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28th International Conference on Computational Linguistics, COLING 2020

作者： Jiang, Bin Zhou, Wanyue Yang, Jingxu Yang, Chao Wang, Shihan Pang, Liang College of Computer Science and Electronic Engineering Hunan University China Intelligent Systems Utrecht University Netherlands CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Endowing a chatbot with a personality is essential to deliver more realistic conversations. Various persona-based dialogue models have been proposed to generate personalized and diverse responses by utilizing predefined persona information. However, generating personalized responses is still a challenging task since the leverage of predefined persona information is often insufficient. To alleviate this problem, we propose a novel Persona Enhanced Dual Alternating Learning Network (PEDNet) aiming at producing more personalized responses in various open-domain conversation scenarios. PEDNet consists of a Context-Dominated Network (CDNet) and a Persona-Dominated Network (PDNet), which are built upon a common encoder-decoder backbone. CDNet learns to select a proper persona as well as ensure the contextual relevance of the predicted response, while PDNet learns to enhance the utilization of persona information when generating the response by weakening the disturbance of specific content in the conversation context. CDNet and PDNet are trained alternately using a multi-task training approach to equip PEDNet with the both capabilities they have learned. Both automatic and human evaluations on a newly released dialogue dataset Persona-chat demonstrate that our method could deliver more personalized responses than baseline methods. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Learning systems

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Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

arXiv

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arXiv 2024年

作者： Zheng, Jiakang Zhang, Jiayi Du, Hongyang Zhang, Ruichen Niyato, Dusit Dobre, Octavia A. Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore Faculty of Engineering and Applied Science Memorial University Canada The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Cell-free (CF) massive multiple-input multiple-output (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Rate-splitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and non-convexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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High-Precision Stereo Matching with a Drone Equipped With Rtk-Gnss and a Single Camera for Plant Height Measurements

SSRN

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SSRN 2022年

作者： Matsuura, Yuta Heming, Zhang Nakao, Kousuke Qiong, Chang Firmansyah, Iman Kawai, Shin Yamaguchi, Yoshiki Hayashi, Hisayoshi Maruyama, Tsutomu Nobuhara, Hajime The Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan College of Engineering Systems School of Science and Engineering University of Tsukuba Japan The Department of Computer Science Graduate School of Science and Technology University of Tsukuba Japan The Faculty of Life and Environmental Sciences University of Tsukuba Japan The School of Computing Tokyo Institute of Technology Japan

We propose a high-precision plant height measurement system based on large-scale stereo images captured by a drone. In our system, the drone collects images of a field using a monocular camera and obtains the position data by a real time kinematic (RTK)-global navigation satellite system (GNSS). The heights of plants in the field are measured by stereo matching. By employing a single monocular camera rather than a binocular camera, we can obtain a sufficient baseline length for accurate height measurements, but the images must be precisely calibrated at run time. To this end, we propose a calibration algorithm with a conventional stereo matching algorithm. The proposed method requires only a pair of images and their position data (RTK-GNSS), which are more suitable for edge computing on drones than three-dimensional (3D) reconstructions by methods such as Structure from Motion. To show the effectiveness of the proposed method, we measured the plant height from photographs captured by the drone at different altitudes. Our method achieves a higher height resolution (1.6 cm) than the 3D reconstruction-based methods Pix4Dmapper and Metashape, with error-rate reductions of 62.2% and 69.4%, respectively, in the 10-20 m altitude range. When the altitude is lowered to 4.1 m , the depth resolution is improved to 1.6 mm with error reductions of 44.4% and 63.0% from those of Pix4Dmapper and Metashape, respectively. On a GTX 1080 TI graphics processing unit, stereo matching of 5472 × 3468-pixel images consumed 88 ms. This execution time is sufficiently fast for real-time measurements. Because our system uses the position data obtained by RTK-GNSS, it achieves centimeter-level accuracy during the drone flight. After the flight, the accuracy can be improved to millimeter level by comparing the results of the stereo matching with an image containing a reference plant of preobtained known height © 2022, The Authors. All rights reserved.

