检索结果-内蒙古大学图书馆

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

来源：评论

学校读者我要写书评

暂无评论

Enhancing Image Understanding in Automatic Captioning using Spatially-Aware Transformer Architectures

引用

Procedia computer science 2025年 258卷 2081-2090页

作者： Varun Prakash Saxena Malaram Kumhar Jitendra Bhatia Sudeep Tanwar Sudesh Rani Mayank Gupta Rushi H. Prajapati Department of Computer Engineering Govt. Women Engineering College Ajmer India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Punjab Engineering College (Deemed to be University) Chandigarh India Sahana Systems Limited Ahmedabad

Automatic captioning of images (ACI) is a sophisticated methodology combining image analysis and text generation, with the attention mechanism playing a critical role in identifying key image elements for elaboration. While transformer-based architectures have proven effective in text analysis and translation, their application to image captioning has been challenged by the structural disparity between image semantics typically identified by object detection models and sentence words. To bridge this gap, we introduce the Image Transformer, a novel model featuring a reformed encoding transformer tailored for spatial relationships among image regions and an implicit decoding transformer. This adaptation significantly enhances the standard transformer architecture, making it more suitable for image structures. Our model sets new state-of-the-art performance benchmarks on both online and offline MS COCO dataset testing platforms by utilizing regional features as inputs, representing a substantial advancement in ACI. Experimental results show that our spatially-aware transformer architecture achieved a BLEU-4 score of 38.4, a CIDEr score of 128.4, and a METEOR score of 27 on the MS COCO dataset, outperforming baseline methods significantly. Additionally, the model demonstrated robust performance with a 4.2% accuracy increase on the ImageNet dataset, validating its effectiveness across diverse scenarios. Its robust performance across diverse scenarios demonstrates its potential for broad application and substantial advancements in automatic image captioning.

关键词： Image captioning models Object Detection Real-Time Processing R-CNN Gated Linear Unit (GLU) Natural Language Processing (NLP) Artificial Intelligence (AI) Long Short Term Memory(LSTM)

来源：评论

学校读者我要写书评

暂无评论

Linear Convergent Distributed Nash Equilibrium Seeking With Compression

引用

IEEE Transactions on Automatic Control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

引用

18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Recommendation System Using Emotion Detection

Recommendation System Using Emotion Detection

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Susindra S Sweta B Keerthikha Shri R K. Brindha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems.

关键词： Industries Emotion recognition Technological innovation Sentiment analysis Decision making Machine learning User experience Real-time systems Internet of Things Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

引用

IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Integration of a Hybrid Model with XAI for Stock Price Forecasting: A Dashboard-Driven Approach

Integration of a Hybrid Model with XAI for Stock Price Forec...

引用

IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Jagannathan J Veera Venkata Shivamani Krishna Goditi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This paper on Integration of a Hybrid Model with XAI for Stock price Forecasting focuses on improving the accuracy and transparency of stock price predictions through a combined model of Long Short-Term Memory (LSTM) and MultiProphet, with an emphasis on explainable artificial intelligence (XAI). LSTM which is a deep learning model, is good at identifying patterns in time-series data. while MultiProphet which is an enhanced version of Facebook's Prophet model, effectively handles seasonal variations and anomalies in financial data. By integrating both models, the hybrid approach delivers more reliable stock forecasting. In addition, this project integrates XAI techniques like SHAP and LIME to offer greater insight into how the model generates predictions, which is crucial for informed financial decision-making. Dynamic dashboard provides an intuitive interface for users to track stock price trends, view forecasts, and access explanations of the model's predictions in real-time. This combination of improved prediction accuracy and model transparency enables better decision-making by stakeholders, who can base their actions on clear and interpretable information. Overall, the project represents a significant advancement in making advanced AI models more accessible and actionable in practical, user-oriented financial applications.

关键词： Analytical models Technological innovation Accuracy Explainable AI Decision making Predictive models Market research Data models Forecasting Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes 1st

Enhancing IoT Security Through Hierarchical Message-Passing ...

