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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yulin Wu Ruimin Hu Xiaochen Wang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Spatial audio content is becoming increasingly popular and is regarded as a set of object signals with associated metadata. The object-based content representation is independent of loudspeaker layouts and provides high spatial resolution when reproduced on more loudspeakers. The audio quality of the traditional spatial audio object coding (SAOC) method has severe aliasing distortion, which impairs the immersive listening experience. In this study, we reduce aliasing distortion by perceptual adaptive subband grouping strategy and use the convolutional neural network (CNN) and residual block to build the side information compressing model. Both objective and subjective experiments on benchmark datasets with different bitrates show that the proposed method achieves favorable performance against state-of-the-art methods.

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Federated Learning with Client Availability Budgets

Federated Learning with Client Availability Budgets

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yunkai Bao Steve Drew Xin Wang Jiayu Zhou Xiaoguang Niu Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Department of Geomatics Engineering University of Calgary Calgary AB Canada Department of Computer Science and Engineering Michigan State University East Lansing MI USA School of Computer Science Wuhan University Wuhan Hubei China

Federated learning (FL) sheds light on efficiently and privately learning from massive Internet of Things (IoT) devices. However, the iterative training and aggregation pose additional stress on the limited energy and availability budgets of clients. In this paper, we discuss two types of availability budgets of IoT clients, including the timing to start participating in FL and the communication budgets due to their constrained energy. We theoretically analyze the effect of availability budgets on FL, based on the availability constraints, by leveraging a decaying quadratic function to prioritize learning from statistically heterogeneous clients during the initial training rounds. We also consider the effects of client availability in terms of their participation to find a balance among clients with varying availability. We present FedCAB, an algorithm applying our theoretical model for the probabilistic rankings of the available clients to select in each round of FL model aggregation. Numerical results show the effectiveness of FedCAB under label distribution skew with a limited communication budget and clients that join the learning process in later rounds. We release the source code of FedCAB at https://***/denoslab/FedCAB.

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization

arXiv

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arXiv 2024年

作者： Wang, Yangdi Zhang, Zhi-Hai Xu, Su Xiu Guo, Wenming School of Management Beijing Institute of Technology Beijing100081 China Department of Industrial Engineering Tsinghua University Beijing100084 China School of Management Beijing Institute of Technology Beijing100081 China School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China

Overfitting commonly occurs when applying deep neural networks (DNNs) on small-scale datasets, where DNNs do not generalize well from existing data to unseen data. The main reason resulting in overfitting is that small-scale datasets cannot reflect the situations of the real world. Label smoothing (LS) is an effective regularization method to prevent overfitting, avoiding it by mixing one-hot labels with uniform label vectors. However, LS only focuses on labels while ignoring the distribution of existing data. In this paper, we introduce the distributionally robust optimization (DRO) to LS, achieving shift the existing data distribution flexibly to unseen domains when training DNNs. Specifically, we prove that the regularization of LS can be extended to a regularization term for the DNNs parameters when integrating DRO. The regularization term can be utilized to shift existing data to unseen domains and generate new data. Furthermore, we propose an approximate gradient-iteration label smoothing algorithm (GI-LS) to achieve the findings and train DNNs. We prove that the shift for the existing data does not influence the convergence of GI-LS. Since GI-LS incorporates a series of hyperparameters, we further consider using Bayesian optimization (BO) to find the relatively optimal combinations of these hyperparameters. Taking small-scale anomaly classification tasks as a case, we evaluate GI-LS, and the results clearly demonstrate its superior performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Fu, Xiya Xiong, Ling Li, Fagen Yang, Xingchun Xiong, Naixue School of Computer and Software Engineering Xihua University Chengdu China University of Electronic Science and Technology of China Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China College of Intelligence and Computing Tianjin University Tianjin China

Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of consumer electronics. However, traditional federated learning faces critical security and privacy challenges in this domain, including single-point attacks, inference attacks, and poisoning attacks. To address these pressing security concerns, this paper proposes a decentralized federated learning framework focusing on security, reliability, and efficiency, tailored to meet the sustainability demands of consumer electronics. The proposed framework is founded upon masking and secret sharing techniques, establishing an emphatic privacy-preserving federated learning framework that ensures the security of gradient data and robustness against participant dropouts. Additionally, we actively motivate high-quality participants to collaborate by incorporating an incentive mechanism. Building upon the enhancement of existing federated learning approaches reliant on masking techniques, the method outlined in this paper significantly reduces communication overhead while preserving accuracy. Empirical research results comprehensively substantiate the superiority of this approach. Furthermore, compared to prevalent blockchain-based federated learning methods, our approach makes noteworthy strides in accuracy and efficiency. IEEE

关键词： Blockchain

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SFR: Semantic-Aware Feature Rendering of Point Cloud

SFR: Semantic-Aware Feature Rendering of Point Cloud

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yaohua Zha Rongsheng Li Tao Dai Jianyu Xiong Xin Wang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Peng Cheng Laboratory Research Center of Artificial Intelligence

Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance in point cloud downstream tasks(e.g., classification and retrieval). These methods first require rendering the point cloud into 2D multi-view images. However, conventional methods only project the geometry of the point cloud, and such projections inevitably suffer from a loss of point cloud semantic information due to dimensionality reduction. We propose a semantic-aware and task-oriented differentiable feature rendering (SFR), which reduces the information loss during projection by generating rendered images with more point cloud semantic information for downstream tasks. Our SFR method can be applied as a plug-and-play module added to any multi-view-based backbone network for end-to-end training. Extensive experiments on benchmark datasets show that our SFR method reaches state-of-the-art performance and brings general improvements to point cloud classification and retrieval tasks.

