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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

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Compartmental spiking neuron model for pattern classification 3

Compartmental spiking neuron model for pattern classificatio...

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3rd international Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Astapova, L.A. Korsakov, A.M. Bakhshiev, A.V. Eremenko, E.A. Smirnova, E.Y. Saint-Petersburg Russia Institute of Machinery Mate-rials and Transport Higher School of Automation and Robotics Saint-Petersburg Russia

One of the directions of development within the framework of the neuromorphic approach is the development of anatomically similar models of brain networks, taking into account the structurally complex structure of neurons and the adaptation of connections between them, as well as the development of learning algorithms for such models. In this work, we use the previously presented compartmental spike model of a neuron, which describes the structure (dendritic tree, soma, synapses) and behaviour (temporal and spatial signal summation, generation of action potential, stimulation and suppression of electrical activity) of a biological neuron. An algorithm for the structural organization of neuron models into a spike neural network is proposed for recognizing an arbitrary impulse pattern by introducing inhibitory synapses between trained neuron models. The dynamically adapting neuron models used are trained according to a previously proposed algorithm that automatically selects parameters such as soma size, dendrite length, and the number of synapses on each of the dendrites in order to induce a temporal response at the output depending on the input pattern encoded using a time window and temporal delays in the vector of single spikes arriving at a separate dendrite of a neuron. The developed algorithms are evaluated on the Iris dataset classification problem with four training examples from each class. As a result of the classification, separate disjoint clusters are formed, which demonstrates the applicability of the proposed spike neural network with a dynamically changing structure of elements in the problem of pattern recognition and classification. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Neurons

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Exploring Mode Connectivity for Pre-trained Language Models

Exploring Mode Connectivity for Pre-trained Language Models

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2022 conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Qin, Yujia Qian, Cheng Yi, Jing Chen, Weize Lin, Yankai Han, Xu Liu, Zhiyuan Sun, Maosong Zhou, Jie NLP Group DCST IAI BNRIST Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China International Innovation Center of Tsinghua University Shanghai China Quan Cheng Laboratory China Pattern Recognition Center WeChat AI Tencent Inc

Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found. Although plenty of works have studied how to effectively and efficiently adapt PLMs to high-performance minima, little is known about the connection of various minima reached under different adaptation configurations. In this paper, we investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path. We conduct empirical analyses to investigate three questions: (1) how could hyperparameters, specific tuning methods, and training data affect PLM's mode connectivity? (2) How does mode connectivity change during pretraining? (3) How does the PLM's task knowledge change along the path connecting two minima? In general, exploring the mode connectivity of PLMs conduces to understanding the geometric connection of different minima, which may help us fathom the inner workings of PLM downstream adaptation. The codes are publicly available at https://***/thunlp/Mode-Connectivity-PLM. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans 19th

Joint Global ICP for Improved Automatic Alignment of Full Tu...

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19th international conference on Computer analysis of Images and patterns (CAIP)

作者： Fetzer, Torben Reis, Gerd Stricker, Didier Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany

ISBN: (纸本)9783030891275;9783030891282

Point cloud registration is an important task in computer vision, computer graphics, robotics, odometry and many other disciplines. The problem has been studied for a long time and many different approaches have been established. In the case of existing rough initializations, the ICP approach is still widely used as the state of the art. Often only the pairwise problem is treated. In case of many applications, especially in 3D reconstruction, closed rotations of sequences of partial reconstructions have to be registered. We show that there are considerable advantages if ICP iterations are performed jointly instead of the usual pairwise approach (Pulli's approach). Without the need for increased computational effort, lower alignment errors are achieved, drift is avoided and calibration errors are uniformly distributed over all scans. The joint approach is further extended into a global version, which not only considers one-sided adjacent scans, but updates symmetrically in both directions. The result is an approach that leads to a much smoother and more stable convergence, which moreover enables a stable stopping criterion to be applied. This makes the procedure fully automatic and therefore superior to most other methods, that often tremble close to the optimum and have to be terminated manually. We present a complete procedure, which in addition addresses the issue of automatic outlier detection in order to solve the investigated problem data independently without any user interaction.

关键词： Point cloud registration Automatic alignment Drift reduction

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A novel soft exosuit based on biomechanical analysis for assisting lower extremity 22

A novel soft exosuit based on biomechanical analysis for ass...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Liu, Youfu Chen, Chunjie Lu, Jun Liu, Yida Zhang, Yu Wang, Zhuo College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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Designing Path of Collision Avoidance for Mobile Manipulator in Worker Safety Monitoring System Using Reinforcement Learning

Designing Path of Collision Avoidance for Mobile Manipulator...

