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Side-Channel Leakage Analysis of Inner Product Masking

computers, Materials & Continua 2024年第4期79卷 1245-1262页

作者： Yuyuan Li Lang Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

The Inner Product Masking(IPM)scheme has been shown to provide higher theoretical security guarantees than the BooleanMasking(BM).This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of *** previous work unfolds when certain(adversarial and implementation)conditions are met,and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected *** this paper,we investigate the security characteristics of IPM under different *** adversarial condition,the security properties of first-order IPMs obtained through parametric characterization are preserved in the face of univariate and bivariate *** implementation condition,we construct two new polynomial leakage functions to observe the nonlinear leakage of the IPM and connect the security order amplification to the nonlinear *** observe that the security of IPMis affected by the degree and the linear component in the leakage *** addition,the comparison experiments from the coefficients,signal-to-noise ratio(SNR)and the public parameter show that the security properties of the IPM are highly implementation-dependent.

关键词： Side-channel analysis inner product masking mutual information nonlinear leakage

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Design of Microstrip Type Broadband Directional Coupler

Design of Microstrip Type Broadband Directional Coupler

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Yan, Hang Wu, Xianliang Wang, Ruikang Wang, Gang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781733509657

A microstrip line broadband directional coupler is designed based on microstrip line theory. The bandwidth of the coupler is extended by using a cascade of multiple coupled lines. The isolation of the directional coupler is greatly improved by covering the microstrip line with a dielectric layer [1] and by loading the branching compensation capacitor to compensate for the odd-even mode phase velocity difference. Simulation results of ANSYS HFSS 2019 show that the return loss (S11) is greater than 20 dB, the insertion loss (S21) is less than 1.7 dB, the coupling degree (S31) is about -6dB(±0.8dB), the isolation degree (S41) is greater than 23.4dB, and the minimum directionality is greater than 17dB during 700 MHz-3700 MHz. The physical size of coupler is 18 mm × 60mm, which meets the design requirements of 6dB coupler. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Directional couplers

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Searching for Best Practices in Retrieval-Augmented Generation

Searching for Best Practices in Retrieval-Augmented Generati...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Wang, Xiaohua Wang, Zhenghua Gao, Xuan Zhang, Feiran Wu, Yixin Xu, Zhibo Shi, Tianyuan Wang, Zhengyuan Li, Shizheng Qian, Qi Yin, Ruicheng Lv, Changze Zheng, Xiaoqing Huang, Xuanjing School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798891761643

Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a "retrieval as generation" strategy. Code and resources are available at https://***/FudanDNN-NLP/RAG. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Joint Optimization of Maximum Achievable Rate in SWIPT Systems Assisted by Active STAR-RIS 18th

Joint Optimization of Maximum Achievable Rate in SWIPT Sys...

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18th International Conference on Wireless Artificial intelligent Computing Systems and Applications, WASA 2024

作者： Yang, Junlong Qin, Xizhong Jia, Zhenhong Mao, Lamu School of Computer Science and Technology XJU Xinjiang China Xinjiang Key Laboratory of Signal Detection and Processing Wulumuqi China

ISBN: (纸本)9783031714665

This investigation focuses on a simultaneous wireless information and power transfer (SWIPT) system, significantly enhanced by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). It extends the degrees of freedom (DoF) for the system and addresses the "double fading" challenge seen in passive RIS frameworks. Our objective is the maximization of the sum achievable rate (SAR), achieved by optimizing the transmit beamforming vector at the base station (BS) and the coefficient and amplification matrices at the aSTAR-RIS. Through an alternating optimization (AO) strategy and fractional programming (FP), a sub-optimal solution is obtained. Simulations demonstrate that our aSTAR-RIS approach results in a 16.5% increase in performance over conventional active RIS configurations and a significant 114% improvement over passive STAR-RIS schemes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Inductive power transmission

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A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

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computers, Materials & Continua 2024年第1期78卷 197-225页

作者： Xiaozhong Yu Jinhua Zheng College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

In rice production,the prevention and management of pests and diseases have always received special *** methods require human experts,which is costly and *** to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is ***,deep learning technology has been employed to detect and identify rice diseases and *** paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing ***,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and *** study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

关键词： Deep learning rice diseases and pests image recognition object detection

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SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

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computers, Materials & Continua 2024年第6期79卷 4641-4657页

作者： Jiahui Liu Lang Li Di Li Yu Ou College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys ***,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within *** challenges significantly hinder key recovery *** paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these ***-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained ***,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each ***,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct ***,an intelligent search method is proposed to identify optimal parameters,further improving attack *** were conducted on both simulated environments and real power traces collected from the SAKURA-G *** the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

关键词： Side-channel analysis correlation power analysis genetic algorithm crossover mutation

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InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance

InferAligner: Inference-Time Alignment for Harmlessness thro...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Wang, Pengyu Zhang, Dong Li, Linyang Tan, Chenkun Wang, Xinghao Zhang, Mozhi Ren, Ke Jiang, Botian Qiu, Xipeng School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Fudan University

