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共 2864 条记录，以下是51-60 订阅

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A Mini-model can Make Machine Unlearning Better 24th

A Mini-model can Make Machine Unlearning Better

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhao, Mingkang Yu, Weng Zhu, Congcong China University of Geosciences Wuhan China Ministry of Education Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance School of Information Engineering Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

In recent years, with the development of machine learning, plenty of personal data have been utilized in the training process of the models which incurs severe privacy leakage in the field. Current regulations mandate the removal of private user information from both databases and machine learning models upon specific deletion requests. While wiping data records from memory storage is a straightforward task, eliminating the influence of specific data samples from an already-trained model is challenging. Numerous studies in machine unlearning focus on mitigating the impact of target data by adjusting model parameters in deep learning. However, existing methods for data removal in deep learning often fall short of meeting real-world practicality. the intricacies of deep neural networks make unlearning in deep learning a less expedient process than retraining from scratch. For example, some methods require a ton of matrix calculations or even more complicated computations, and some algorithms need a lot of space, which ends up hindering their effectiveness in real-world applications. Accordingly, we’ve come up with a more practical solution. Our method just needs a small part of the validation dataset to pre-train a mini-model to update the parameters of the target model quickly. With this assistant model, we can quickly and accurately unlearn target classes and items. We prove that our proposed method is highly effective in unlearning tasks with not very large amounts of data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor 20th

swDarknet: A Heterogeneous Parallel Deep Learning Framework ...

引用

20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Liu, Huazeng Tian, Min Wu, Xiaoming Yang, Meihong Ren, Zenghui Li, Yanlong Jinan China Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9789819628292

As the size of AI models grows dramatically to achieve higher accuracy, more and more models are trained on supercomputers with massive computing and memory. However, it is still challenging to accelerate deep learning applications on supercomputer systems with many-core architectures. In this paper, we propose swDarknet: a Darknet-based heterogeneous parallel deep learning framework on the SW26010 Pro processor. First, We designed three heterogeneous parallel DNN layers: adaptive block-splitting strategy for matrix-multiplication layer, optimized memory access for convolutional layer, and block-based parallel pooling layer. Second, we proposed a model averaging (MA) method to efficiently scale swDarknet across many processors. Finally, we evaluated the performance of swDarknet on a core group. As compared with darknet, the convolutional layers achieved an average speedup of 32x. When training AlexNet and VGG-16, swDarknet can achieve up to 36.79× and 181.35× speedup, respectively. © IFIP international Federation for Information processing 2025.

关键词： Supercomputers

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Data Poisoning Attack Against Reinforcement Learning from Human Feedback in Robot Control Tasks 24th

Data Poisoning Attack Against Reinforcement Learning from H...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhou, Zihui Gao, Yutong Qi, Minfeng Wuhan430074 China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

Reinforcement Learning from Human Feedback (RLHF) technology provides a method for agents to learn human preferences and perform actions that satisfy human desires. this technology was originally used to complete robot control tasks in situations where it was difficult to design a reward function. However, in the process of collecting human feedback, malicious human annotators may launch attacks on RLHF, posing a significant challenge to the technology. Previous research has mostly focused on studying the harm caused by different attacks on RLHF in fine-tuning large language models (LLMs). However, our study is focused on the field of robot control. We designed two data poisoning attacks against human feedback datasets and implemented our attacks in three different offline reinforcement learning (RL) environments. the experimental results show that RLHF is vulnerable to data poisoning attacks in robot control tasks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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8th international conference on Material Engineering and Nanotechnology, ICMEN 2024

8th International Conference on Material Engineering and Nan...

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8th international conference on Material Engineering and Nanotechnology, ICMEN 2024

