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arXiv 2024年

作者： Min, Dehai Hu, Nan Jin, Rihui Lin, Nuo Chen, Jiaoyan Chen, Yongrui Li, Yu Qi, Guilin Li, Yun Li, Nijun Wang, Qianren School of Computer Science and Engineering Southeast University China Department of Computer Science The University of Manchester United Kingdom Advanced Cognitive AI Lab Huawei Technologies China Key Laboratory of New Generation Artificial Intelligence Technology Southeast University Ministry of Education China

Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain specific data has attracted wide attention. However, domain data often exists in a hybrid format, including text and semi-structured tables, posing challenges for the seamless integration of information. Table-to-Text Generation is a promising solution by facilitating the transformation of hybrid data into a uniformly text-formatted corpus. Although this technique has been widely studied by the NLP community, there is currently no comparative analysis on how corpora generated by different table-to-text methods affect the performance of QA systems. In this paper, we address this research gap in two steps. First, we innovatively integrate table-to-text generation into the framework of enhancing LLM-based QA systems with domain hybrid data. Then, we utilize this framework in real-world industrial data to conduct extensive experiments on two types of QA systems (DSFT and RAG frameworks) with four representative methods: Markdown format, Template serialization, TPLM-based method, and LLM-based method. Based on the experimental results, we draw some empirical findings and explore the underlying reasons behind the success of some methods. We hope the findings of this work will provide a valuable reference for the academic and industrial communities in developing robust QA systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Metadata

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Optimizing Transistor Selection Using AH-TOR for Low Noise Amplifier Design in Sensing Applications

Optimizing Transistor Selection Using AH-TOR for Low Noise A...

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International Conference on Sensing Technology, ICST

作者： Dayarnab Baidya Karthick Thiyagarajan Mitradip Bhattacharjee Yang An Department of Electrical Engineering and Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications.

关键词： Low-noise amplifiers Wireless sensor networks PHEMTs Decision making Receivers Electron mobility Robustness Sensors Transistors Integrated circuit reliability

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Analyzing Accuracy of Inverse Power Flow from Noisy Edge Measurements

Analyzing Accuracy of Inverse Power Flow from Noisy Edge Mea...

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2022 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2022

作者： Morgenstern, Carl W. Morgenstern, Jamie H. Yang, Rui Cook, Elizabeth Arizona State University Electrical Engineering TempeAZ United States Computer Science University of Washington SeattleWA United States Power Systems Engineering National Renewable Energy Lab GoldenCO United States Advanced Grid Solutions Duquesne Light Company PittsburghPA United States

ISBN: (纸本)9781665437752

Distributed energy resources (DERs) are driving the need for detailed distribution circuit models which require accurate impedance estimates. Estimating the impedances can be time consuming and verifying the estimates is nearly impossible since the measurements available are noisy and limited to the edge nodes of the circuit. Therefore, electric distribution companies (EDCs) are interested in building models with data-driven methods to save time and improve accuracy. Previous work focused on building data-driven circuit models has not provided theoretical guarantees on the data quantity and quality, so their solutions may not be reliable enough for EDCs to adopt. Therefore, this work provides theoretical guarantees for recovering the impedances of a 3-node model which is designed to be a foundational building block that could eventually recover larger circuits. Our analysis bounds a proposed impedance estimator to the expected value which is extended to establish an upper limit of the data quantity required to meet an accuracy threshold. Unfortunately the proposed estimator, and other estimators examined in this paper, are biased. The estimators are studied empirically to begin to understand how the noise affects the bias. The outcomes suggest that using current instead of power measurements to estimate the impedance results in identical bounds and significantly less bias. © 2022 IEEE.

关键词： Electric load flow

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Bidirectional Multi-Stack RNNs with Attention for Machine Translation

Bidirectional Multi-Stack RNNs with Attention for Machine Tr...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Zhiren Chen Ziyu Qiu Nuo Chen the Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Faculty of Computer Science Dalhousie University Halifax Canada Department of Mechanical Engineering Advanced Control and Mechatronics Lab Dalhousie University Halifax Canada

This project developed a novel attention algorithm for multi-stack bidirectional encoder-decoder RNN (including GRU and LSTM) sequence-to-sequence models, particularly for language translation tasks. The attention mechanism utilizes matrix rearranging and multiplication to compute the significance of the vectors in the encoder output to the vectors in the current decoder hidden states when predicting each word. Our approach achieved 98% of the performance of fine-tuned pretrained T5-small, with 30% to 50% fewer parameters depending on vocabulary size, making our model an ideal choice in cases of single-processor training, low processor resource, limited memory, small dataset, or tasks not supported by pretrained transformers.