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IEEE international conference on intelligence and Safety for robotics (ISR)

作者： Jiwoong Lim Jihyun Lee Changjoo Lee Gunwoo Kim Younghoon Cha Joonhyung Sim Sungsoo Rhim Kyung Hee University Yongin-si Gyeonggi-do Republic of Korea

Mobile manipulator is a robot for the next generation of processes. The mobile manipulator can increase process efficiency by being combined with the collaborative robot system. However, due to worker safety issues, simultaneous control of the mobile manipulator is still difficult. In this paper, we present the designing path of collision avoidance of a mobile manipulator for the safety of workers. By monitoring the position and velocity of a worker modeled as a cylinder, the mobile manipulator designs a path to avoid the worker using reinforcement learning. We learned the robot using simulation, and we checked the mobile and manipulator move to the destination by avoiding the worker.

关键词： Robot control Reinforcement learning Manipulators Stability analysis Safety Mobile robots Collision avoidance

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ASMMC21: The 6th international Workshop on Affective Social Multimedia Computing 21

ASMMC21: The 6th International Workshop on Affective Social ...

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23rd ACM international conference on Multimodal Interaction, ICMI 2021

作者： Huang, Dongyan Schuller, Björn Tao, Jianhua Xie, Lei Yang, Jie Ubtech Robotics United States Ieee United States National Laboratory of Pattern Recognition China Northwestern Polytechnical University China Iis Nsf United States

ISBN: (纸本)9781450384810

Affective social multimedia computing is an emergent research topic for both affective computing and multimedia research communities. Social multimedia is fundamentally changing how we communicate, interact, and collaborate with other people in our daily lives. Comparing with well-organized broadcast news and professionally made videos such as commercials, TV shows, and movies, social multimedia media computing imposes great challenges to research communities. Social multimedia contains much affective information. Effective extraction of affective information from social multimedia can greatly help social multimedia computing (e.g., processing, index, retrieval, and understanding). Although much progress have been made in traditional multimedia research on multimedia content analysis, indexing, and retrieval based on subjective concepts such as emotion, aesthetics, and preference, affective social multimedia computing is a new research area. The affective social multimedia computing aims to proceed affective information from social multi-media. For massive and heterogeneous social media data, the research requires multidisciplinary understanding of content and perceptual cues from social multimedia. This workshop served as a successful step towards this goal and attracted contributions from different research disciplines on the analysis of affective signals in interaction (multimodal analyses enabling artificial agents in Human-Machine Interaction, social Interaction with artificial agents) and social multimedia (e.g., twitter, wechat, weibo, youtube, facebook, etc). This paper provides a summary of the activities of the workshop and the accepted papers and abstracts. © 2021 Owner/Author.

关键词： Social networking (online)

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1st international conference on Applied intelligence and Informatics, AII 2021

1st International Conference on Applied Intelligence and Inf...

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1st international conference on Applied intelligence and Informatics, AII 2021

ISBN: (纸本)9783030822682

The proceedings contain 30 papers. The special focus in this conference is on Applied intelligence and Informatics. The topics include: Key Techniques and Challenges for Processing of Heart Sound Signals;enhanced Signal Processing Using Modified Cyclic Shift Tree Denoising;a Machine Learning Driven Android Based Mobile Application for Flower Identification;a Generative Text Summarization Model Based on Document Structure Neural Network;human Gender Detection from Facial Images Using Convolution Neural Network;few-Shot Learning for Tamil Handwritten Character Recognition Using Deep Siamese Convolutional Neural Network;A CNN Based Model for Venomous and Non-venomous Snake Classification;recognition of Dysfluency in Speech: A Bidirectional Long-Short Term Memory Based Approach;distributed Denial of Service Attack Detection Using Machine Learning and Class Oversampling;glaucoma Detection Using Inception Convolutional Neural Network V3;scientific Metrological analysis of Government Services Based on Big Data analysis and Visualization Software Driven by Information Technology;violent Video Event Detection: A Local Optimal Oriented pattern Based Approach;human Age Estimation Using Deep Learning from Gait Data;an Error Resilient Video Transmission in Ad Hoc Network Using Error Diffusion Block Truncation Coding;ALO: AI for Least Observed People;COVID-Hero: Machine Learning Based COVID-19 Awareness Enhancement Mobile Game for Children;Literature Classification Model of Deep Learning Based on BERT-BiLSTM——Taking COVID-19 as an Example;identifying Relevant Stakeholders in Digital Healthcare;COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model;mixed Bangla-English Spoken Digit Classification Using Convolutional Neural Network;iConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis.