ISBN: (纸本)9798891761643

As large language models (LLMs) rapidly evolve, they are increasingly being customized through fine-tuning to suit the specific needs of various applications. A critical aspect of this advancement is the alignment process, which ensures that these models perform tasks in ways that align with human values and expectations. Current alignment methods, such as direct preference optimization (DPO) and reinforcement learning from human feedback (RLHF), focus primarily on alignment during training phase. However, these methods often involve complex and resource-intensive training processes, posing significant challenge for their implementation. Therefore, we propose InferAligner, a simple yet effective method for harmlessness alignment during inference phase. InferAligner decouples harmlessness from helpfulness. During the training phase, it focuses solely on enhancing the target model's capabilities on downstream tasks. In the inference phase, it utilizes safety steering vectors extracted from the aligned model to guide the target model towards harmlessness alignment. Experimental results show that our method can be very effectively applied to domain-specific models in finance, medicine, and mathematics, as well as to multimodal large language models (MLLMs) such as LLaVA. It significantly diminishes the attack success rate (ASR) of both harmful instructions and jailbreak instructions, while maintaining almost unchanged performance in downstream tasks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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SPEECHTOKENIZER: UNIFIED SPEECH TOKENIZER FOR SPEECH LANGUAGE MODELS 12

SPEECHTOKENIZER: UNIFIED SPEECH TOKENIZER FOR SPEECH LANGUAG...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Xin Zhang, Dong Li, Shimin Zhou, Yaqian Qiu, Xipeng School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Fudan University

Current speech large language models build upon discrete speech representations, which can be categorized into semantic tokens and acoustic tokens. However, existing speech tokens are not specifically designed for speech language modeling. To assess the suitability of speech tokens for building speech language models, we established the first benchmark, SLMTokBench. Our results indicate that neither semantic nor acoustic tokens are ideal for this purpose. Therefore, we propose SpeechTokenizer, a unified speech tokenizer for speech large language models. SpeechTokenizer adopts the Encoder-Decoder architecture with residual vector quantization (RVQ). Unifying semantic and acoustic tokens, SpeechTokenizer disentangles different aspects of speech information hierarchically across different RVQ layers. Furthermore, We construct a Unified Speech Language Model (USLM) leveraging SpeechTokenizer. Experiments show that SpeechTokenizer performs comparably to EnCodec in speech reconstruction and demonstrates strong performance on the SLMTokBench benchmark. Also, USLM outperforms VALL-E in zero-shot Text-to-Speech tasks. Code and models are available at https://***/ZhangXInFD/SpeechTokenizer/. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Semantics

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A Medical Data-Effective Learning Benchmark for Highly Efficient Pre-training of Foundation Models 24

A Medical Data-Effective Learning Benchmark for Highly Effic...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yang, Wenxuan Tan, Weimin Sun, Yuqi Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Foundation models, pre-trained on massive datasets, have achieved unprecedented generalizability. However, is it truly necessary to involve such vast amounts of data in pre-training, consuming extensive computational resources? This paper introduces data-effective learning, aiming to use data in the most impactful way to pre-train foundation models. This involves strategies that focus on data quality rather than quantity, ensuring the data used for training has high informational value. Data-effective learning plays a profound role in accelerating foundation model training, reducing computational costs, and saving data storage, which is very important as the volume of medical data in recent years has grown beyond many people's expectations. However, due to the lack of standards and comprehensive benchmark, research on medical data-effective learning is poorly studied. To address this gap, our paper introduces a comprehensive benchmark specifically for evaluating data-effective learning in the medical field. This benchmark includes a dataset with millions of data samples from 31 medical centers (DataDEL), a baseline method for comparison (MedDEL), and a new evaluation metric (NormDEL) to objectively measure data-effective learning performance. Our extensive experimental results show the baseline MedDEL can achieve performance comparable to the original large dataset with only 5% of the data. Establishing such an open data-effective learning benchmark is crucial for the medical foundation model research community because it facilitates efficient data use, promotes collaborative breakthroughs, and fosters the development of cost-effective, scalable, and impactful healthcare solutions. The benchmark can be accessed at https://***/shadow2469/*** GitHub Repository. © 2024 ACM.

关键词： Contrastive Learning

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Addressing Imbalance for Class Incremental Learning in Medical Image Classification 24

Addressing Imbalance for Class Incremental Learning in Medic...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hao, Xuze Ni, Wenqian Jiang, Xuhao Tan, Weimin Yan, Bo Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneously available. However, in real-world medical scenarios, there's a common need to continuously learn about new diseases, leading to the emerging field of class incremental learning (CIL) in the medical domain. Typically, CIL suffers from catastrophic forgetting when trained on new classes. This phenomenon is mainly caused by the imbalance between old and new classes, and it becomes even more challenging with imbalanced medical datasets. In this work, we introduce two simple yet effective plug-in methods to mitigate the adverse effects of the imbalance. First, we propose a CIL-balanced classification loss to mitigate the classifier bias toward majority classes via logit adjustment. Second, we propose a distribution margin loss that not only alleviates the inter-class overlap in embedding space but also enforces the intra-class compactness. We evaluate the effectiveness of our method with extensive experiments on three benchmark datasets (CCH5000, HAM10000, and EyePACS). The results demonstrate that our approach outperforms state-of-the-art methods. © 2024 ACM.

关键词： Convolutional neural networks

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