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An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

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An improved RL-based framework for multiple biomedical event...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhao, Yao Zhao, Weizhong Jiang, Xingpeng He, Tingting Su, Bianping Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Hubei Wuhan430079 China School of Computer Central China Normal University Hubei Wuhan430079 China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Hubei Wuhan430079 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China Xi'An University of Architecture and Technology Shanxi Xi'an China

ISBN: (纸本)9781665401265

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method. © 2021 IEEE.

关键词： Reinforcement learning

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

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arXiv 2024年

作者： Liu, Xiaoning Wu, Zongwei Li, Ao Vasluianu, Florin Zhang, Yulun Gu, Shuhang Zhang, Le Zhu, Ce Timofte, Radu Jin, Zhi Wu, Hongjun Wang, Chenxi Ling, Haitao Cai, Yuanhao Bian, Hao Zheng, Yuxin Lin, Jing Yuille, Alan Shao, Ben Guo, Jin Liu, Tianli Wu, Mohao Feng, Yixu Hou, Shuo Lin, Haotian Zhu, Yu Wu, Peng Dong, Wei Sun, Jinqiu Zhang, Yanning Yan, Qingsen Zou, Wenbin Yang, Weipeng Li, Yunxiang Wei, Qiaomu Ye, Tian Chen, Sixiang Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Zuo, Zhichao Wang, Mingshen Wang, Junhu Wei, Yanyan Sun, Xiaopeng Gao, Yu Huang, Jiancheng Chen, Hongming Chen, Xiang Tang, Hui Chen, Yuanbin Zhou, Yuanbo Dai, Xinwei Qiu, Xintao Deng, Wei Gao, Qinquan Tong, Tong Li, Mingjia Hu, Jin He, Xinyu Guo, Xiaojie Sabarinathan Uma, K. Sasithradevi, A. Sathya Bama, B. Mohamed Mansoor Roomi, S. Srivatsav, V. Wang, Jinjuan Sun, Long Chen, Qiuying Shao, Jiahong Zhang, Yizhi Conde, Marcos V. Feijoo, Daniel Benito, Juan C. García, Alvaro Lee, Jaeho Kim, Seongwan Sharif, S.M.A. Khujaev, Nodirkhuja Tsoy, Roman Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Malagi, Sampada Joshi, Amogh Akalwadi, Nikhil Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Lian, Wenyi Lian, Wenjing Kalyanshetti, Jagadeesh Aralikatti, Vijayalaxmi Ashok Yashaswini, Palani Upasi, Nitish Hegde, Dikshit Patil, Ujwala Sujata, C. Yan, Xingzhuo Hao, Wei Fu, Minghan Choksy, Pooja Sarvaiya, Anjali Upla, Kishor Raja, Kiran Yan, Hailong Zhang, Yunkai Li, Baiang Zhang, Jingyi Zheng, Huan University of Electronic Science and Technology of China China Computer Vision Lab University of Würzburg Germany Shanghai Jiao Tong University China Computer Vision Lab ETH Zurich Switzerland School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Johns Hopkins University United States Tsinghua University China Zhejiang Dahua Technology Co. Ltd. China School of Computer Science Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China School of Computer Science Xi'an University of Architecture and Technology China South China University of Technology China Fuzhou University China Chengdu University of Information Technology China Hong Kong University of Science and Technology Guangzhou China Hefei University of Technology China Individual Researcher Shenyang Aerospace University China Nanjing University of Science and Technology China Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China Tianjin University China Couger Inc Japan Sasi Institute of Technology & Engineering India Vellore Institute of Technology India Thiagarajar college of engineering India Coventry University United Kingdom Cidaut AI CVLab University of Wuerzburg Germany Opt-AI University Teknologi Malaysia Kuala Lumpur Malaysia Universiti Teknologi Malaysia Kuala Lumpur Malaysia KLE Technological University Karnataka Hubballi India School of Electronics and Communication Engineering KLE Technological University Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Uppsala University Sweden Northeastern University United States Bosch Investment Ltd. China Fortinet Inc. University of Saskatchewan Canada Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technolog

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field. © 2024, CC BY.