ISBN: (纸本)9789819636235

the proceedings contain 30 papers. the special focus in this conference is on Material Engineering and Nanotechnology. the topics include: On the Effect of Magnesium on the Mechanical Properties of Aluminium Alloy 9031 (i.e. Modified Aluminium Alloy 3003);Analyzing the Breakdown of the Stokes-Einstein Law in AgI–AgPO3 and M–PO3 (M = Mg, Ca, Sr, Ba) Melts;influence of Particle Shape on Heat Transfer from Plasmonic Gold Nanoparticles to a Matrix;development of an Image processing Method to Determine the Air Void Parameters of Concrete;targeting Colorectal Cancer Using Krantz Aloe (Aloe arborescens) Extract Green Synthesised Chromium Doped Fe3O4 Nanoparticles;Evaluation of Physico-mechanical Properties of LDPE-Based Bubble Wrap and Coconut Coir as Concrete Pavement Tile Reinforcement;formation Mechanism of CaZrO3/ZrO2 Eutectic Solidified Film Using High Power Laser;using parallel Plate Electrode Engineering Techniques for Nondestructive Testing of Timber;effect of Ruthenium on Phase Stability and Superelasticity of Lean TiNb Alloys: Ab-Initio and Experimental Insights;towards Resilient and Sustainable Water and Energy Sectors in Jordan;First Principles Investigation of Binary FCC Pt-Ru and Pt-Ir Alloys;ultrasound-Assisted Extraction of Starch from Taro (Colocasia Esculenta) Peel in Varying Sorbitol Concentration with Pineapple (Ananas Comosus) Leaf Fibers and Rice (Oryza Sativa) Husks as Additives for the Production of Enhanced Bioplastic;waste Vegetable Oil as a Sustainable Additive for Enhancing Asphalt Binder Properties: A Review;utilization of Tilapia Fish Scale (Oreochromis niloticus) for the Production of Collagen-Sodium Silicate Composite Aerogel for Moderate Temperature Insulation;using Radio Wave Sensor for Assessment of Sustainable Road Pavement Incorporated Olive Waste Ash.

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Hybridization of One- and Two-Point Bandits Convex Optimization in Non-stationary Environments 24th

Hybridization of One- and Two-Point Bandits Convex Optimiz...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. the path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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Novel Scheduling and Shifter Networks for 5G LDPC Decoders 18th

Novel Scheduling and Shifter Networks for 5G LDPC Decoders

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18th international Workshop on Design and Architecture for Signal and Image processing, DASIP 2025

作者： Papageorgiou, Nikos Paliouras, Vassilis Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031878961

Low Density Parity Check (LDPC) codes under iterative decoding have shown remarkable error correction capabilities, with moderate complexity requirements. Initially, in this paper a check based on a part of the parity check matrix (core part check) is presented. the proposed check is amenable for hardware implementation and allows the termination of the decoding procedure at a sub-iteration level, i.e., within an iteration. In this way the number of clock cycles required reduced by 150 for the semi-parallel architecture and for 5G NR codes. Simultaneously the Block Error Rate (BLER) remains the same while hardware becomes simpler. In addition, this paper introduces a novel scheduling scheme combined with the core part check and a syndrome-select logic. Experimental results are offered, assuming the parallel architecture, which show that the proposed rescheduling reduces the average number of required clock cycles per decoded word. Simultaneously improves the BLER utilizing exactly the same hardware. Specifically, targeting 5G NR LDPC codes, gains of 35% are achieved, for both the OMS and NMS algorithms. Furthermore, an innovative approach for the merge network of the reconfigurable barrel shifter is proposed. Finally, an ASIC implementation for semi-parallel architecture and the energy consumption gained with the proposed algorithm are presented. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： parallel architectures

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Optimizing Vo-Viso: A Modified Methodology to parallel Computing with Isolating Data in Memristor Arrays 20th

Optimizing Vo-Viso: A Modified Methodology to Parallel Comp...

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20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Wang, Wenqing Chen, Yabo Xu, Nuo Hu, Yihong Huang, Chenglong Wei, Shaojun Fang, Liang National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9789819628292

the MAGIC NOR gate-based memristor array is a promising processing-in-memory (PIM) approach due to its massive parallelism, flexible logical operation, and non-volatile memory characteristics. the Vo-Viso MAGIC NOR gate operation mode is proposed to achieve high-throughput parallel computing and isolate other data within a memristor array. However, Viso has a cumulative effect on the node voltage of the active row, calling into question the feasibility of Vo-Viso operation mode. In this paper, circuit analysis defines the necessary circuit parameter conditions for enabling parallel computing and isolation units in the MAGIC NOR gate array. By algorithms in Python and CUDA C++ exploration, we determined that achieving the necessary conditions within the array parameters ranges—including the tolerance margin VΔ, array size, Vo, Viso and memristor parameters suitable for real-world circuit requirements—is unfeasible. In response to these challenges, we modified the memristor array architecture. the modified architecture employs transistors to activate compute units while safeguarding the rest selectively. the feasibility of this architecture is validated by SPICE with selected transistors fabricated using a 65 nm process, taking into account transistor resistance and the fluctuations in Vset and Vreset in the 8×8 modified memristor array. To accommodate the design requirement of the state-of-the-art memristor array, the feasibility of 64×64 modified memristor array, taking into account the impact of wire resistance, is further verified. this paper bridges the gap between reliable high-throughput parallel computing operation and MAGIC NOR PIM architecture. © IFIP international Federation for Information processing 2025.

