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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Weili Ding Shuhui Zhang Han Liu Engineering Research Center of Intelligent Control System and Intelligent Equipment Ministry of Education Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao People's Republic of China College of Computer Science and Software Engineering Shenzhen University Shenzhen People's Republic of China

ISBN: (数字)9798350379167

ISBN: (纸本)9798350379174

To address the challenges of gesture recognition accuracy within human-computer interaction technologies, and to overcome the limitations of single-modal biometric feature recognition - which often falls short in practical applications, this paper introduces a multimodal feature fusion classification algorithm, sEMG-vision Tra. This algorithm aims to integrate different types of biometric data, thereby enhancing the robustness and effectiveness of gesture recognition. Initially, we utilize the Mediapipe framework to extract skeletal point data from images of the human hand. The next process involves calculating distance and angle features between skeletal points, as well as extracting time-domain features from surface electromyographic (sEMG) signals. Following this, features from these two modalities are concatenated along the feature dimension and then linearly projected onto the target dimension. To effectively capture the intricate interrelationships among these features, we employ a multi-layer Transformer encoder for encoding the combined features. The encoded features are subsequently mapped to the final output dimension. The effectiveness of our approach is demonstrated through validation on a custom dataset. This algorithm's key contribution lies in the fusion of two distinct modal features, images and EMG signals, to achieve a more holistic and precise feature representation. By this, it significantly enhances our ability to capture the essential information for applications like gesture recognition or human-computer interaction. The algorithm showcases exceptional performance, achieving a noteworthy accuracy rate of 95.56% on a custom dataset.

关键词： Human computer interaction Biometrics Visualization Accuracy Gesture recognition Feature extraction Transformers Electromyography Classification algorithms Time-domain analysis

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LCW: A Lightweight Recommendation Framework for Non-profit Crowdfunding Projects 21

LCW: A Lightweight Recommendation Framework for Non-profit C...

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4th International Conference on Computer Science and software engineering, CSSE 2021

作者： Liu, Jinxin Xiao, Yingyuan Zheng, Wenguang Engineering Research Center of Learning-BasedIntelligent System Ministry of Education Tianjin University of Technology China Tianjin Key Laboratory OfIntelligence Computing and Novel Software Technology Tianjin University of Technology China

ISBN: (纸本)9781450390675

In recent years, crowdfunding platform has gradually emerged as a new economic model, and more and more people choose crowdfunding to obtain the initial funds needed by the project. However, with the increasing number of projects, it is difficult for sponsors to find suitable projects they want to invest in. Therefore, it is very important to build an efficient recommendation system on the crowdfunding platform. Different from previous crowdfunding recommendation systems designed to improve returns, this paper designs a lightweight recommendation framework LCW for non-profit crowdfunding platforms. A large number of experiments on real kiva data sets have proved the effectiveness of this method and it obtains the SOTA results. © 2021 ACM.

关键词： Recommender systems

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sNPU: Trusted Execution Environments on Integrated NPUs

sNPU: Trusted Execution Environments on Integrated NPUs

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Annual International Symposium on Computer Architecture, ISCA

作者： Erhu Feng Dahu Feng Dong Du Yubin Xia Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Department of Precision Instrument Tsinghua University Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (数字)9798350326581

ISBN: (纸本)9798350326598

Trusted execution environment (TEE) promises strong security guarantee with hardware extensions for security-sensitive tasks. Due to its numerous benefits, TEE has gained widespread adoption, and extended from CPU-only TEEs to FPGA and GPU TEE systems. However, existing TEE systems exhibit inadequate and inefficient support for an emerging (and significant) processing unit, NPU. For instance, commercial TEE systems resort to coarse-grained and static protection approaches for NPUs, resulting in notable performance degradation (10%–20%), limited (or no) multitasking capabilities, and suboptimal resource utilization. In this paper, we present a secure NPU architecture, known as sNPU, which aims to mitigate vulnerabilities inherent to the design of NPU architectures. First, sNPU proposes NPU Guarder to enhance the NPU’s access control. Second, sNPU defines new attack surfaces leveraging in-NPU structures like scratchpad and NoC, and designs NPU Isolator to guarantee the isolation of scratchpad and NoC routing. Third, our system introduces a trusted software module called NPU Monitor to minimize the software TCB. Our prototype, evaluated on FPGA, demonstrates that sNPU significantly mitigates the runtime costs associated with security checking (from upto 20% to 0%) while incurring less than 1% resource costs.

