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Uplink Performance of Cell-Free Symbiotic Radio With Hardware Impairments for IoT

IEEE Internet of Things Journal 2025年

作者： Sun, Qiang Zhou, Yu Shen, Yushi Li, Feiyang Chen, Xiaomin Li, Dong Zhang, Jiayi Nantong University School of Information Science and Technology Nantong226019 China NovNet Computing System Tech Company Beijing100081 China Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

Cell-free massive multiple-input multiple-output symbiotic radio (CF-SR) has recently been introduced as a promising solution for the Internet of Things (IoT), offering cost advantages and more uniform coverage performance for user devices. However, most previous studies assume perfect hardware, which is impractical in IoT systems. In this paper, we investigate the uplink performance of CF-SR systems in the presence of hardware impairments (HWIs). We adopt two novel schemes for hybrid combining, namely hybrid maximum ratio (HMR) and hybrid local minimum mean square error (HL-MMSE), both of which effectively enhance the spectral efficiency (SE) of the backscattering link. We derive closed-form expressions for the achievable SE of both conventional MR and HMR combining schemes, taking into account the imperfect channel state information (CSI) and HWIs. The simulation results that both the direct and backscattering links are primarily limited by HWIs including multiplicative and additive distortions from the device side. We apply the differential evolution (DE) algorithm for power control to maximize the minimum SE, and the results show that the DE algorithm improves the minimum SE by approximately 52%, avoiding further degradation of the SE of the weakest device due to the impact of HWIs. © 2014 IEEE.

关键词： Mean square error

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LibAMM: empirical insights into approximate computing for accelerating matrix multiplication 24

LibAMM: empirical insights into approximate computing for ac...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xianzhi Zeng Wenchao Jiang Shuhao Zhang National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China and Nanyang Technological University Singapore University of Technology and Design National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798331314385

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM's effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM's performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM's effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM —a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions.

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Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

arXiv

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arXiv 2024年

作者： Chu, Zhaoyang Wan, Yao Li, Qian Wu, Yang Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology China School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Big Data and Software Engineering Chongqing University China School of Computer Science and Engineering University of New South Wales Australia School of Computer Science University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers. © 2024, CC BY.

关键词： Graph neural networks

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Using Lock-Free Design for Throughput-Optimized Cache Eviction

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Proceedings of the ACM on Measurement and Analysis of Computing systems 2025年第2期9卷 1-28页

作者： Chao Dong Fang Wang Hong Jiang Dan Feng Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics) Engineering Research Center of data storage systems and Technology School of Computer Science and Technology) Ministry of Education of China Huazhong University of Science and Technology Wuhan Hubei China Department of Computer Science and Engineering University of Texas at Arlington Arlington Texas USA

In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in terms of concurrent throughput. However, existing cache eviction policies often struggle to provide adequate concurrent throughput due to their reliance on coarse-grained locking mechanisms and complex data structures. This paper presents a practical approach to cache eviction algorithm design, called Mobius, that optimizes the concurrent throughput of caches and reduces cache operation latency by utilizing lock-free data structures, while maintaining comparable hit ratios. Mobius includes two key designs. First, Mobius employs two lock-free FIFO queues to manage cache items, ensuring that all cache operations are executed efficiently in parallel. Second, Mobius integrates a consecutive detection mechanism that merges multiple modifications during eviction into a single operation, thereby reducing data races. Extensive evaluations using both synthetic and real-world workloads from high-concurrency clusters demonstrate that Mobius achieves a concurrent-throughput improvement ranging from 1.2× to 8.5× over state-of-the-art methods, while also maintaining lower latency and comparable cache hit ratios. The implementation of Mobius in CacheLib and RocksDB highlights its effectiveness in enhancing cache performance in practical scenarios.

关键词： cache eviction policy concurrency fifo queue lock-free throughput

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ENHANCING TWITTER BOT DETECTION VIA MULTIMODAL INVARIANT REPRESENTATIONS

arXiv

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arXiv 2024年

作者： Gong, Jibing Peng, Jiquan Qu, Jin Du, ShuYing Wang, Kaiyu School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China

Detecting Twitter Bots is crucial for maintaining the integrity of online discourse, safeguarding democratic processes, and preventing the spread of malicious propaganda. However, advanced Twitter Bots today often employ sophisticated feature manipulation and account farming techniques to blend seamlessly with genuine user interactions, posing significant challenges to existing detection models. In response to these challenges, this paper proposes a novel Twitter Bot Detection framework called BotSAI. This framework enhances the consistency of multimodal user features, accurately characterizing various modalities to distinguish between real users and bots. Specifically, the architecture integrates information from users, textual content, and heterogeneous network topologies, leveraging customized encoders to obtain comprehensive user feature representations. The heterogeneous network encoder efficiently aggregates information from neighboring nodes through oversampling techniques and local relationship transformers. Subsequently, a multi-channel representation mechanism maps user representations into invariant and specific subspaces, enhancing the feature vectors. Finally, a self-attention mechanism is introduced to integrate and refine the enhanced user representations, enabling efficient information interaction. Extensive experiments demonstrate that BotSAI outperforms existing state-of-the-art methods on two major Twitter Bot Detection benchmarks, exhibiting superior performance. Additionally, systematic experiments reveal the impact of different social relationships on detection accuracy, providing novel insights for the identification of social bots. © 2024, CC BY.