关键词： Point cloud compression Training Geometry Three-dimensional displays Shape Semantics Signal processing

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Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

arXiv

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arXiv 2023年

作者： Yu, Chuer Zhang, Xuhong Duan, Yuxuan Yan, Senbo Wang, Zonghui Xiang, Yang Ji, Shouling Chen, Wenzhi The College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310027 China The school of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States The School of Software and Electrical Engineering Swinburne University of Technology HawthornVIC3122 Australia

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Convergence of 5G with Internet of Things for Enhanced Privacy

Convergence of 5G with Internet of Things for Enhanced Priva...

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Future of Information and Communication Conference, FICC 2021

作者： Batool, Amreen Sun, Baoshan Saleem, Ali Ali, Jawad School of Computer Science and Software Engineering Tiangong University Tianjin China Department of Computer Science COMSATS University Islamabad Lahore Pakistan Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9783030730994

In this paper, we address the issue of privacy in 5th generation (5G) driven Internet of Things (IoT) and related technologies while presenting a comparison with previous technologies for communication and unaddressed issues in 5G. Initially, an overview of 5G driven IoT is presented with details about both technologies and eventually leading to problems that 5th generation will face. Details about 5G are also presented while comparing them with previous technologies. The architecture of 5G is presented hence explaining layers of 5G and technologies like SDN, NFV and cloud computing that compose these layers. The architecture for 5g based IoT is also presented for providing visual understanding as well as explained based on how this addresses the issues present in 4G. Privacy is highlighted in 5G driven IoT while providing details about how SDN, NFV and cloud computing helps in elimination of this issue. The issues presented will be compared with 4G based IoT and solutions are provided about mitigation of these issues particularly bandwidth and security. Moreover, techniques used by 4G and 5G technologies for handling the issues of privacy in IoT are presented in a nutshell as a table. Paper also presents a detailed overview of technologies making 5G possible meanwhile giving an explanation about how these technologies resolve privacy issues in 5G. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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IRP2API: Automated Mapping of Cyber Security Incident Response Plan to Security Tools’ APIs

IRP2API: Automated Mapping of Cyber Security Incident Respon...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zarrin Tasnim Sworna Muhammad Ali Babar Anjitha Sreekumar School of Computer Science University of Adelaide Australia Cyber Security Cooperative Research Centre Australia Centre for Research on Engineering Software Technologies (CREST) University of Adelaide Australia

Security Operation Center (SOC) uses Incident Response Plan (IRP) to respond to security incidents by orchestrating diverse security tools’ activities in a Security Orchestration, Automation and Response (SOAR) platform. SOC teams manually dig through API documentation of security tools to find the appropriate APIs to define, update and execute an IRP, which hampers effective and efficient incident response. We propose a novel framework, namely IRP2API, for automated mapping of IRP to diverse security tools’ APIs. IRP2API enables SOC teams to effectively and efficiently execute IRP tasks, whilst significantly reducing the required human effort. IRP2API is a unified framework for diverse security tools using an unsupervised transfer learning approach based on API documentation. IRP2API alleviates the requirement of expert knowledge, expensive manually labeled data and access to the code repository. IRP2API achieves suitable semantic coverage by leveraging different semantic variation enrichment methods to deal with the semantic variation of IRP and API data. To demonstrate the real world viability of IRP2API, we experimentally evaluate its effectiveness and efficiency using IRPs of a real world SOAR platform, 6 security tools and 4 transfer learning-based pre-trained embedding approaches. IRP2API achieves 91.1% Top-15 Accuracy and mean reciprocal rank@15 of 57.4 for automated IRP to API mapping, which is 41.6% and 81.6% improved compared to the best results across all non-transfer learning-based baselines. It indicates its effectiveness to support a SOC team. IRP2API requires only 0.8 sec per IRP task to map suitable API that reflects its real world applicability in time-critical SOC.

关键词： Codes Semantics Transfer learning Supervised learning Documentation software Security

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A Multi-objective Salp Swarm Algorithm for Multi-product Two-sided Disassembly Line Balancing Problem with Special Workers

A Multi-objective Salp Swarm Algorithm for Multi-product Two...

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Chinese Control and Decision Conference, CCDC

作者： Xinyu Zhu Shujin Qin XiWang Guo Jiacun Wang Qi Zhang Jiawei Li School of Economics and Management Shangqiu Normal University Shangqiu China Liaoning Petrochemical University Monmouth University West Long Branch USA Computer Science and Software Engineering Monmouth University West Long Branch USA Information Science and Engineering Shenyang University of Chemical Technology Shenyang China Computer and Communication Engineering Liaoning Petrochemical University Fushun China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The escalating volume of discarded large-scale electronic products has triggered significant attention within the research community toward the TDLBP (Two-sided Disassembly Line Balancing Problem. In response, this study presents a biobjective optimization problem for TDLBP, incorporating the considerations of hiring special WGB (Workers with Government Benefits) workers. The objectives are two-fold: one is to maximize disassembly profit, and the other is to concurrently enhance the WGB index. The resulting problem is named a Bi-objective Multi-product Two-sided Disassembly-line-balancingproblem considering Workers-with-government-benefits. MSSA (Multi-objective Salp Swarm Algorithm) is proposed to tackle the problem. The effectiveness of the proposed algorithm is validated through comparison with NSGA-III and MOEA/DCRA.

关键词： Heuristic algorithms Government Mathematical models Encoding Decoding Indexes Optimization

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