检索结果-内蒙古大学图书馆

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

引用

computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

来源：评论

学校读者我要写书评

暂无评论

Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

引用

computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

来源：评论

学校读者我要写书评

暂无评论

When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference 24

When Neural Code Completion Models Size up the Situation: At...

引用

44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Sun, Zhensu Du, Xiaoning Song, Fu Wang, Shangwen Li, Li Beihang University Beijing China Monash University Melbourne VIC Australia Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China National University of Defense Technology Changsha China Beihang University Beijing Yunnan Key Laboratory of Software Engineering China

ISBN: (纸本)9798400702174

Leveraging recent advancements in large language models, modern neural code completion models have demonstrated the capability to generate highly accurate code suggestions. However, their massive size poses challenges in terms of computational costs and environmental impact, hindering their widespread adoption in practical scenarios. Dynamic inference emerges as a promising solution, as it allocates minimal computation during inference while maintaining the model's performance. In this research, we explore dynamic inference within the context of code completion. Initially, we conducted an empirical investigation on GPT-2, focusing on the inference capabilities of intermediate layers for code completion. We found that 54.4% of tokens can be accurately generated using just the first layer, signifying significant computational savings potential. Moreover, despite using all layers, the model still fails to predict 14.5% of tokens correctly, and the subsequent completions continued from them are rarely considered helpful, with only a 4.2% Acceptance Rate. These findings motivate our exploration of dynamic inference in code completion and inspire us to enhance it with a decision-making mechanism that stops the generation of incorrect code. We thus propose a novel dynamic inference method specifically tailored for code completion models. This method aims not only to produce correct predictions with largely reduced computation but also to prevent incorrect predictions proactively. Our extensive evaluation shows that it can averagely skip 1.7 layers out of 16 layers in the models, leading to an 11.2% speedup with only a marginal 1.1 % reduction in ROUGE- L. © 2024 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Charging Dynamic Sensors through Online Learning 42

Charging Dynamic Sensors through Online Learning

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Sun, Yu Lin, Chi Yang, Wei Ren, Jiankang Wang, Lei Wu, Guowei Zhang, Qiang Dalian University of Technology School of Software Technology Dalian116024 China Xidian University The State Key Laboratory of Integrated Services Networks Xi'an710071 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116621 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

ISBN: (纸本)9798350334142

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes' short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term. © 2023 IEEE.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Generating Multimodal Metaphorical Features for Meme Understanding 24

Generating Multimodal Metaphorical Features for Meme Underst...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Xu, Bo Zheng, Junzhe He, Jiayuan Sun, Yuxuan Lin, Hongfei Zhao, Liang Xia, Feng School of Software Key Laboratory for Ubiquitous Network and Service Software of Liaoning Dalian University of Technology Liaoning Dalian China School of Computing Technologies Royal Melbourne Institute of Technology MelbourneVIC Australia School of Computer Science and Technology Dalian University of Technology Liaoning Dalian China School of Computing Technologies RMIT University MelbourneVIC Australia

ISBN: (纸本)9798400706868

Understanding a meme is a challenging task, due to the metaphorical information contained in the meme that requires intricate interpretation to grasp its intended meaning fully. In previous works, attempts have been made to facilitate computational understanding of memes through introducing human-annotated metaphors as extra input features into machine learning models. However, these approaches mainly focus on formulating linguistic representation of a metaphor (extracted from the texts appearing in memes), while ignoring the connection between the metaphor and corresponding visual features (e.g., objects in meme images). In this paper, we argue that a more comprehensive understanding of memes can only be achieved through a joint modelling of both visual and linguistic features of memes. To this end, we propose an approach to generate Multimodal Metaphorical feature for Meme Classification, named MMMC. MMMC derives visual characteristics from linguistic attributes of metaphorical concepts, which more effectively convey the underlying metaphorical concept, leveraging a text-conditioned generative adversarial network. The linguistic and visual features are then integrated into a set of multimodal metaphorical features for classification purpose. We perform extensive experiments on a benchmark metaphorical meme dataset, MET-Meme. Experimental results show that MMMC significantly outperforms existing baselines on the task of emotion classification and intention detection. Our code and dataset are available at https://***/liaolianfoka/MMMC. © 2024 ACM.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Observation points classifier ensemble for high-dimensional imbalanced classification

