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Emotion classification based on EEG wavelet features and LSTM network 5

Emotion classification based on EEG wavelet features and LST...

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5th International conference on Signal Processing and computer Science, SPCS 2024

作者： Tian, Jinghong Luo, Xu Department of Mathematics and Applied Mathematics Jinan University Panyu Campus Guangdong Province Guangzhou China Department of Computer and Mathematics Fundamentals Teaching Shenyang Normal University Shenyang China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This study explores an advanced method for emotion classification using electroencephalogram (EEG) data, leveraging the DEAP dataset. The proposed approach combines wavelet transform for feature extraction with long short-term memory (LSTM) neural networks for classification. Initially, the EEG signals were decomposed using fast discrete wavelet transform (DWT) to extract wavelet coefficients from both low-frequency and high-frequency sub-bands. Key statistical features, including the maximum, minimum, mean, standard deviation, energy value, and relative energy value of these coefficients, were computed to form comprehensive feature vectors. These feature vectors were then input into a sophisticated 7-layer LSTM neural network for training and testing. The LSTM network's ability to handle long-term dependencies in sequential data proved highly effective in this context. This study conducted experimental comparisons between single-channel and multi-channel classification performance and explored the impact of different feature component combinations on classification outcomes. The experimental results showed that multi-channel combinations significantly improved classification accuracy, with the best accuracy 95.15% observed in the 8-channel combination scheme. Further analysis revealed that when the feature data contains wavelet components with frequencies between 0 and 8 Hz, the classification performance of the network can be significantly improved. © 2025 SPIE.

关键词： Electroencephalography

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Scloud+: An Efficient LWE-Based KEM Without Ring/Module Structure 9th

Scloud+: An Efficient LWE-Based KEM Without Ring/Module Stru...

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9th International conference on Security Standardisation Research, SSR 2024

作者： Wang, Anyu Zheng, Zhongxiang Zhao, Chunhuan Qiu, Zhiyuan Zeng, Guang Yuan, Ye Mu, Changchun Wang, Xiaoyun Institute for Advanced Study BNRist Tsinghua University Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China Shield Lab Huawei Technologies Beijing China Shandong Institute of Blockchain Jinan China Digital Currency Institute the People’s Bank of China China Zhongguancun Laboratory Beijing China National Financial Cryptography Research Center Beijing China Key Laboratory of Cryptologic Technology and Information Security School of Cyber Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031875403

We present Scloud+, an LWE-based key encapsulation mechanism (KEM). The key feature of Scloud+ is its use of the unstructured-LWE problem (i.e., without algebraic structures such as rings or modules) and its incorporation of ternary secrets and lattice coding to enhance performance. A notable advantage of the unstructured-LWE problem is its resistance to potential attacks exploiting algebraic structures, making it a conservative choice for constructing high-security schemes. However, a key disadvantage of such schemes is their limited computational and communication efficiency. Scloud+ utilizes ternary secrets and BW32 lattice codes to enhance noise control and ensure robust error correction during decryption, enabling smaller parameters while maintaining low decryption failure probabilities. Equipped with these techniques, Scloud+ exhibits a significant improvement in efficiency. When compared with FrodoKEM for parameter sets targeting 128, 192, and 256 bits of security respectively, Scloud+ achieves practical performance with a public key size approximately 0.71∼0.87x and a ciphertext size approximately 0.56∼0.78x that of FrodoKEM. The encapsulation plus decapsulation time is approximately 0.74∼0.84x that of FrodoKEM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum cryptography

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SYNAPSE: TRAJECTORY-AS-EXEMPLAR PROMPTING WITH MEMORY FOR computer CONTROL 12

SYNAPSE: TRAJECTORY-AS-EXEMPLAR PROMPTING WITH MEMORY FOR CO...

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12th International conference on Learning Representations, ICLR 2024

作者： Zheng, Longtao Wang, Rundong An, Bo Wang, Xinrun Nanyang Technological University Singapore Skywork AI Singapore

