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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Jiang, Changkun Shenzhen University College of Computer Science and Software Engineering China

ISBN: (纸本)9798350334142

Mobile edge caching (MEC) allows edge devices to cache popular contents and deliver them to end-users directly, which can effectively alleviate increasing backbone loads and improve end-users' quality of service. Peer content sharing (PCS) enables edge devices to share the cached contents with others, and can further increase the caching efficiency. While many efforts have been made to content caching or sharing technologies, it remains open to study the complicated technical and economic interplay between both technologies. In this paper, we propose a joint MEC-PCS framework, and focus on capturing the strategic interactions between different types of edge devices. Specifically, we model their interactions as a non-cooperative game, where each device (player) can choose to be an agent (who caches and shares contents with others) or a requester (who doesn't cache but requests contents from others) of each content. We analyze the existence and uniqueness of the game equilibrium systematically under the generic usage-based pricing (for PCS). To address the incomplete information issue, we further design a behavior rule that allows players to achieve the equilibrium via a dynamic learning algorithm. Simulations show that the joint MEC-PCS framework can reduce the total system cost by 60%, compared with the benchmark pure caching system without PCS. © 2023 IEEE.

关键词： Economic analysis

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DualGuard: Obfuscated Federated Learning with Two-Party Secure Robust Aggregation

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IEEE Internet of Things Journal 2025年第11期12卷 16595-16609页

作者： Pan, Hao Bao, Haiyong Guan, Menghong Li, Zhiqiang Huang, Cheng Dai, Hong-Ning East China Normal University Software Engineering Institute Shanghai200062 China Fudan University School of Computer Science Shanghai200438 China Hong Kong Baptist University Department of Computer Science Hong Kong

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, data privacy leakage and Byzantine clients are common challenges in the FL aggregation phase. While extensive research has been conducted to explore defenses for these risks independently, there is a notable lack of scholarly work on integrated defense strategies to address both challenges simultaneously. To bridge this gap, we propose a novel Two-Party Secure Robust Aggregation (TPSRA) framework. The critical insight of TPSRA is to couple client-side gradient obfuscation with server-side secure two-party computation to achieve robust and private FL aggregation. Specifically, clients obfuscate and split local gradients using matrix theory, while servers utilize a novel secure multi-party computation protocol based on mutually orthogonal matrices to preserve the privacy of local gradients. Additionally, TPSRA designs and integrates state-of-the-art robust aggregation algorithms into compatible sub-protocols, enabling efficient parallel computation. This establishes a highly efficient and versatile secure robust aggregation framework for FL. Experiments demonstrate that our TPSRA framework not only effectively resists gradient leakage attacks and detects malicious gradients, but also exhibits superior computational and communication efficiency. We also prove theoretically that TPSRA is secure under the semi-honest adversary model. © 2014 IEEE.

关键词： Federated learning

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GCPNet: Global-Context Pyramid Network for Infrared Small Target Detection 3

GCPNet: Global-Context Pyramid Network for Infrared Small Ta...

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3rd International Conference on Electronic Information engineering and computer, EIECT 2023

作者： Ling, Siyao Wu, Yujie Zhang, Jian Li, Zhaoyan Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350357707

The detection of infrared weak small targets often presents substantial challenges due to their small size, remote distance, and low contrast, exacerbated by external factors such as atmospheric interference. While data-driven methods based on Convolutional Neural Networks (CNN) have significantly improved detection performance, existing networks remain susceptible to background noise and interference, especially within complex backgrounds. The absence of effective feature fusion also suggests that targets may get lost in deeper network layers, making the direct application to infrared small target detection challenging. To address these issues, we propose a multi-scale infrared small target detection network named Global-Context Pyramid Network (GCPNet). GCPNet employs a Pooling Dual-Route Feature Fusion Module (PRFM), effectively promoting the interactive fusion of multi-layer features, and introduces a Guided Attention Module (GAM), substantially enhancing the discriminatory ability for high-value target features. Experimental results on the SIRSTAUG and MDvsFAcGan datasets demonstrate that our GCPNet performs remarkably well, notably outperforming existing methods under complex background and weak texture conditions. © 2023 IEEE.

关键词： Network layers

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An Appointed Access Control Scheme for Distributed-Cloud Environment

An Appointed Access Control Scheme for Distributed-Cloud Env...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Zhou, Zheng Ling, Xiong Xu, Feng Xianhua, Niu Peng, Chen School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9781665494274

With the development of cloud computing technology, single clouding services have not been able to cope adequately with the online services boom, distributed-cloud is satisfied with the demand. The access control technology among the distributed-cloud environment has become an indispensable threshold to ensure the security of cloud services. However, the current access control schemes are not appointed, i.e., once a user gets the credential, he/she has the capacity to access all services in the distributed-cloud environment. This is conflict with the practical application usually restricted by the role of the user. To overcome this weakness, this work designs an appointed access control scheme using signcryption for distributed-cloud environment, which provides mutual authentication, hierarchical access control, appointed access, etc. Finally, we compare with the existing scheme, and the results show that our scheme has the best computational efficiency and communication efficiency. Therefore, our scheme is more suitable for complex and diverse distributed-cloud environment. © 2023 IEEE.