关键词： Costs Runtime Prototypes Computer architecture Routing software Resource management

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Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis 25

Enhancing Web Service Anomaly Detection via Fine-grained Mul...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xixuan Yang Xin Huang Chiming Duan Tong Jia Shandong Dong Ying Li Gang Huang Peking University Beijing China Nanyang Normal University Nanyang Henan China Peking University Beijing China and National Key Laboratory of Data Space Technology and System Beijing China Alibaba Group Hangzhou Zhejiang China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400713316

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.

关键词： anomaly detection

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Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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A Dynamic Recognition Method of Metamorphic Relation Identification 13

A Dynamic Recognition Method of Metamorphic Relation Identif...

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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Hong, Jie Zhang, Jie Qiu, Qi Ma, Angang Li, Meng Yan, Shiyu Gong, Helin School of Computing University of South China Hunan Hengyang China Engineering and Technology Research Center Software Evaluation and Testing for Intellectual Equipment of Hunan Province Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

Metamorphic testing is one of the effective methods to alleviate the test oracle problem. Metamorphic relation is the core of metamorphic testing, and there is no effective automatic identification technology. This paper transforms the metamorphic relation recognition issue into a symbolic expression regression problem. The test input pairs are generated using the preset input pattern, and the output pattern is mined by gene expression programming. After reduction and verification, a valid metamorphic relation is obtained. Experiments show that this method can identify the relation obtained by static analysis and a more significant number. At the same time, compared with existing technologies, it has apparent advantages in effectiveness, reliability, and performance. © 2022 IEEE.

关键词： Static analysis

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PB-UAP: Hybride Universal Adversarial Attack for Image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology 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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training Image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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Unified Modeling Method of Heterogeneous Resources for Multi-platform Avionics system Based on AADL

Unified Modeling Method of Heterogeneous Resources for Multi...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Hui Huang Chang Liu Xunli Yang Dong An Yingxian Guo Wenjing Zhu Key Laboratory of Avionics System Integration China Aeronautical Radio Electronics Research Institute Shanghai China School of Reliability and Systems Engineering Beihang University Beijing China School of Software Beihang University Beijing China

Currently, single-platform avionics system modeling often uses service-oriented modeling methods to describe service-oriented architecture (SOA). However, for the multi-platform avionics system, the traditional modeling method can not represent the heterogeneous resources uniformly; and the description method of the relationship between services and resources is lacking. To solve these problems, this paper proposes a modeling method based on architecture analysis and design language (AADL). The heterogeneous resource pool model is constructed by unified description of heterogeneous resources internally and externally. In addition, a service-oriented approach is used to match function servitization with resources. This method is used to model a multi-platform avionics system. The example shows that this method can abstract heterogeneous resources in the same way, support multiple types of resources, and describe the relationship between services and resources in the task scenario.

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC systems

Joint DNN Partition and Thread Allocation Optimization for E...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Shi, Yizhou Li, Liying Zeng, Yue Cong, Peijin Zhou, Junlong Nanjing University National Key Laboratory for Novel Software Technology Nanjing210023 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China

ISBN: (纸本)9783982674100

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%. © 2025 EDAA.

关键词： Energy utilization

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Residual resampling-based physics-informed neural network for neutron diffusion equations

arXiv

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arXiv 2024年

作者： Zhang, Heng He, Yun-Ling Liu, Dong Hang, Qin Yao, He-Min Xiang, Di Chongqing University of Posts and Telecommunications Chongqing400065 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China CNNC Engineering Research Center of Nuclear Energy Software and Digital Reactor Chengdu610213 China

The neutron diffusion equation plays a pivotal role in the analysis of nuclear reactors. Nevertheless, employing the Physics-Informed Neural Network (PINN) method for its solution entails certain limitations. Traditional PINN approaches often utilize fully connected network (FCN) architecture, which is susceptible to overfitting, training instability, and gradient vanishing issues as the network depth increases. These challenges result in accuracy bottlenecks in the solution. In response to these issues, the Residual-based Resample Physics-Informed Neural Network(R2-PINN) is proposed, which proposes an improved PINN architecture that replaces the FCN with a Convolutional Neural Network with a shortcut(S-CNN), incorporating skip connections to facilitate gradient propagation between network layers. Additionally, the incorporation of the Residual Adaptive Resampling (RAR) mechanism dynamically increases sampling points, enhancing the spatial representation capabilities and overall predictive accuracy of the model. The experimental results illustrate that our approach significantly improves the model’s convergence capability, achieving high-precision predictions of physical fields. In comparison to traditional FCN-based PINN methods, R2-PINN effectively overcomes the limitations inherent in current methods, providing more accurate and robust solutions for neutron diffusion equations. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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