关键词： Global positioning system

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Security aspects of FPGA and virtualization case studies 51

Security aspects of FPGA and virtualization case studies

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Die 51. Jahrestagung der Gesellschaft fur Informatikin, INFORMATIK 2021 - 51st Annual Conference of the German Informatics Society, INFORMATIK 2021

作者： Tellabi, Asmaa Sabri, Abdelbast Ruland, Christoph Waedt, Karl University of Siegen Faculty of Science and Engineering Data Communication Systems Hölderlinstraße 3 Siegen57068 Germany University Friedrich-Alexander-University Martensstraße 5a Erlangen91058 Germany Framatome GmbH Henri-Dunant-Str. 50 Erlangen91058 Germany

ISBN: (纸本)9783885797081

Virtualization technology is a technology that has been integrated a lot lately, thanks to its interoperability and enhanced performances. An essential element in virtualization is the hypervisor that is responsible of managing virtual machines;it allows various guest operating systems (OS) to run on one hardware entity simultaneously. Many researchers recognized the challenge of allowing multiple critical applications to share one hardware platform without interfering, and ensuring security of transactions a major safety and security challenge in virtualization. Usually, security by design is also comprises the protection of intellectual property (IP), possible safety related damages and extend financial losses. By the extension of the usage of programmable logic outside commercial markets to avionics and military applications, security by design integrates extra aspects to ensure safety and cybersecurity. Solutions for protecting application data during transmission and storage exist, but solutions for protecting Field Programmable Gate Array (FPGA) configuration data are not well-known. In the context of Industry 4.0, the FPGA hardware solutions provide the needed level of flexibility and performance. The flexibility, portability and even, to a wide extent, technology independence are due to the software based definition of the hardware via Hardware Description Languages (HDL). In this paper, threats and vulnerabilities surrounding FPGAs will be addressed. An example of a type 1 hypervisor called XtratuM will be ported on top of a Xilinx Soc 7000 zc706 will be integrated, as well as a prototype of embedded system using PetaLinux will be provided. © 2021 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Embedded systems

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Remaining Useful Life Prediction of Aero-Engine using CNN-LSTM and mRMR Feature Selection

Remaining Useful Life Prediction of Aero-Engine using CNN-LS...

引用

System Reliability and Safety engineering (SRSE), International Conference on

作者： Zhikun Zhou Lechang Yang Zhe Wang Yuantao Yao School of Mechanical Engineering University of Science and Technology Beijing China Department of Advanced Design and Systems Engineering City University of Hong Kong Hong Kong SAR Centre for Intelligent Multidimensional Data Analysis Shatin Hong Kong SAR Institute of Nuclear Energy Safety Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

Remaining useful life (RUL) prediction is critical in prognostics and health management (PHM) applications, where the trend of data-driven approaches using operational data from complex systems to establish degradation processes has attracted increasing attention. In order to solve the problem of feature screening and life prediction in the remaining service life prediction of aero-engine, this paper proposes Max-Relevance and Min-Redundancy (mRMR) method to screen sensor features by calculating the mutual information between features, uses the advantage of LSTM network in processing time series data, constructs samples by time window sliding, and designs the RUL direct prediction framework of CNN-LSTM network. The validation experiments were carried out on the C-MPASS dataset. Experimental results show that compared with other single deep learning and traditional models, the proposed hybrid model has lower regression analysis error and degradation prediction error, and can obtain more accurate remaining useful life prediction results.

关键词： Degradation Deep learning Analytical models Time series analysis Predictive models Feature extraction data models

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High-Precision Stereo Matching with a Drone Equipped With Rtk-Gnss and a Single Camera for Plant Height Measurements

SSRN

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SSRN 2022年

关键词： Global positioning system

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A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

arXiv

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arXiv 2021年

作者： Peiris, Himashi Hayat, Munawar Chen, Zhaolin Egan, Gary Harandi, Mehrtash Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Melbourne Australia Monash University Melbourne Australia Department of Data Science & AI Faculty of IT Monash University Melbourne Australia

We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boundary refinement. Empirically, we show that the proposed design choices result in a computationally efficient model, with competitive and promising results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation (BraTS) Task. We further show that the representations learned by our model are robust against data corruptions. Our code implementation is publicly available. Copyright © 2021, The Authors. All rights reserved.

关键词： Tumors

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A Tendon-Driven Actuator with Cantilever Initiated Variable Stiffness Used for Robotic Fingers

SSRN

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SSRN 2024年

作者： Li, Chenning Yang, Shangkui Branson, David T. Song, Zhibin Sun, Tao Dai, Jiansheng Kang, Rongjie Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical Engineering Tianjin University Tianjin300072 China Advanced Manufacturing Technology Research Group Faculty of Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Beijing Institute of Technology China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen518055 China

Variable stiffness actuators (VSAs) have emerged as a key actuation technology known for their bionic performance and task adaptability. However, current VSAs often exhibit relatively large sizes, making them possible for use in robotic arms and legs but less convenient for integrations into robotic hands. This paper introduces a compact design of a tendon-driven variable stiffness actuator (TVSA) based on an adjustable cantilever mechanism, which can be embedded into a robotic finger. This implementation endows the robotic finger with the decoupled regulation of joint position and stiffness. A concise and computationally efficient stiffness mapping model from the TVSA to the finger joints is then established, providing a theoretical foundation for the stiffness regulation of the tendon-driven fingers. A prototype of a robotic hand equipped with the presented TVSA demonstrates safe interactions with various objects with diverse shapes, weights and stiffness. © 2024, The Authors. All rights reserved.