关键词： Image enhancement

A systematic mapping study in AIOps

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arXiv 2020年

作者： Notaro, Paolo Cardoso, Jorge Gerndt, Michael Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany Ultra-scale AIOps Lab Huawei Munich Research Center Germany Department of Informatics Engineering/CISUC University of Coimbra Portugal

IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and Big Data. However, past AIOps contributions are scattered, unorganized and missing a common terminology convention, which renders their discovery and comparison impractical. In this work, we conduct an in-depth mapping study to collect and organize the numerous scattered contributions to AIOps in a unique reference index. We create an AIOps taxonomy to build a foundation for future contributions and allow an efficient comparison of AIOps papers treating similar problems. We investigate temporal trends and classify AIOps contributions based on the choice of algorithms, data sources and the target components. Our results show a recent and growing interest towards AIOps, specifically to those contributions treating failure-related tasks (62%), such as anomaly detection and root cause analysis. © 2020, CC BY.

关键词： Artificial intelligence

Fault Tolerance Research of Visual Convolutional Neural Networks Based on Soft Errors

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Fault Tolerance Research of Visual Convolutional Neural Netw...

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Xiaotong Xu Yuzhuo Fu Ting Liu Hongjun You Intelligent Computer Architecture Technology(iCAT) Lab of School of Microelectronics Shanghai JiaoTong University Shanghai China Key Laboratory of Intelligent Computing Technology (SAST) Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

Injecting faults to the system architecture layer and studying the upper neural network for fault tolerancehe is difficult and time-consuming. This paper proposes an automatic method covering time and space, which can inject faults into the processor on the Simics simulation platform, simulating soft errors, and then collect the time sequence data of the system architecture layer and the observed node data of visual convolutional neural networks program layer. At the same time, combined with the relevant standards, the GAN classifier is used to calibrate the different fault models after converting time sequence data into time sequence images. Finally, the Bayesian network is used to form the path of fault propagation from the architecture layer to the program layer and the result layer. After intensive fault injection into critical registers, the probability of neural network failure caused by soft errors is effectively stimulated.

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Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

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arXiv 2022年

作者： Ignatov, Andrey Timofte, Radu Chiang, Cheng-Ming Kuo, Hsien-Kai Xu, Yu-Syuan Lee, Man-Yu Lu, Allen Cheng, Chia-Ming Chen, Chih-Cheng Yong, Jia-Ying Shuai, Hong-Han Cheng, Wen-Huang Jia, Zhuang Xu, Tianyu Zhang, Yijian Bao, Long Sun, Heng Zhang, Diankai Gao, Si Liu, Shaoli Wu, Biao Zhang, Xiaofeng Zheng, Chengjian Lu, Kaidi Wang, Ning Sun, Xiao Wu, HaoDong Liu, Xuncheng Zhang, Weizhan Yan, Caixia Du, Haipeng Zheng, Qinghua Wang, Qi Chen, Wangdu Duan, Ran Duan, Ran Sun, Mengdi Zhu, Dan Chen, Guannan Cho, Hojin Kim, Steve Yue, Shijie Li, Chenghua Zhuge, Zhengyang Chen, Wei Wang, Wenxu Zhou, Yufeng Cai, Xiaochen Cai, Hengxing Xu, Kele Liu, Li Cheng, Zehua Lian, Wenyi Lian, Wenjing Computer Vision Lab ETH Zurich Switzerland AI Witchlabs Switzerland University of Wuerzburg Germany MediaTek Inc. Taiwan National Yang Ming Chiao Tung University Taiwan Video Algorithm Group Camera Department Xiaomi Inc. China Audio & Video Technology Platform Department ZTE Corp. China China School of Computer Science and Technology Xi'an Jiaotong University China MIGU Video Co. Ltd China BOE Technology Group Co. Ltd. China GenGenAI Korea Republic of North China University of Technology China Institute of Automation Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology China 4Paradigm Inc. Beijing China National University of Defense Technology Changsha China University of Oxford Oxford United Kingdom Uppsala University Sweden Northeastern University China

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of lowbitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt/30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

Orion: A Technique to Prune State Space Search Directions for Guidance-Based Formal Verification

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Orion: A Technique to Prune State Space Search Directions fo...

