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Communication and Networking: international conference, FGCN 2011, Held as Part of the Future Generation information Technology conference, FGIT 2011, ... in computer and information science)

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2012年

作者： Taihoon Hoonn Kim Hojjat Adeli Waichi Fang Thanos Vasilakos Adrian Stoica Charalampos Z Patrikakis Gansen Zhao Javier Garcia Villalba Yang Xiao

ISBN: (纸本)9783642272004

The two volume set, CCIS 265 and 266, constitutes the refereed proceedings of the international conference, FGCN 2011, held as Part of the Future Generation information Technology conference, FGIT 2011, Jeju Island, Korea, in December 2011. The papers presented were carefully reviewed and selected from numerous submissions and focuse on the various aspects of future generation communication and networking.

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Spatial Frequency-Aware Self-Distillation for Weakly-Supervised Semantic Segmentation

Spatial Frequency-Aware Self-Distillation for Weakly-Supervi...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Fang, Jingyuan Ning, Yang Nie, Xiushan School of Computer Science and Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368741

Weakly-supervised semantic segmentation (WSSS) aims to achieve pixel-level classification under image-level supervision. Recent class activation map (CAM)-based methods seek to expand foreground activation while suppressing background. However, they often overlook the uncertainty of CAM, where non-salient activation in some regions complicates semantic classification. These regions are typically dismissed as noise, resulting in inappropriate activations due to inadequate regularization. To resolve this, we introduce a Spatial Frequency-Aware Self-Distillation strategy (SFS). Firstly, to enhance the perception of high-frequency spatial information in uncertain regions, we propose a boundary self-distillation and uncertain region reconstruction strategy, which captures high-frequency boundary information and fine-grained spatial context in these regions. Secondly, to enhance the discrimination of low-frequency semantic features, we propose a contrastive attention mechanism that guides the Vision Transformer (ViT) to focus more on the foreground, thereby improving the distinction between foreground and background. Finally, our SFS demonstrates outstanding performance on both the VOC 2012 and COCO 2014 datasets, attributed to its superior spatial frequency perception capabilities. The code is available at https://***/fjoybest/SFS. © 2025 IEEE.

关键词： self-distillation spatial frequency awareness weakly-supervised semantic segmentation

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Evaluation of Novel Approaches to Software Engineering: 5th international conference, ENASE 2010, Athens, Greece, July 22-24, 2010, Revised Selected ... in computer and information science)

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2012年

作者： Leszek A Maciaszek Pericles Loucopoulos

ISBN: (纸本)9783642233906

This book contains a collection of thoroughly refereed papers presented at the 5th international conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2010, held in Athens, Greece, in July 2010. The 19 revised and extended full papers were carefully selected from 70 submissions. They cover a wide range of topics, such as quality and metrics; service and Web engineering; process engineering; patterns, reuse and open source; process improvement; aspect-oriented engineering; and requirements engineering.

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th international conference on Computational Methods in science and Technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Productivity convergence and the role of innovation in productivity convergence: The case of processing and manufacturing enterprises in Vietnam 6

Productivity convergence and the role of innovation in produ...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Thanh Nam, Ninh Khac Ngoc Luu, Thi Minh Hung, Ta Huy Mai Huong, Nguyen Thi University of Social Sciences and Humanitites Vietnam National University Hanoi Viet Nam Vietnam Academy of Sciences and Technology 18 Hoang Quoc Viet Ha Noi Cau Giay Viet Nam International School Vietnam National University Hanoi Viet Nam Institute of Geological Sciences Viet Nam Academy of Science and Technology Viet Nam

