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检索条件"机构=Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3"

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DASIV: Directional Acoustic Sensing based Intelligent Vehicle Interaction System

DASIV: Directional Acoustic Sensing based Intelligent Vehicl...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Zhao, Dinghua Zhou, Juntao Ding, Dian Lu, Yu Li, Yijie Yang, Hang Chen, Yi-Chao Xue, Guangtao Shanghai Jiao Tong University Department of Computer Science and Engineering China Governance and Web3 Shanghai Key Laboratory of Trusted Data Circulation China

ISBN: (纸本)9798350367942

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%). © 2024 IEEE.

关键词： Palmprint recognition

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DI-Tree: A Dual-ended Interval Tree for Efficient Event Matching in Content-based Pub/Sub Systems 30

DI-Tree: A Dual-ended Interval Tree for Efficient Event Matc...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Li, Junshen Ren, Haiyang Liao, Zhengyu Shi, Wanghua Qian, Shiyou Xue, Guangtao Cao, Jian Zheng, Zhonglong Zhejiang Normal University Zhejiang China Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China

ISBN: (纸本)9798331515966

Content-based publish/subscribe systems have the capability to achieve fine-grained data distribution, rapidly forwarding data from publishers to subscribers with specific requirements. The event matching algorithm is a fundamental component, quickly searching for subscriptions that match an event based on constraints defined by subscribers. As data scales continue to expand, there is a heightened demands for more efficient, robust, and versatile event matching algorithms. In this paper, we propose a novel data structure called the Dual-ended Interval Tree (DI-Tree). Firstly, given the splitting point, this data structure classifies intervals into three categories based on the joint distribution of their left and right endpoints in the attribute value domain. Furthermore, the DI-Tree utilizes blue and green nodes to store these three categories of intervals, resulting in enhanced indexing efficiency. Moreover, by utilizing the DITree to efficiently search matching and unmatching intervals, we develop innovative forward and backward event matching algorithms. Additionally, to enhance matching efficacy and reduce memory usage, we introduce key optimization techniques, focusing on improving node balance and bitset optimization. We conduct extensive experiments to evaluate the performance of DITree. When compared with five state-of-the-art event matching algorithms, the DI-Tree demonstrates an average reduction of up to 76.8 % in terms of matching time. This significant improvement highlights the effectiveness of our proposed strategies and the potential of DI-Tree in optimizing event matching performance. © 2024 IEEE.

关键词： data structures

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Generative Diffusion Model-based Energy Management in Networked Energy Systems

Generative Diffusion Model-based Energy Management in Networ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lu, Xinyu Feng, Zhanbo Sun, Jiawei Lou, Jiong Wu, Chentao Bao, Wugedele Li, Jie Shanghai Jiao Tong University China Yancheng Blockchain Research Institute China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3 China Hohhot Minzu College China

ISBN: (纸本)9798350368741

In recent years, the proliferation of renewable energy sources has heightened the focus on networked energy systems. These systems face significant challenges due to the unpredictable nature of energy generation and consumption, as well as the complexity of managing numerous components and parameters. To address the challenges associated with the time-consuming nature of optimization problems and the expansive solution space, we propose an innovative energy management method based on a generative diffusion model applicable to general networked energy systems. This approach aims to balance energy supply and demand while minimizing transmission costs. The efficacy of this method is validated through evaluations on real-world datasets and simulations, demonstrating a 26.6% cost reduction compared to the state-of-the-art model and a 62.8% decrease in execution time compared to existing optimizers. This research highlights the potential of generative diffusion techniques in networked energy management. Code: https://***/gale13/GEM. © 2025 IEEE.

关键词： energy management Generative diffusion model renewable energy sources scheduling

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Are LLMs Aware that Some Questions are not Open-ended?

Are LLMs Aware that Some Questions are not Open-ended?

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

作者： Yang, Dongjie Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is worth exploring whether LLMs are aware that some questions have limited answers and need to respond more deterministically but some do not. We refer to this as question awareness of LLMs. The lack of question awareness in LLMs leads to two phenomena that LLMs are: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions. In this paper, we first evaluate the question awareness in LLMs. The experimental results show that LLMs have the issues of lacking awareness of questions in certain domains, e.g. factual knowledge, resulting in hallucinations during the generation. To mitigate these, we propose a method called Question Awareness Temperature Sampling (QuATS). This method enhances the question awareness of LLMs by adaptively adjusting the output distributions based on question features. The automatic adjustment in QuATS eliminates the need for manual temperature tuning in text generation and consistently improves model performance in various benchmarks. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Head-wise Shareable Attention for Large Language Models

Head-wise Shareable Attention for Large Language Models

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

作者： Cao, Zouying Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-grained sharing rules, e.g., layer-wise. This becomes limiting given the prevalence of LLMs and sharing an entire layer or block obviously diminishes the flexibility of weight sharing. In this paper, we present a perspective on head-wise shareable attention for large language models. We further propose two memory-efficient methods that share parameters across attention heads, with a specific focus on LLMs. Both of them use the same dynamic strategy to select the shared weight matrices. The first method directly reuses the pre-trained weights without retraining, denoted as DirectShare. The second method first post-trains with constraint on weight matrix similarity and then shares, denoted as PostShare. Experimental results reveal our head-wise shared models still maintain satisfactory capabilities, demonstrating the feasibility of fine-grained weight sharing applied to LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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LaCo: Large Language Model Pruning via Layer Collapse