引用

CAAI Transactions on Intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

来源：评论

学校读者我要写书评

暂无评论

Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting 38

Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynami...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Jiawei Fan, Zexin Yang, Jian Xie, Jin PCA Lab VCIP College of Computer Science Nankai University China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding. Different from plane-based explicit representations, we decompose the 4D encoding into one spatial and three temporal 3D hash encodings without the low-rank assumption. Additionally, we design a novel attention module that generates the attention scores in a directional range to aggregate the spatial and temporal features. The directional attention enables Grid4D to more accurately fit the diverse deformations across distinct scene components based on the spatial encoded features. Moreover, to mitigate the inherent lack of smoothness in explicit representation methods, we introduce a smooth regularization term that keeps our model from the chaos of deformation prediction. Our experiments demonstrate that Grid4D significantly outperforms the state-of-the-art models in visual quality and rendering speed. Project page: https://***/Grid4D-web/. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Issue Title Generation: How Far Can Large Language Models Go?

Issue Title Generation: How Far Can Large Language Models Go...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Jun Liu, Shifan He, Qifan Xie, Songcheng Cui, Zhanqi School of Computer Science Beijing Information Science and Technology University Beijing100101 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing210016 China

ISBN: (纸本)9781665410205

In open-source software and platforms, developers utilize issues to record software failures or propose new features. The title of an issue, which is a mandatory field, should accurately describe the core content in a concise way. However, developers often face challenges in crafting high-quality issue titles due to insufficient experience or limited proficiency. As a result, researchers have proposed several methods for automatically generating issue titles, but typically relying on constructing large datasets to train models. Recently, Large Language Models (LLMs) have exhibited exceptional performance across a variety of general tasks, suggesting significant potential for issue title generation. Initial experiments indicate that the direct application of LLMs fails to yield satisfactory results. Therefore, we propose a method named LBITG (LLMs-Based Issue Title Generation). LBITG enhances the effectiveness of LLMs by providing contextual information through four types of prompts, which include example prompt and label prompt. These prompts serve as guidance for LLMs, thereby further improving their performance. Experimental results demonstrate that LBITG can significantly enhance the quality of issue titles generated by LLMs without any training. In the within-project scenario, LBITG achieves a minimum improvement of 111.29% in ROUGE, 104.54% in BLEU, and 188.48% in METEOR compared to iTAPE, and achieves performance comparable to that of the SOTA method iTiger. Moreover, LBITG demonstrates superior performance in the cross-project scenario, which outperforms iTiger by 25.33%, 30.14%, and 27.29% in terms of ROUGE-1, BLEU-1, and METEOR, respectively. © 2024 IEEE.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Vision Mamba Mender 38

Vision Mamba Mender

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Cao, Anda Feng, Zunlei Zhang, Shengxuming Wang, Yi Jia, Lingxiang Song, Mingli College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Video Anomaly Detection with Video Vision Transformer 8

Video Anomaly Detection with Video Vision Transformer

引用

8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Guo, Biao Liu, Mingrui He, Qian Jiang, Ming Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China

ISBN: (纸本)9798350397932

Video anomaly detection refers to the identification of unusual events or activities and represents irregular behavior. However, it is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams. Most of the existing methods rely on autoencoder, which fail to capture the global context information of the video. In this paper, we propose a new video anomaly detection method called VadVVT using Video Vision Transformer (ViViT). This model uses Transformer to automatically learn video representation and global contexts. It utilizes the transform encoder to extract spatio-temporal features from the video sequence and employs a decoder to predict the next frame. Our method models normality in the training phase and identifies an event with unpredictable as anomalies during the test phase. The experiments results on three public datasets demonstrate that our method outperforms existing methods. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：