Building agents with large language models (LLMs) for computer control is a burgeoning research area, where the agent receives computer states and performs actions to complete complex tasks. Previous computer agents have demonstrated the benefits of in-context learning (ICL);however, their performance is hindered by several issues. First, the limited context length of LLMs and complex computer states restrict the number of exemplars, as a single webpage can consume the entire context. Second, the exemplars in current methods, such as high-level plans and multi-choice questions, cannot represent complete trajectories, leading to suboptimal performance in long-horizon tasks. Third, existing computer agents rely on task-specific exemplars and overlook the similarity among tasks, resulting in poor generalization to novel tasks. To address these challenges, we introduce SYNAPSE, a computer agent featuring three key components: i) state abstraction, which filters out task-irrelevant information from raw states, allowing more exemplars within the limited context, ii) trajectory-as-exemplar prompting, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-making, and iii) exemplar memory, which stores the embeddings of exemplars and retrieves them via similarity search for generalization to novel tasks. We evaluate SYNAPSE on MiniWoB++, a standard task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, SYNAPSE achieves a 99.2% average success rate (a 10% relative improvement) across 64 tasks using demonstrations from only 48 tasks. Notably, SYNAPSE is the first ICL method to solve the book-flight task in MiniWoB++. SYNAPSE also exhibits a 56% relative improvement in average step success rate over the previous state-of-the-art prompting scheme in Mind2Web. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Trajectories

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Modeling high-Order Relationships Between Human and Video for Emotion Recognition in Video Learning 31st

Modeling High-Order Relationships Between Human and Video f...

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31st International conference on Multimedia Modeling, MMM 2025

作者： Ai, Hanxu Tao, Xiaomei Li, Xingbing Gan, Yanling Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (纸本)9789819620630

In video learning, accurately identifying learners’ emotional state is fundamental for enhancing the learner experience and improving teaching effectiveness. Most existing methods focus on analyzing learners’ physiological signals, neglecting the impact of instructional videos on learners, especially the hidden high-order relationships between video stimuli and physiological signals. This paper introduces a Multimodal Multi-Hpergraph emotion recognition framework, MMH, leveraging the strengths of hypergraphs in modeling high-order relationships. MMH captures high-order relationships between different modalities within instructional videos and between videos and learners, aiming to identify learners’ emotional states during video learning. During the hypergraph construction process, we employ two strategies to construct the hypergraph: (1) using multiple k-value feature KNN construction methods, and (2) construction methods based on vertex attributes. In the hypergraph learning process, we introduce a vertex attention mechanism to update vertex features during the hypergraph learning process, further improving the emotion recognition performance of MMH. Our method achieves state-of-the-art cross-subject emotion recognition performance on the VLMED dataset. Experimental results on two public benchmark datasets, MAHNOB-HCI and DEAP, further verify the effectiveness of our method in emotion recognition tasks. Our source code will be made publicly available upon acceptance of the paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Optimizing CNNs Throughput on Bandwidth-Constrained Distributed Multi-FPGA Architectures 29

Optimizing CNNs Throughput on Bandwidth-Constrained Distribu...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhou, Yuzhu Peng, Bo Li, Qiang Lin, Maosong Lei, Xiang Wang, Qiao Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology School of Computer Science and Technology Mianyang China

ISBN: (纸本)9798350330717

Existing multi-FPGA architectures often leverage high-speed interconnect technologies to achieve higher performance by exploiting ample communication bandwidth. In this paper, we propose an effective mapping approach for accelerating CNNs on bandwidth-constrained distributed multi-FPGA architectures. We formulate the system-level mapping problem and then introduce a method based on Genetic Algorithm (GA) and Mixed-Integer Nonlinear programming (MINLP) to attain optimal solutions. © 2023 IEEE.

关键词： acceleration bandwidth-constrained convolutional neural networks multi-FPGA

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Rate Distortion Analysis of Wavefield Coding in Wireless Geophone Networks 8th

Rate Distortion Analysis of Wavefield Coding in Wireless G...

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8th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2024

作者： Khan, Hamood ur Rehman Khan, Farhan Sir Syed Center for Advanced Studies in Engineering Islamabad Pakistan Electrical and Computer Engineering Program Habib University Karachi Pakistan

ISBN: (纸本)9783031788055

Current and future trends in seismic acquisition point towards higher geophone densities (forecasted to be 1M nodes per survey). The geophones’ high operating precision and a sampling rate of a few milliseconds leads to a huge aggregate data rate in the geophone array. To handle this large data rate a hierarchy of multiplexed lines (including fiber optic cables) are used, resulting in substantial deployment costs. This work considers wireless geophone networks to mitigate these costs. Because of limited bandwidth of the wireless medium, compression of acquired data is needed. We assess lossy source coding (signal compression) performance using a rate-distortion tradeoff based on a physical model of the earth. The distortion criterion used is mean squared error. The earth’s physical model considered here consists of a randomly layered subsurface structure where the acoustic impedance of the earth varies as a homogeneous spatial random process. The rate distortion performance is assessed in terms of the parameters of this physical model e.g., the speed of sound underground and correlation length of acoustic impedance process. In comparison with previous work, this paper derives a closed form expression for the autocorrelation function for the reflection process for the 3D subsurface volume. We also show from the resulting rate-distortion curves that the compression performance improves both as the coding bock length increases and as the correlation length of the medium properties increases. The rate-distortion surface as a function of normalized MSE and sound of speed underground is also computed and validated through extensive simulations. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Signal distortion