LaCo: Large Language Model Pruning via Layer Collapse

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

作者： Yang, Yifei Cao, Zouying Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large language models (LLMs) based on transformer are witnessing a notable trend of size expansion, which brings considerable costs to both model training and inference. However, existing methods such as model quantization, knowledge distillation, and model pruning are constrained by various issues, including hardware support limitations, the need for extensive training, and alterations to the model internal structure. In this paper, we propose a concise layer-wise structured pruner called Layer Collapse (LaCo), in which rear model layers collapse into a prior layer, enabling a rapid reduction in model size while preserving the model structure. Comprehensive experiments show that our method maintains an average task performance of over 80% at pruning ratios of 25-30%, significantly outperforming existing state-of-the-art structured pruning methods. We also conduct post-training experiments to confirm that the LaCo effectively inherits the parameters of the original model. Additionally, we perform ablation studies on various settings of LaCo. Finally, we discuss our motivation from the perspective of layer-wise similarity and evaluate the performance of the pruned LLMs across various pruning ratios. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Instruction-Driven Game Engine: A Poker Case Study

Instruction-Driven Game Engine: A Poker Case Study

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

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

ISBN: (纸本)9798891761674

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. © 2024 Association for Computational Linguistics.

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Hey Hey, My My, Skewness Is Here to Stay: Challenges and Opportunities in Cloud Block Store Traffic 25

Hey Hey, My My, Skewness Is Here to Stay: Challenges and Opp...

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20th European Conference on Computer Systems-EuroSys

作者： Wu, Haonan Xu, Erci Wang, Ligang Hong, Yuandong Niu, Changsheng Shi, Bo Zhu, Lingjun He, Jinnian Wu, Dong Zhang, Weidong Wang, Qiuping Wang, Changhong Chen, Xinqi Xue, Guangtao Chen, Yi-Chao Ding, Dian Shanghai Jiao Tong Univ Shanghai Peoples R China Alibaba Cloud Hangzhou Peoples R China Shanghai Key Lab Trusted Data Circulat Governance & Web3 Shanghai Peoples R China

ISBN: (纸本)9798400711961

Elastic Block Storage (EBS) has a pivotal role in modern data center infrastructure, providing reliable, high-performance and flexible block storage service to users. In Alibaba Cloud, EBS is the most widely used service and has been supporting the operation of millions of virtual disks. However, even with layers of load balancing and caching, we still observe significant traffic skewness across the EBS stack. This motivates us to comprehensively investigate symptoms and root causes behind the traffic patterns and, more importantly, explore the fixes for the identified issues. In this paper, we collect 310 million IO traces from approximately 60k virtual machines and 140k virtual disks deployed in our EBS. Based on extensive statistical analysis, we examine the traffic skewness across multiple components of the EBS stack. We identify four typical symptoms related to the IO virtualization framework, traffic throttle, storage cluster management and cache. For each symptom, we further explore the potential solutions along with the challenges.

关键词： Compute-Storage Disaggregation Elastic Block Storage Load Balancing Cache

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GPMS: Enabling Indoor GNSS Positioning using Passive Metasurfaces 24

GPMS: Enabling Indoor GNSS Positioning using Passive Metasur...

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30th International Conference on Mobile Computing and Networking, ACM MobiCom 2024

作者： Wang, Yezhou Pan, Hao Qiu, Lili Zhong, Linghui Liu, Jiting Ma, Ruichun Chen, Yi-Chao Xue, Guangtao Ren, Ju Shanghai Jiao Tong University China Microsoft Research Asia China Central South University China Columbia University United States Yale University United States Tsinghua University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798400704895

Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this issue, we propose an innovative indoor GNSS positioning system called GPMS, which uses passive metasurface technology to redirect GNSS signals from outdoors into indoor spaces. These passive metasurfaces are strategically optimized for indoor coverage by steering and scattering the GNSS signals across a wide range of incident angles. We further develop a novel localization algorithm that can determine which metasurface the signal goes through and localize the user using the set of metasurfaces as anchor points. A distinct advantage of our localization algorithm is that it can be implemented on existing mobile devices without any hardware modifications. We implement the prototype of GPMS, and deploy six metasurfaces in two indoor environments, a 10×50 m2 office floor and a 15×20 m2 lecture room, to evaluate system performance. In terms of coverage, our GPMS increases the C/N0 from 9.1 dB-Hz to 23.2 dB-Hz and increases the number of visible satellites from 3.6 to 21.5 in the office floor. In terms of indoor positioning accuracy, our proposed system decreases the absolute positioning error from 30.6 m to 3.2 m in the office floor, and from 11.2 m to 2.7 m in the lecture room, demonstrating the feasibility and benefits of metasurface-assisted GNSS for indoor positioning. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Global positioning system

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