The study utilizes enterprise survey data from the General Statistics Office (GSO) spanning 2012 to 2020 to measure productivity convergence and assess the impact of innovation on this convergence within Vietnam's processing and manufacturing sectors. By applying the semi-parametric estimation method proposed by Levinsohn and Petrin, the study first calculates Total Factor Productivity (TFP) and then uses regression estimates to analyze unconditional (absolute) convergence, represented as β. Finally, the study evaluates how innovation influences productivity convergence. The research findings reveal that TFP's contribution to output growth tends to increase. Productivity convergence in the manufacturing and processing industry is notably rapid, with low-productivity enterprises catching up 38.8% faster than their high-productivity counterparts, and a half-life index of merely 1.8 years. Furthermore, innovation is found to have a positive impact on productivity convergence. These results provide critical empirical evidence for business managers and policymakers to formulate effective strategies and policies that foster innovation in Vietnam's processing and manufacturing enterprises. © 2025 The Authors.

关键词： Smart manufacturing

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Characterizing the Influence of Topology on Graph Learning Tasks 29th

Characterizing the Influence of Topology on Graph Learning T...

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29th international conference on Database Systems for Advanced Applications (DASFAA)

作者： Wu, Kailong Xie, Yule Ding, Jiaxin Ren, Yuxiang Fu, Luoyi Wang, Xinbing Zhou, Chenghu Shanghai Jiao Tong Univ Shenzhen Shenzhen Guangdong Peoples R China Huawei Technol Shenzhen 2012 Labs Shenzhen Guangdong Peoples R China

ISBN: (纸本)9789819757787;9789819757794

Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the fundamental problem of understanding and analyzing how graph topology influences the performance of learning models on downstream tasks has not yet been well understood. In this paper, we propose a metric, TopoInf, which characterizes the influence of graph topology by measuring the level of compatibility between the topological information of graph data and downstream task objectives. We provide analysis based on the decoupled GNNs on the contextual stochastic block model to demonstrate the effectiveness of the metric. Through extensive experiments, we demonstrate that TopoInf is an effective metric for measuring topological influence on corresponding tasks and can be further leveraged to enhance graph learning.

关键词： GNNs graph topology graph rewiring

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Integrated Non-Terrestrial and Terrestrial Quantum Anonymous Networks

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IEEE Network 2025年第3期39卷 196-206页

作者： Khan, Awais Bui, Tinh Thanh Rehman, Junaid Ur Chatzinotas, Symeon Cotton, Simon L. Dobre, Octavia A. Duong, Trung Q. Shin, Hyundong Kyung Hee University Department of Electronics and Information Convergence Engineering Gyeonggi-do Yongin-si17104 Korea Republic of King Fahd University of Petroleum and Minerals Department of Electrical Engineering Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada Queen's University Belfast Department of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Due to optical fiber limitations for quantum communication, global-scale quantum networks are possible only by integrating non-terrestrial components in the overall network architecture. Quantum networks are expected to support distributed tasks for quantum information processing, such as quantum sensing, control, communication, and computing. These networks enable emerging applications that are uniquely quantum or augmented by quantum mechanics. Leveraging quantum resources, anonymous networking can be enhanced to such an extent that even an adversary with control over all network resources cannot trace the message source, achieving perfect untraceability. To provide this untraceable global connectivity, we demonstrate the integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) for quantum anonymous communication (QAC), highlighting possible architectures and key challenges in these integrated NTN-TN quantum anonymous networks (QANs). To illustrate and benchmark the design of QAC protocols within integrated networks, we present essential quantum protocols such as anonymous conference key agreement (CKA) and anonymous broadcast. In the first case study, we propose a satellite-to-ground quantum anonymous CKA (QA-CKA) protocol and assess the anonymous key exchange rate. This QA-CKA protocol utilizes low Earth orbit satellites to generate anonymous keys among two distinct TN nodes. In the second case study, we develop an air-to-ground quantum anonymous broadcast (QAB) protocol and examine the anonymous broadcast announcement rate. This QAB protocol exploits unmanned aerial vehicles to enable a broadcasting party to transmit classical information anonymously across two distinct TNs. These QAC protocols are simulated with realistic integrated network parameters to provide practical estimates of achievable performance. Furthermore, we discuss future research directions for enabling integrated NTN-TN QANs. © 1986-2012 IEEE.