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Performance Evaluation of Secure and Privacy-preserving DNS at the 5G Edge

Performance Evaluation of Secure and Privacy-preserving DNS ...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Yacoub Hanna Diana Pineda Kemal Akkaya Abdullah Aydeger Ricardo Harrilal-Parchment Hamdah Albalawi Advanced Wireless and Security Lab Florida International University Miami FL USA Dept. of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA

With the improved network performance and efficiency, 5G has been a very appealing alternative for various applications and devices, including but not limited to Industrial IoT (IIoT) applications. However, since IIoT applications require real-time transmission guarantees for time-critical data, optimizing the 5G network performance has attracted much research recently by pushing critical services to the 5G edge. One of such services is Domain Name System (DNS) which is typically offered by ISPs to serve 5G networks. However, due to its heavy role on Internet traffic, DNS has seen many attacks in the past in terms of its authenticity of records and exposure of user requests. Therefore, there have been many variants of DNS protocol such as DNSSEC and DNS over TLS (DoT) to address these security and privacy issues. As such these new protocols need to be integrated into 5G edge and their overhead should be investigated for their potential use in time-critical applications. In this paper, we consider deploying secure and privacy-preserving DNS to the 5G base stations and investigate the feasibility and performance of such an edge computing approach. To this end, we utilize SDN capabilities and forward packets to a local SDN controller for extracting and processing DNS queries. Thus, the DNS packets are served at the 5G edge with optional security and privacy features without being forwarded to remote ISP servers. Our approach is implemented in a virtualized 5G testbed network, and we present various DNS performance results via different metrics and scenarios.

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Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance

Score-Based Data Generation for EEG Spatial Covariance Matri...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ce Ju Reinmar Josef Kobler Cuntai Guan S-Lab and School of Computer Science and Engineering Nanyang Technological University Singapore RIKEN Artificial Intelligence Project Tokyo and Advanced Telecommunications Research Institute International Kyoto Japan

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Fréchet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Quan Zhang Lei Wang Vishal M. Patel Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Department of Electrical and Computer Engineering Johns Hopkins University USA Pazhou Lab (HuangPu) Guangdong China Guangdong Province Key Laboratory of Information Security Technology Guangdong China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： computer vision Cameras Transformers Pattern recognition Computational complexity Identification of persons

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Adaptive energy-preserving algorithms for guiding center system

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Plasma Science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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DynaPP: A Dynamic Resolution Model with Patch Packing for Fast Online Video Detection

DynaPP: A Dynamic Resolution Model with Patch Packing for Fa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Changrok So Simon S. Woo Jong Hwan Ko Advanced Robotics Lab CTO Division LG Electronics Seoul South Korea Department of Artificial Intelligence Sungkyunkwan University Suwon South Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Online video detection becomes more challenging with higher resolution as computational costs increase proportionally with increasing resolution. To address this issue, we present a novel approach, DynaPP, which arranges object candidate regions into a compact form. DynaPP performs resource-intensive whole-image inference only on sparse key frames, employing reduced resolutions for inference on other frames. Additionally, we propose transforming a 1-stage detector into a dynamic resolution model to facilitate frame inference at reduced resolutions. Here, the dynamic resolution model signifies a model capable of inferring all resolutions, distinguishing itself from typical models by not having restricted inferable resolutions. Unlike prior studies introducing new model structures for multi-resolution models, our work demonstrates that slight modifications to existing models can convert them to dynamic resolution models. DynaPP showcases substantial acceleration in video detection across four representative video datasets: AU-AIR (5.5×), UAVDT (3.67×), VisDrone (2.73×), and ImageNet VID (3.69×), while maintaining a mean average precision with a small loss (≤2.2). Furthermore, we observed that our method achieves a detection acceleration of up to 8.84×, depending on the video clip.

关键词： Image resolution Neural networks Detectors Computational efficiency

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OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning

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IEEE Transactions on Multimedia 2025年

作者： Yin, Xu Pan, Fei An, Guoyuan Huo, Yuchi Xie, Zixuan Yoon, Sung-Eui Korea Advanced Institute of Science and Technology School of Computing Daejeon34141 Korea Republic of University of Michigan School of Computer Science Engineering United States Zhejiang University State Key Lab of CAD and CG China Zhejiang Lab 310058 China University of Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Korea Advanced Institute of Science and Technology Faculty of School of Computing Deajeon34141 Korea Republic of

Existing open-set recognition (OSR) studies typically assume that each image contains only one class label, with the unknown test set (negative) having a disjoint label space from the known test set (positive), a scenario referred to as full-label shift. This paper introduces the mixed OSR problem, where test images contain multiple class semantics, with both known and unknown classes co-occurring in the negatives, leading to a more complex super-label shift that better reflects real-world scenarios. To tackle this challenge, we propose the OpenSlot framework, based on object-centric learning, which uses slot features to represent diverse class semantics and generate class predictions. The proposed anti-noise slot (ANS) technique helps mitigate the impact of noise (invalid or background) slots during classification training, addressing the semantic misalignment between class predictions and ground truth. We evaluate OpenSlot on both mixed and conventional OSR benchmarks. Without elaborate designs, our method not only excels existing approaches in detecting super-label shifts across OSR tasks, but also achieves state-of-the-art performance on conventional benchmarks. Meanwhile, OpenSlot can localize class objects without using bounding boxes during training, demonstrating competitive performance in open-set object detection and potential for generalization. © 1999-2012 IEEE.

关键词： Object detection

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