引用

1st International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Senthil Kumar, C. Vijay Anand, R. School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9783031808388

Internet of Things (IoT) gadgets connect and simplify life in several ways. The independence of these technologies poses some issues, such as the protection of security and privacy from malicious and compromised nodes inside the network. To overcome this complication, Enhancing IoT Security Through Hierarchical Message-Passing Graph Neural Networks: A Trust-Driven Strategy for Identifying Malicious Nodes (HMPGNN-Trust-IoT) is proposed. These issues are addressed by the proposed HMPGNN-Trust-IoT solution, which develops a mechanism for recognizing malicious and compromised nodes utilizing trust-based deep learning. With the help of the dataset provided, HMPGNN-Trust develops a worldwide model to predict the IoT node’s irrational behavior. A global dataset comprising 19 trust parameters on three main categories including knowledge, experience, and reputation are used in the proposed HMPGNN-Trust-IoT technique. The data are fed to pre-processing. In preprocessing, it cleans and normalizes the data in the dataset by utilizing a Nanoplasmonic Ultra Wideband Band Pass Filter (NUWBF). HMPGNN-Trust uses the idea of communities using dedicated servers to divide the dataset into smaller portions for effective training to lessen the computational strain. Finally, it classifies the nodes by utilizing a Hierarchical Message-Passing Graph Neural Network (HMPGNN) into benign and malicious nodes. The proposed HMPGNN-Trust-IoT is implemented using Python. To identify Trust Management in IOT performance metrics like precision, accuracy, and F1-score are considered. Performance of the HMPGNN-Trust-IoT approach attains 24.11%, 28.56%, and 22.73% high accuracy, 21.89%, 23.04%, and 9.51% high precision, 25.289%, 15.35%, and 19.91% higher F1-Score compared with existing methods such as the Securing IoT along Deep Federated Learning: A Trust-based Malicious Node Identification method (TM-ANN -IoT), the Trust-driven reinforcement selection technique in federated learning on Internet of Thin

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Fuzzy Routing Protocol for Energy Optimization in the Underwater Wireless Sensor Networks

引用

computers, Materials & Continua 2025年第5期83卷 1791-1820页

作者： Mehran Tarif Mohammadhossein Homaei Amir Mosavi Department of Computer Science University of VeronaVerona37134Italy Department of Computer Systems Engineering and Telematics University of ExtremaduraCaceres10003ExtremaduraSpain Institute of the Information Society Ludovika University of Public ServiceBudapest1083Hungary John Von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical UniversityKaraganda100000Kazakhstan

Underwater Wireless Sensor Networks(UWSNs)are gaining popularity because of their potential uses in oceanography,seismic activity monitoring,environmental preservation,and underwater ***,these networks are faced with challenges such as self-interference,long propagation delays,limited bandwidth,and changing network *** challenges are coped with by designing advanced routing *** this work,we present Under Water Fuzzy-Routing Protocol for Low power and Lossy networks(UWF-RPL),an enhanced fuzzy-based protocol that improves decision-making during path selection and traffic distribution over different network *** method extends RPL with the aid of fuzzy logic to optimize depth,energy,Received Signal Strength Indicator(RSSI)to Expected Transmission Count(ETX)ratio,and *** protocol outperforms other techniques in that it offersmore energy efficiency,better packet delivery,lowdelay,and no queue *** also exhibits better scalability and reliability in dynamic underwater networks,which is of very high importance in maintaining the network operations efficiency and the lifetime of UWSNs *** to other recent methods,it offers improved network convergence time(10%–23%),energy efficiency(15%),packet delivery(17%),and delay(24%).

关键词： Underwater sensor networks(UWSNs) routing energy fuzzy logic multipath load balancing

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Enhanced Object Detection for Humanoid Robots

Deep Learning-Based Enhanced Object Detection for Humanoid R...

引用

Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Akshit Negi Harshita Patel School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society.

关键词： Deep learning Analytical models Humanoid robots computer architecture Object detection Medical services Object recognition Security Computational intelligence Business

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：