without compromising the models' performance in downstream tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation 23

Joint Internal Multi-Interest Exploration and External Domai...

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2023 World Wide Web Conference, WWW 2023

作者： Liu, Weiming Zheng, Xiaolin Chen, Chaochao Su, Jiajie Liao, Xinting Hu, Mengling Tan, Yanchao College of Computer Science Zhejiang University China College of Computer and Data Science Fuzhou University China

ISBN: (纸本)9781450394161

Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize different domain knowledge and users' historical behaviors for the next-item prediction. In this paper, we focus on the cross-domain sequential recommendation problem. This commonly exist problem is rather challenging from two perspectives, i.e., the implicit user historical rating sequences are difficult in modeling and the users/items on different domains are mostly non-overlapped. Most previous sequential CDR approaches cannot solve the cross-domain sequential recommendation problem well, since (1) they cannot sufficiently depict the users' actual preferences, (2) they cannot leverage and transfer useful knowledge across domains. To tackle the above issues, we propose joint Internal multi-interest exploration and External domain alignment for cross domain Sequential Recommendation model (IESRec). IESRec includes two main modules, i.e., internal multi-interest exploration module and external domain alignment module. To reflect the users' diverse characteristics with multi-interests evolution, we first propose internal temporal optimal transport method in the internal multi-interest exploration module. We further propose external alignment optimal transport method in the external domain alignment module to reduce domain discrepancy for the item embeddings. Our empirical studies on Amazon datasets demonstrate that IESRec significantly outperforms the state-of-the-art models. © 2023 ACM.

关键词： Alignment

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Clinically Robust Polyp Segmentation: Enhanced Generalization and Perturbation Resistance

Clinically Robust Polyp Segmentation: Enhanced Generalizatio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Shanchuan Yuan, Fang Su, Jian-Nan Gan, Min College of Computer Science and Technology Qingdao University Qingdao China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350368741

Colonoscopy is vital for detecting colorectal polyps, which are closely linked to colorectal cancer. Accurate segmentation of polyps in colonoscopic images is essential for diagnosis and surgical planning but is challenging due to variability in polyp size, shape, and unclear boundaries. The Segment Anything Model (SAM) has shown promise in polyp segmentation but relies heavily on user-provided prompts and involves a large number of parameters, limiting its practicality in clinical settings. To address the limitations of SAM in clinical practice, we introduced Low Rank and Perturbation Segment Anything Model (LP-SAM) to improve segmentation accuracy and generalization ability while reducing the parameter count and complexity of user input. LP-SAM showed enhanced generalization and a lightweight design, making it more suitable for clinical applications where precise user input may not always be feasible. Comparative evaluations demonstrate that LP-SAM outperforms state-of-the-art methods on datasets such as CVC-ColonDB, CVC-300, and ETIS. © 2025 IEEE.

关键词： Low-rank Adaptation Perturbation Resistance polyp segmentation Segment Anything Model

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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FCC: Fast Source Congestion Control for Ultra-Low Latency in data Center Networks 32

FCC: Fast Source Congestion Control for Ultra-Low Latency in...

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32nd IEEE/ACM International Symposium on Quality of Service, IWQoS 2024

作者： Chen, Haodong Zhou, Jiajia Chen, Xinyang Cai, Jiayi Zhu, Longlong Liu, Hongyan Zhang, Dong Wu, Chunming College of Computer and Data Science Fuzhou University China College of Computer Science and Technology Zhejiang University China Zhicheng College Fuzhou University Fuzhou China

data center networks will elevate link bandwidth to over 200Gbps to meet the performance demands of distributed ML and NVMe. That means more traffic will be transmitted within one RTT, making the workload more burst. The challenge posed to congestion control (CC) is that there is barely enough time to make correct decisions. Sub-RTT, a method for switches to proactively generate congestion signals, reduces feedback delays, enhancing CC to handle burst traffic. However, Sub-RTT increases the sensitivity of the sender to queuing in switch buffers, thus leading to issues such as overreactions and link oscillations. We identified that utilizing fine-grained congestion signals and refining congestion stages to adjust congestion control decisions accordingly is key to solving the overreaction problem. In this paper, we propose FCC, a CC algorithm capable of swiftly addressing congestion with Sub-RTT while preventing overreactions in burst workload, thus achieving ultra-low latency and maintaining high link throughput. FCC utilizes programmable data planes to gather fine-grained congestion information through in-band network telemetry (INT), deploys bitmap-based snapshots on switches for real-time congestion stage identification, and dynamically allocates decisions. Extensive simulation experiments indicate that, compared to HPCC, FCC reduces the peak buffer occupancy on bottleneck switches by a factor of 3.2. In large-scale cluster simulations, FCC, when compared to HPCC and DCQCN, respectively, reduces the 99-th-percentile flow completion time of short flows by 39.2% and 128.2%. © 2024 IEEE.

关键词： Time switches

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