关键词： Stiffness

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Hyperspectral Brain Tissue Classification using a Fast and Compact 3D CNN Approach

Hyperspectral Brain Tissue Classification using a Fast and C...

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International Image Processing, Applications and systems Conference (IPAS)

作者： Hamail Ayaz David Tormey Ian McLoughlin Muhammad Ahmad Saritha Unnikrishnan Faculty of Engineering and Design and Mathematical Modelling and Intelligent Systems for Health and Environment Atlantic Technological University Sligo Ireland Faculty of Engineering and Design Centre for Precision Engineering Materials and Manufacturing Research (PEM) Atlantic Technological University Sligo Ireland Department of Computer Science and Applied Physics Atlantic Technological University Galway Ireland Department of Computer Science National University of Computer and Emerging Sciences Chiniot Pakistan

ISBN: (纸本)9781665462204

Glioblastoma (GB) is a malignant brain tumor and requires surgical resection. Although complete resection of GB improves prognosis, supratotal resection may cause neurological abnormalities. Therefore, intraoperative tissue classification techniques are needed to delineate infected tumor regions to remove reoccurrences. To delineate the affected regions, surgeons mostly rely on traditional magnetic resonance imaging (MRI) which often lacks accuracy and precision due to the brain-shift phenomenon. Hyperspectral Imaging (HSI) is a noninvasive advanced optical technique and has the potential to classify tissue cells accurately. However, HSI tumor classification is challenging due to overlapping regions, high interclass similarity, and homogeneous information. Additionally, HSI models using 2D Convolutional Neural Network (CNN) models works with spectral information eliminating spatial features and 3D followed by 2D hybrid model lacks abstract level spatial information. Therefore, in this study, we have used a minimal layer 3D CNN model to classify the GB tumor region from normal tissues using an intraoperative VivoHSI dataset. The HSI data have normal tissue (NT), tumor tissue (TT), hypervascularized tissue or blood vessels (BV), and background (BG) tissue cells. The proposed 3D CNN model consists of only two 3D layers using limited training samples (20%), which are further divided into 50% for training and 50% for validation and blind tested (80%) on the rest of the data. This study outperformed then state-of-the-art hybrid architecture by achieving an overall accuracy of 99.99%.

关键词： Training Solid modeling Three-dimensional displays Magnetic resonance imaging Surgery Brain modeling data models

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A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network

A Hybrid MTS Anomaly Detection Method Based on Reconstructio...

引用

data Driven Control and Learning systems (DDCLS)

作者： X. Yuan Ke Ning K. Wei Yan-Lin He Qun-Xiong Zhu Ming-Qing Zhang Yi Luo Yang Zhang College of Information Science and Technology Beijing University of Chemical Technology Beijing P. R. China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing P. R. China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P. R. China Research Institute of Mine Big Data Chinese Institute of Coal Science Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Effective anomaly detection of multivariate time series data is crucial for modern industrial applications. However, existing works usually ignore spatial and temporal dependencies of series. The data with complicated spatial-temporal feature put forward higher requirements for accurate detection of anomalies. In this paper, we propose an anomaly detection method for multivariate time series based on spatial-temporal network (STN). Firstly, the time series are reconstructed by Generative Adversarial Networks (GAN). The reconstruction block is used to amplify the influence of the abnormal state and prompt the anomalies more detectable, as poorly reconstructed data of abnormal state further worsen the quality of the final predicted value. Secondly, an adaptive learning graph structure layer is proposed. The graph structure is learned by adaptive method without any prior knowledge and is used for reconstructed data prediction. Thirdly, we use STN to extract spatial and temporal dependencies of series. Especially, attention mechanism is introduced into graph convolution network to enhance the prediction performance by focusing on more important neighbor node message. Finally, our model is tested on 3 public datasets to evaluate performance. The results demonstrate that our method performs better than baselines.

关键词： Learning systems Attention mechanisms Convolution Time series analysis Neural networks Focusing Generative adversarial networks

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