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ReSLB: Load Balanced Workflow for Distributed Deep Learning Mass Spectrometry Database 24

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

引用

24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Mocheng, Li Yang, Liu Yang, Ou Zhiguang, Chen Nong, Xiao Tao, Chen College of Computer National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China Beijing China

ISBN: (纸本)9798350319934

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that. © 2022 IEEE.

关键词： Mass spectrometry

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HyFarM: Task Orchestration on Hybrid Far Memory for high performance Per Bit 40

HyFarM: Task Orchestration on Hybrid Far Memory for High Per...

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IEEE 40th International conference on computer Design (ICCD)

作者： Wang, Jing Li, Chao Mei, Junyi He, Hao Wang, Taolei Wang, Pengyu Zhang, Lu Guo, Minyi Wu, Hanqing Chen, Dongbai Liu, Xiangwe Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China Alibaba Cloud Hangzhou Peoples R China

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Tapping into secondary memory resources, i.e., far memory (FM), has shown huge potential to improve the cost-efficiency of data centers. Recent advances in both storage-based vertical FM and network-based horizontal FM have raised new questions about leveraging hybrid FM tiers to achieve the best performance per bit of memory. It is still unclear how to efficiently place tasks when far memory access is enabled. In this work, we propose HyFarM, a novel task management strategy for hybrid FM clusters. We analyze FM sensitivity and cooperatively co-locate tasks to enable high utilization and scalability. Further, by tapping into dynamic memory adaption within and across servers, our strategy allows one to consistently deliver high performance on memory-intensive tasks. We evaluate our design with a heavily instrumented testbench. Compared with the state-of-the-art designs, HyFarM respectively improves memory utilization and the overall performance per bit (PPB) by up to 17.6% and 20.5%, with minor overhead.

关键词： memory disaggregation hybrid scheduling

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Pose Estimation of Multiple Domains Based on the Fusion of Multiple Deep Learning Models and Baidu API 6

Pose Estimation of Multiple Domains Based on the Fusion of M...

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6th International conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Wang, Jieying Song, Qingzeng Xue, Yongjiang Qiao, Fei School of Computer Science and Technology Tiangong University Tianjin China Tsinghua University Beijing China

ISBN: (纸本)9798350353174

To enhance the precision of pose estimation across various domains, this study introduces a method that leverages the general object recognition capabilities of the Baidu API. For an unfamiliar image, we first employ the output of the Baidu API to infer its domain, then proceed with keypoint detection using a pose estimation model pre-trained on relevant domain datasets. This approach has been validated through extensive experiments on three widely-used datasets: MPII, Animalpose, and Animalkingdom. In comparison to existing algorithms, our method reduces the number of high-performance models to be trained, thus minimizing computational demands and ensuring high scalability. Experimental findings confirm that our method is not only simpler but also more accurate. © 2024 IEEE.

关键词： Contrastive Learning

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Learning Event Guided high Dynamic Range Video Reconstruction

Learning Event Guided High Dynamic Range Video Reconstructio...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang, Yixin Han, Jin Liang, Jinxiu Sato, Imari Shi, Boxin Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

Limited by the trade-off between frame rate and exposure time when capturing moving scenes with conventional cameras, frame based HDR video reconstruction suffers from scene-dependent exposure ratio balancing and ghosting artifacts. Event cameras provide an alternative visual representation with a much higher dynamic range and temporal resolution free from the above issues, which could be an effective guidance for HDR imaging from LDR videos. In this paper, we propose a multimodal learning framework for event guided HDR video reconstruction. In order to better leverage the knowledge of the same scene from the two modalities of visual signals, a multimodal representation alignment strategy to learn a shared latent space and a fusion module tailored to complementing two types of signals for different dynamic ranges in different regions are proposed. Temporal correlations are utilized recurrently to suppress the flickering effects in the reconstructed HDR video. The proposed HDRev-Net demonstrates state-of-the-art performance quantitatively and qualitatively for both synthetic and real-world data.

关键词： Computational imaging

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