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IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data

IRIS: Implicit Reinforcement without Interaction at Scale fo...

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IEEE international conference on robotics and Automation (ICRA)

作者： Mandlekar, Ajay Ramos, Fabio Boots, Byron Savarese, Silvio Li Fei-Fei Garg, Animesh Fox, Dieter Stanford Vis & Learning Lab Stanford CA 94305 USA NVIDIA Santa Clara CA 95051 USA Univ Sydney Sydney NSW Australia Univ Washington Seattle WA 98195 USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728173955

Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. However, leveraging a fixed batch of data can be problematic for larger datasets and longer-horizon tasks with greater variations. The data can exhibit substantial diversity and consist of suboptimal solution approaches. In this paper, we propose Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets. IRIS factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low-level and selectively combines parts of suboptimal solutions leading to more successful task completions. We evaluate IRIS across three datasets, including the RoboTurk Cans dataset collected by humans via crowdsourcing, and show that performant policies can be learned from purely offline learning. Additional results at https://***/***/iris/.

关键词： Task analysis Robots Trajectory Iris Learning (artificial intelligence) Iris recognition Grasping

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26th international conference on Conceptual Structures, ICCS 2021

26th International Conference on Conceptual Structures, ICCS...

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26th international conference on Conceptual Structures, ICCS 2021

ISBN: (纸本)9783030869816

The proceedings contain 16 papers. The special focus in this conference is on Conceptual Structures. The topics include: Packing Problems, Dimensions and the Tensor Product of Complete Lattices;random Generation of a Locally Consistent Spatio-Temporal Graph;mining Contextual Rules to Predict Asbestos in Buildings;Temporal Sequence Mining Using FCA and GALACTIC;restricted Bi-pattern Mining;a Semantic-Based Approach for Assessing the Impact of Cyber-Physical Attacks: A Healthcare Infrastructure Use Case;nested Conceptual Graphs for Information Fusion Traceability;generating Layered Enterprise Architectures with Conceptual Structures;an Approach to Identifying the Most Predictive and Discriminant Features in Supervised Classification Problems;combining Implications and Conceptual analysis to Learn from a Pesticidal Plant Knowledge Base;ranking Schemas by Focus: A Cognitively-Inspired Approach;improving the Performance of Lindig-Style Algorithms with Empty Intersections;quantifying the Conceptual Error in Dimensionality Reduction;conceptual Relevance Index for Identifying Actionable Formal Concepts.

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Event Extraction in Vertical Domain Based on Similar Semantics and Dependency Syntax 22

Event Extraction in Vertical Domain Based on Similar Semanti...

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Proceedings of the 2022 5th international conference on Artificial intelligence and pattern Recognition

作者： Ying Cui Xiang Dai Lican Dai Second laboratory Southwest China Institute of Electronic Technology China

ISBN: (纸本)9781450396899

Event extraction has become an unavoidable bottleneck in the process of event map construction and event analysis and prediction. The accuracy and completeness of the extracted determine the quality of event knowledge map construction and the credibility of event prediction. Aiming at the difficulties of extracting events based on traditional pattern matching, the low recall rate and accuracy rate, and the low accuracy of event extraction based on in-depth learning method in specific fields, this paper proposes a method for extracting news events in vertical domain based on similar semantics and dependency syntax, and the algorithm is verified by taking the field of political diplomacy as an example. By calculating the similarity between the trigger word set and the Syntax descriptor, the trigger word list is extended to lay a foundation for identifying event types accurately. Further, based on the guidance of pattern, through the construction of meta-event template, we can recognize and extract events in specific domain, so as to achieve a structured description of events. The accuracy of extraction results is obviously better than that of end-to-end event extraction model based on deep neural network, and it can be used for reference and implementation in other specific fields. Firstly, this paper makes theoretical and practical research on news sentence level meta-event extraction, and introduces in detail the methods of event extraction in political diplomacy based on similar semantics and dependency syntax. Finally, we summarized the main difficulties in event extraction, and discussed the application prospects of event extraction.

关键词： Event Extraction Meta-event pattern matching Political diplomacy areas Syntax Vertical domain

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