关键词： SPICE

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On the Incorrect Use of Application Efficiency to Calculate Performance Portability 15th

On the Incorrect Use of Application Efficiency to Calcula...

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15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Marowka, Ami Parallel Research Lab Petah Tikva Israel

ISBN: (纸本)9783031856990

the emergence of heterogeneity in high-performance computing, which harnesses under one integrated system several platforms of different architectures, also led to the development of innovative cross-platform programming models. Along with the expectation that these models will yield computationally intensive performance, there is demand for them to provide a reasonable degree of performance portability. therefore, new tools and metrics are being developed to measure and calculate the level of performance portability of applications and programming models. the ultimate measure of performance portability is performance efficiency. Performance efficiency refers to the achieved performance as a fraction of some peak theoretical or practical baseline performance. Application efficiency approaches are the most popular and attractive performance efficiency measures among researchers because they are simple to measure and calculate. Unfortunately, the way they are used yields results that do not make sense, while violating one of the basic criteria that defines and characterizes the performance portability metrics. In this paper, we demonstrate how researchers currently use application efficiency to calculate the performance portability of applications and explain why this method deviates from its original definition. then, we show why the obtained results do not make sense and propose practical solutions that satisfy the definition and criteria of performance portability metrics. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Application Efficiency Metrics Performance Portability

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Dynamic Privacy Protection with Large Language Model in Social Networks 24th

Dynamic Privacy Protection with Large Language Model in So...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xie, Yizhe Zhu, Congcong Zhang, Xinyue Hu, Xiangyu Liu, Xuan School of Data Science City University of Macau Macau China Hainan International College Minzu University of China Hainan China School of Information Engineering Minzu University of China Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9789819615445

In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static, falter in effectively safeguarding privacy amidst the complex interplay of continuously changing network interactions and structures. Addressing these deficiencies, this study introduces a novel dynamic privacy protection system anchored by large language model (LLM). Leveraging the natural language processing prowess of LLM, this system excels in real-time, context-sensitive analysis and protection of textual data within vast and variable social networks. By integrating closed-loop control theory, the system adeptly balances robust privacy safeguards with the requisite fluidity of information exchange. Experimental validations on large network datasets illustrate the system’s adeptness in balancing privacy leaks and information distortion through intelligent adaptations to privacy thresholds and strategic noise injection. the outcomes highlight the system’s utility in enhancing data security and operational efficiency, promising significant implications for future applications in broader domains such as mobile computing and IoT. this study not only propels forward the capabilities of dynamic privacy protection mechanisms but also sets a foundational architecture for subsequent innovations in privacy-sensitive, data-intensive environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Cross-Modal Mask and Detail Alignment for Text-Based Person Retrieval 24th

Cross-Modal Mask and Detail Alignment for Text-Based Perso...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Guo, Ao Liu, Xuan Liu, Xianggan Hu, Bingmeng Yuan, Jie Chu, Chiawei Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China City University of Macau China

ISBN: (纸本)9789819615278

Text-based person retrieval primarily aims to retrieve the images of target persons represented by a given text query. In this task, how to effectively align images and text globally and locally is an important challenge. At the same time, since multiple modalities are involved, reducing the differences between different modalities is also an important challenge. Existing work has focused more on reconstructing an image rather than the semantic consistency between the image and text modalities. therefore, we introduce the Cross-Modal Mask and Detail Alignment (CMDA) framework to address these challenges. Under this framework, our proposed Cross-Modal Mask Alignment module (CMA) semantically aligns features generated by supplementing randomly masked image/text with another modality text/image. Additionally, to narrow the gap between the image and text modalities, we designed the Cross-Modal Detail Alignment module (CDA), which establishes connections between images and texts and facilitates interactions between these two different modalities. Experimental results show that our model exhibits outstanding performance across multiple public datasets, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReID. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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