关键词： Broadcasting antennas

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A Weakly Supervised Semantic Segmentation Model with Enhanced CLIP Feature Extraction

A Weakly Supervised Semantic Segmentation Model with Enhance...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Kong, Fanxuan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9798350368741

This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model's image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve the performance of the Weakly Supervised Semantic Segmentation (WSSS) task. WSSS-ECFE employs the Enhanced Bottleneck module proposed in this paper and adds dynamic residual connection to improve the model's processing effect on complex scenes. In terms of implementation, the Enhanced Bottleneck module employs the Swish activation function and the Depthwise Separable Convolution to enhance the feature extraction and segmentation capability of the model, and uses multiple attention mechanisms to further optimize the feature representation and segmentation accuracy. The WSSS task on the public datasets PASCAL VOC 2012 and MS COCO 2014 achieves 82.6% and 56.3% mean intersection over union (mIoU), achieving state-of-the-art performance in models with low resource requirements. © 2025 IEEE.

关键词： Attention Mechanism Bottleneck Feature Extraction Residual Connection Weakly Supervised Semantic Segmentation (WSSS)

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Hybrid Scalable Video Coding with Neural Compression and Enhancement for Streaming Media 31st

Hybrid Scalable Video Coding with Neural Compression and E...

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31st international conference on Multimedia Modeling, MMM 2025

作者： Ye, Yuyao Yang, Jiayu Zhao, Yang Gao, Mengping Cao, Hongbin Wang, Ronggang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China School of Computer and Information Hefei University of Technology Hefei China Bytedance Inc. Shenzhen China

ISBN: (纸本)9789819620609

Streaming media is important in modern information consumption industry. However the limited and varied computing resources and bandwidth of client devices pose challenges for video coding. To make a balance between speed and coding efficiency and provide scalability for different devices, we propose a hybrid scalable coding framework (HSVC) which maintains a layered structure including a low resolution basement layer encoded by traditional coding methods and an optional enhancement layer encoded by neural coding methods. The enhancement layer coding is designed with a neural contextual compression module and a neural motion vector-guided enhancement module, which make full use of inter-layer information to boost the performance of enhancement layer and produce better visual quality. Experiments demonstrate that the proposed method outperforms existing scalable coding methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalable video coding

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DRANet: Dual-threshold Guided Reliability Aware Network for Semi-Supervised Image Semantic Segmentation

DRANet: Dual-threshold Guided Reliability Aware Network for ...

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2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Qiu, Lizhe Zhang, Mingyang Zheng, Yuan College of Computer Science Inner Mongolia University Huhhot China

ISBN: (纸本)9798350368741

Different from supervised semantic segmentation task, semi-supervised semantic segmentation (SSSS) aims to alleviate the burden of time-consuming pixel-wise manual labeling. Although existing methods have achieved the promising performance with a small amount of labeled images, they still suffer from an insufficient mining on a large amount of unlabeled images due to simple evaluation way for them. To address this issue, we propose a novel Dual-threshold Guided Reliability Aware Network (DRANet) for SSSS task. To comprehensively evaluate the reliability of unlabeled images, we provide a dual-threshold guided augmentation module (DGAM), which not only introduces two predefined thresholds to group unlabeled images with different confidences according to their learning status, but also performs different data augmentations on unlabeled images based on their evaluation results. In particular, for an unlabeled image with higher confidence, a double-stream augmentation strategy is adopted, leading to a more sufficient learning for it. Experiments on Cityscapes and Pascal VOC 2012 datasets confirm the superiority of our DRANet over existing methods. Code is available at https://***/ZY-IMUCV/DRANet_QLZ_2024. © 2025 IEEE.

关键词： data augmentation Semantic segmentation semi-supervised learning teacher-student model

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