关键词： Tweets

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Distraction Detection in Driving Using Pose Extraction and Machine Learning Techniques 7

Distraction Detection in Driving Using Pose Extraction and M...

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7th International Conference on Information Technology, Information systems and Electrical engineering, ICITISEE 2023

作者： Bachtiar, Fitra Abdurrachman Arwani, Issa Prasetyo, Ridwan Aji Budi Pangestu, Gusti Soetedjo, Riza Setiawan Mahesa, Al Ravie Muthiar Brawijaya University Informatics Engineering Department Malang Indonesia Brawijaya University Information System Department Malang Indonesia Brawijaya University Psychology Department Malang Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350382266

There are a lot of technologies have been implemented in the modern cars these days, ranging from preventive braking system to driving assistance. It is believe that equipping car with modern technology would enhance safety. However, there are other aspects need to be considered in safety driving, that is human factor. Human factors are believed have high contribution in road accidents. Human factors such as emotional control and distraction may leads to dangerous driving behaviours. This study investigates the driver pose distraction. The distracted driver pose detected through pose detection using landmark feature extraction. The dataset used in this study is State Farm Distracted Driving Detection which is a secondary dataset. The extracted driver pose are in numeric form and used as the input for the classification model to detect ten kinds of driving distraction ranging from safety driving to talking to passenger. Four classifiers used to detect distracted driver are CatBoost, Decision Tress, Random Forest and SVM. The model performance evaluated using Precision, Recall, Accuracy, and F1-score. Furthermore, prediction time recorded to determine the best model to detect driver distraction. The results shows that CatBoost model outperformed all other model with F1-score and prediction time of 0.899 and 0.014 second. Random Forest model performance is the second highest compared to four other with F1-score and prediction time of 0.881 and 0.028 seconds. CatBoost model is the best model even though Cat Boost model execution time is not the highest and the model performance is not significantly different compared to Random Forest. © 2023 IEEE.

关键词： Extraction

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

Why Does Little Robustness Help? A Further Step Towards Unde...

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IEEE Symposium on Security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness

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Unlocking Security: A Comprehensive Exploration of Biometric Authentication Techniques

Unlocking Security: A Comprehensive Exploration of Biometric...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Jindam Sriman Puneet Thapar Arsalan Ahmed Alyas Urvashi Singh School of Computer Science Engineering Lovely Professional University Phagwara India Information System Technologies Wilmington University United States

Biometric authentication is a rapidly evolving field in information security, providing a personalized and reliable approach to identity verification. This paper explores various biometric methods, including fingerprints, iris scans, facial recognition, voice authentication, and behavioral biometrics, highlighting their strengths and vulnerabilities. Addressing challenges like spoofing attacks, multimodal integration, and privacy concerns, the paper emphasizes the need for robust security measures. The thorough analysis considers critical factors such as accuracy, user-friendliness, and vulnerability to fraudulent activities, providing a well-rounded assessment of their strengths and weaknesses. Besides, the review delves into the convergence of diverse biometric modalities and the evolving landscape of multimodal authentication systems, underscoring the pressing need for sustained research and advancement to address security challenges and privacy issues. The ultimate objective is to support user acceptance and instill confidence in biometric authentication. By critically appraising established techniques and contemplating potential future developments, this paper aims to provide profound insights for researchers and practitioners in the field of biometric authentication.

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Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography

Invertible Mosaic Image Hiding Network for Very Large Capaci...

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

作者： Zihan Chen Tianrui Liu Jun-Jie Huang Wentao Zhao Xing Bi Meng Wang College of Computer Science and Technology National University of Defense Technology China College of System Engineering National University of Defense Technology China School of Computer Science and Information Engineering Hefei University of Technology China

The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information among multiple images will become increasingly severe when the number of secret images becomes larger, thus restrict the development of very large capacity image steganography. In this paper, we propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes very large capacity image steganography with high quality by concealing a single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling (IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works for downscaling the single mosaic secret image form by spatially splicing the multiple secret images, and the IIH module then conceal this mosaic image under the cover image. The proposed InvMIHNet successfully conceal and reveal up to 16 secret images with a small number of parameters and memory consumption. Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms state-of-the-art methods in terms of both the imperceptibility of stego image, recover accuracy of secret image and security against steganlysis methods. The code is available at https://***/Brittany-Chen/InvMIHNet.

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

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

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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