关键词： Distributed cloud

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MANIN: Multi-Attention Neural Interaction Networks for Social Rumor Detection 6

MANIN: Multi-Attention Neural Interaction Networks for Socia...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Yuan, Xue Sun, Jiehu Wu, Yue Institute of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9781665491259

With the fast-changing development of emerging online media, the accurate detection and timely suppression of rumors in online social networks becomes a vital task for maintaining social security and purifying public networks. Most existing works rely only on monotonous text content and shallow semantic information, and there is a lack of critical attention and mining for potential user relationships formed in the process of rumor spreading. To better improve these problems by employing attention mechanisms, we propose a novel deep model, Multi-Attention Neural Interaction Networks, termed as MANIN, which mainly consists of a self-attention-based encoder for extracting rich semantic representations from texts, a post-comment co-attention mechanism for studying the interaction between the original post and its comments, and a graph attention neural network for mining potential user interactions. We have conducted numerous experiments on real-world datasets and verified their validity, and the results show that the model proposed by us outperforms the baselines with an accuracy rate of 81.9% and can detect rumors at early stages. © 2023 IEEE.

关键词： Social networking (online)

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Lightwight Image Super-Resolution: The Shift Ladder Transformer Approach 2

Lightwight Image Super-Resolution: The Shift Ladder Transfor...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Jian Wu, Yujie Ma, Jingbin Ling, Siyao Li, Zhaoyan School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350331417

In lightweight Super Resolution (SR) tasks, balancing performance and efficiency is a challenge for CNN-based models. The Transformer architecture has been introduced into super-resolution tasks due to its global self-attention mechanism. However, existing lightweight image super-resolution networks based on Transformers typically employ localized attention to sparsify their attention, thereby limiting their receptive field and uniformity of pixel weight distribution. To address these challenges, this paper introduces the Shift Ladder Transformer for Super Resolution (slTSR), a lightweight model that melds lightweight convolutional layers with a distinct residual Shift Ladder Transformer block. With the integration of the Shift Ladder Transformer Layer (SLTL) strategy, slTSR effectively amalgamates information from diverse spatial windows, enhancing information exchange. Experiments on diverse datasets consistently highlight the exceptional performance and efficiency of slTSR. © 2023 IEEE.

关键词： Lightweight image super-resolution Localized attention Shift Ladder Transformer

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Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network 21

Multi-Agent Graph Reinforcement Learning based On-Demand Wir...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhao, Ze Yu Che, Yueling Luo, Sheng Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9783903176553

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices’ own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs’ trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs’ self-attentions for their collaborative WET, we propose the multi-agent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices’ energy requirements. © 2023 IFIP.

关键词： Reinforcement learning

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Water Consumption Dynamics in Izmir: Analyzing Influences of District, Seasonality, and External Events

Water Consumption Dynamics in Izmir: Analyzing Influences of...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Arslan, Ahmet Titiz, Imren Ece Gürcan, Ege Can Demir, Alper Software Engineering Izmir University of Economics Izmir Turkey Computer Engineering Izmir University of Economics Izmir Turkey

ISBN: (纸本)9798350379433

This study investigates the patterns of water consumption in Izmir from January 2015 to January 2024, using a dataset provided by Izmir's Open Data Portal. The research aims to understand how different factors such as district, seasons, and external events like the COVID-19 pandemic influence water usage across the city. By employing statistical analysis and machine learning models including Decision Trees and K-means, this project identifies significant spatial and temporal variations in water consumption. This analysis not only aids in the efficient management of water resources but also serves as a foundation for future predictive modeling and sustainability efforts in urban settings. © 2024 IEEE.

关键词： COVID-19 data science Izmir urban sustainability water consumption

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A Systematic Review of Complexity in Agile software Project Delivery 34

A Systematic Review of Complexity in Agile Software Project ...

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34th Australasian Conference on Information Systems, ACIS 2023

作者： Vaipulu, Taniela Lal, Ramesh Thorpe, Stephen J. Project Management Research Office Software Engineering Research Centre Department of Computer Science and Software Engineering School of Engineering Computer and Mathematical Sciences Auckland University of Technology New Zealand

In Agile software delivery, complexity factors have become an important part that has been linked to the success of software projects, especially with highly structured plan-driven approaches. Although there are a range of known techniques, practices and structures aimed at improving communication, team, and organisational cohesion, there remains a poor understanding of how organisations and teams can effectively deal with complexity in Agile software delivery. While theoretical and management frameworks of complexity exist, nevertheless, little is known about the important complexity factors at play in the planning, design, and creation of Agile software at the various levels of organisational structures. To better understand complexity and how it may impact Agile delivery across organisational structures, processes and roles, a systematic literature review was undertaken to identify the known complexity factors, to identify gaps in our knowledge, and to identify the approaches that have been undertaken to date to address complexity. This review contributes a fresh perspective to the literature on complexity in Agile software delivery and forwards our conversation on complexity in IT projects, and their effective management. The review was structured around Remington and Pollack’s (2016) model identifying different types of complexity. The review’s findings indicate that complexities result from aspects in functional teams, organizational structures, roles, Agile tasks, and practice-related issues that were identified in distributed Agile projects. We conclude that there may remain a lack of understanding in practice of the complexity factors that are at play in an Agile software delivery context. These findings are relevant to researchers and practitioners seeking to identify areas of focus for addressing complexity in Agile software delivery. Copyright © 2023 Taniela Ramesh Stephen.

关键词： software design

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