Asian Test Symposium (ATS)

作者： V.S. Vineesh Binod Kumar Rushikesh Shinde Akshay Jaiswal Harsh Bhargava Virendra Singh Computer Architecture and Dependable Systems Lab IIT Bombay Qualcomm India Private Limited Bengaluru

ISBN: (数字)9781728126951

ISBN: (纸本)9781728126968

Model checking of large designs is a challenging task because of different scalability issues. In this paper, we aim to utilize guided state space traversal to address this issue. However, providing guidance for state space traversal of complex designs is also an equally challenging problem. We adopt a simulation-based strategy combined with Bayesian modelling approach for finding effective guidance hints for state space traversal. A heuristic-based structural dependency of the design yields ineffective guidance hints which need further of filtering. To prune out the ineffective guidance hints, we first generate module-level sub-properties from static analysis of the design. These sub-properties and structural dependency-based guidance hints are analyzed in simulation traces generated from the constrained-random test benches. These conditional occurrence of sub-properties and guidance hints are inputs to a Bayesian model which can then provide us the guidance hints with the highest profitability. With the proposed methodology, we succeed in pruning out the set of unprofitable guidance hints and obtain effective search directions which are then used to assist the model checking procedure. Experiments on two complex designs for different properties show the effectiveness of the proposed methodology in reducing CPU time during model checking.

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A review of design guidelines for clinical auditory alarms

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A review of design guidelines for clinical auditory alarms

International Conference on Healthcare Ergonomics and Patient Safety, HEPS 2019

作者： Vieira, Joana Santos, Jorge Almeida Noriega, Paulo CIAUD Research Centre for Architecture Urbanism and Design Lisbon School of Architecture Universidade de Lisboa Lisbon Portugal CCG: Centre for Computer Graphics Guimarães Portugal Ergonomics & Human Factors Group ALGORITMI Research Centre University of Minho Guimarães Portugal School of Psychology University of Minho Braga Portugal ErgoUX Ergonomics and UX Lab Lisbon School of Architecture Universidade de Lisboa Lisbon Portugal

ISBN: (纸本)9783030240660

The global medical safety standard IEC 60601-1-8 includes general requirements for the design of auditory alarms for medical equipment. This document was last amended in 2012 and is currently being updated to be released in the end of 2019. The update includes major changes in the auditory warning signals design guidelines. One of the most important is the fact that it will allow stakeholders to design different versions of the same sound. It is thus relevant to systematize the thorough and robust body of knowledge developed in recent years regarding the design of clinical auditory alarms. This paper identifies the reported problems with clinical auditory alarms;revisits early and current audio design guidelines;and systematizes recommendations or good-practices on healthcare noise, considering spectral, temporal and spatial characteristics of auditory alarms. All content is based on standards and/or experimental studies on detection, learning, subjective perception, or spatial localization. Its aim is to provide a practical tool for designers to design better clinical auditory alarms. © 2019, Springer Nature Switzerland AG.

关键词： Design

A Review on Current and Old SCADA Networks Applied to Water Distribution Systems 1

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A Review on Current and Old SCADA Networks Applied to Water ...

1st International Conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Franco, Daniel Jose Muhammed, Abdullah Bin Subramaniam, Shamala K. Abdullah, Azizol Silva, Rui Miguel Akram, Omar Khasro University Putra Malaysia Department of Communication Technology and Network Serdang Malaysia Lab Ubinet Computer Security and Cybercrime Polytechnic Institute of Beja Department of Engineering Beja Portugal University Putra Malaysia Department of Architecture Serdang Malaysia

ISBN: (纸本)9781728144870

Water is probably the oldest factor receiving attention from the population, since the beginning of the human race. The evolution of water supply systems is directly related to the urban growth of the cities and their geographical location. This study focuses on SCADA (Supervisory Control And Data Acquisition) systems used to control and managing water distribution systems all around the world, highlighting their components and deeply approaching SCADA networks and communication protocols. Based on the need of protection and security on SCADA networks, this study aims to provide a review of the existing literature, through the case study of water distribution systems, identifying the different components of a SCADA system, as well as its communication architectures and protocols and some known attacks, to design a framework on current and old SCADA networks. Results show that SCADA systems are not just applied to water distribution and water waste systems, but also to many other industrial automated systems that control on crucial systems. The security of such systems is yet weak and faces many vulnerabilities and threats, where security mechanisms must be applied. For this purpose, the study of SCADA networks and communications is considered a crucial point for the correct development of security tools. © 2019 IEEE.

关键词： SCADA systems

NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

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arXiv 2019年

作者： Gutierrez-Galan, Daniel Dominguez-Morales, Juan P. Perez-Peña, Fernando Linares-Barranco, Alejandro Robotics and Computer Technology Lab Universidad de Sevilla Spain Department of Computer Architecture and Technology Universidad de Cádiz Spain

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks