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Transferable persona-grounded dialogues via grounded minimal edits

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arXiv 2021年

作者： Wu, Chen Henry Zheng, Yinhe Mao, Xiaoxi Huang, Minlie Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Fuxi Ai Lab NetEase Inc. Hangzhou China

Grounded dialogue models generate responses that are grounded on certain concepts. Limited by the distribution of grounded dialogue data, models trained on such data face the transferability challenges in terms of the data distribution and the type of grounded concepts. To address the challenges, we propose the grounded minimal editing framework, which minimally edits existing responses to be grounded on the given concept. Focusing on personas, we propose Grounded Minimal Editor (GME), which learns to edit by disentangling and recombining persona-related and persona-agnostic parts of the response. To evaluate persona-grounded minimal editing, we present the PERSONAMINEDIT dataset, and experimental results show that GME outperforms competitive baselines by a large margin. To evaluate the transferability, we experiment on the test set of BLENDEDSKILLTALK and show that GME can edit dialogue models' responses to largely improve their persona consistency while preserving the use of knowledge and empathy. © 2021, CC BY.

关键词： Large dataset

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction 23

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Towards an Interaction Architecture for the iCub Robot: Soci...

23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Shaghaghi, Sahand Aliasghari, Pourya Tripp, Bryan Anderson, Britt Dautenhahn, Kerstin Nehaniv, Chrystopher L. Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Psychology University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350373578

Gaze behaviour plays a crucial role in social interactions extending to Human-Robot Interactions. Humanoid robots equipped with anthropomorphic vision systems may be expected to employ natural gaze behaviour in interactions with humans. Here, we implemented a novel first-person human gaze state detection tool on the iCub robot and assessed its gaze detection accuracy. Using this tool, we then developed two social gaze interaction architectures for the iCub robot. The Social Gaze Space theory (SGS) of Jording and colleagues was the theoretical framework for building our first architecture, SGS Base (SGS-B). Our second architecture, SGS Interaction Architecture (SGS-IA), is the extended version of SGS Base that lets the robot take on different higher-level goals in an interaction, including being attentive to the interaction partner or attempting to direct their attention to an object. These architectures support dyadic social interactions involving initiating and responding to joint attention towards objects of interest, and are expected to allow the robot to interact in a more human-like manner in social interactions. The gaze detection accuracy of the iCub robot equipped with our choice of algorithms exceeded that of previously implemented methods. System validation trials confirmed that the robot detects the social gaze states correctly in the majority of the instances, allowing for behavioural control according to the Social Gaze Space theory. Validation tests presented in this article demonstrate the functionality of the SGS Interaction Architecture and also highlight the differences in personal interaction dynamics when the humanoid robot has no particular goals as opposed to when it has interactional goals appropriate to a teaching scenario. © 2024 IEEE.

关键词： Anthropomorphic robots

Consistency and Diversity induced Human Motion Segmentation

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arXiv 2022年

作者： Zhou, Tao Fu, Huazhu Gong, Chen Shao, Ling Porikli, Fatih Ling, Haibin Shen, Jianbing The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of High Performance Computing Agency for Science Technology and Research Singapore Riyadh Saudi Arabia The Research School of Engineering The Australian National University Australia The Department of Computer Science Stony Brook University Strony Brook NY United States The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University United States

Subspace clustering is a classical technique that has been widely used for human motion segmentation and other related tasks. However, existing segmentation methods often cluster data without guidance from prior knowledge, resulting in unsatisfactory segmentation results. To this end, we propose a novel Consistency and Diversity induced human Motion Segmentation (CDMS) algorithm. Specifically, our model factorizes the source and target data into distinct multi-layer feature spaces, in which transfer subspace learning is conducted on different layers to capture multi-level information. A multi-mutual consistency learning strategy is carried out to reduce the domain gap between the source and target data. In this way, the domain-specific knowledge and domain-invariant properties can be explored simultaneously. Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance. Moreover, to preserve the temporal correlations, an enhanced graph regularizer is imposed on the learned representation coefficients and the multi-level representations of the source data. The proposed model can be efficiently solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experimental results on public human motion datasets demonstrate the effectiveness of our method against several state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion analysis

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report 17th

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Efficient and Accurate Quantized Image Super-Resolution on...

17th European Conference on computer vision, ECCV 2022

作者： Ignatov, Andrey Timofte, Radu Denna, Maurizio Younes, Abdel Gankhuyag, Ganzorig Huh, Jingang Kim, Myeong Kyun Yoon, Kihwan Moon, Hyeon-Cheol Lee, Seungho Choe, Yoonsik Jeong, Jinwoo Kim, Sungjei Smyl, Maciej Latkowski, Tomasz Kubik, Pawel Sokolski, Michal Ma, Yujie Chao, Jiahao Zhou, Zhou Gao, Hongfan Yang, Zhengfeng Zeng, Zhenbing Zhuge, Zhengyang Li, Chenghua Zhu, Dan Sun, Mengdi Duan, Ran Gao, Yan Kong, Lingshun Sun, Long Li, Xiang Zhang, Xingdong Zhang, Jiawei Wu, Yaqi Pan, Jinshan Yu, Gaocheng Zhang, Jin Zhang, Feng Ma, Zhe Wang, Hongbin Cho, Hojin Kim, Steve Li, Huaen Ma, Yanbo Luo, Ziwei Li, Youwei Yu, Lei Wen, Zhihong Wu, Qi Fan, Haoqiang Liu, Shuaicheng Zhang, Lize Zong, Zhikai Kwon, Jeremy Zhang, Junxi Li, Mengyuan Fu, Nianxiang Ding, Guanchen Zhu, Han Chen, Zhenzhong Li, Gen Zhang, Yuanfan Sun, Lei Zhang, Dafeng Yang, Neo Liu, Fitz Zhao, Jerry Ayazoglu, Mustafa Bilecen, Bahri Batuhan Hirose, Shota Arunruangsirilert, Kasidis Ao, Luo Leung, Ho Chun Wei, Andrew Liu, Jie Liu, Qiang Yu, Dahai Li, Ao Luo, Lei Zhu, Ce Hong, Seongmin Park, Dongwon Lee, Joonhee Lee, Byeong Hyun Lee, Seunggyu Chun, Se Young He, Ruiyuan Jiang, Xuhao Ruan, Haihang Zhang, Xinjian Liu, Jing Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Computer Vision Lab ETH Zürich Zürich Switzerland AI Witchlabs Zürich Switzerland University of Würzburg Würzburg Germany Synaptics Europe Lausanne Switzerland Seongnam Korea Republic of Yonsei University Seoul Korea Republic of TCL Research Europe Warsaw Poland East China Normal University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China BOE Technology Group Co. Ltd. Beijing China Nanjing University of Science and Technology Nanjing China Ant Group Hangzhou China GenGenAI Seoul Korea Republic of Hefei University of Technology Hefei China Megvii Technology Beijing China Chengdu China Xidian University Xi’an China Qingdao Hi-image Technologies Co. Ltd. Qingdao China Seoul Korea Republic of Wuhan University Wuhan China Beijing China Samsung Research Beijing China Aselsan Corporation Ankara Turkey Waseda University Tokyo Japan TCL Corporate Research Hong Kong Intelligent Computational Imaging Lab Seoul National University Seoul Korea Republic of Bilibili Inc. Shanghai China Shanghai Jiao Tong University Shanghai China Assiut University Asyut Egypt

ISBN: (纸本)9783031250651

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image enhancement

A benchmark of recent population-based metaheuristic algorithms for multi-layer neural network training 20

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A benchmark of recent population-based metaheuristic algorit...

2020 Genetic and Evolutionary Computation Conference, GECCO 2020

作者： Mousavirad, Seyed Jalaleddin Schaefer, Gerald Jalali, Seyed Mohammad Jafar Korovin, Iakov Nature-inspired Computational Intelligence Lab Ontario Tech University Oshawa Canada Department of Computer Science Loughborough University Loughborough United Kingdom Institute for Intelligent Systems Research and Innovation Deakin University Geelong Australia Souther Federal University Taganrog Russia

ISBN: (纸本)9781450371278

Multi-layer neural networks (MLNNs) are extensively used in many industrial applications. Training is the crucial task for MLNNs. While gradient descent-based approaches are most commonly employed here, they suffer from drawbacks such as getting stuck in local optima. One approach to address this is to employ population-based metaheuristic algorithms. Since a variety of such algorithms have been proposed in the literature, in this paper we benchmark the performance of 15 metaheuristic algorithms, including state-of-the-art as well as some of the most recent algorithms, for neural network training. In particular, we evaluate particle swarm optimisation (PSO), differential evolution (DE), artificial bee colony (ABC), imperialist competitive algorithm (ICA), cuckoo search (CS), gravitational search algorithm (GSA), bat algorithm (BA), firefly algorithm (FA), grey wolf optimiser (GWO), ant lion optimiser (ALO), dragonfly algorithm (DA), sine cosine algorithm (SCA), whale optimisation algorithm (WOA), grasshopper optimisation algorithm (GOA), and salp swarm algorithm (SSA), and assess their performance on different classification algorithms. © 2020 ACM.

关键词： Multilayer neural networks

Rethinking natural adversarial examples for classification models

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arXiv 2021年

作者： Li, Xiao Li, Jianmin Dai, Ting Shi, Jie Zhu, Jun Hu, Xiaolin Beijing National Research Center for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Trustworthy AI Lab of Shield Lab at Huawei Singapore Research Center Singapore

Recently, it was found that many real-world examples without intentional modifications can fool machine learning models, and such examples are called "natural adversarial examples". ImageNet-A is a famous dataset of natural adversarial examples. By analyzing this dataset, we hypothesized that large, cluttered and/or unusual background is an important reason why the images in this dataset are difficult to be classified. We validated the hypothesis by reducing the background influence in ImageNet-A examples with object detection techniques. Experiments showed that the object detection models with various classification models as backbones obtained much higher accuracy than their corresponding classification models. A detection model based on the classification model EfficientNet-B7 achieved a top-1 accuracy of 53.95%, surpassing previous state-of-the-art classification models trained on ImageNet, suggesting that accurate localization information can significantly boost the performance of classification models on ImageNet-A. We then manually cropped the objects in images from ImageNet-A and created a new dataset, named ImageNet-A-Plus. A human test on the new dataset showed that the deep learning-based classifiers still performed quite poorly compared with humans. Therefore, the new dataset can be used to study the robustness of classification models to the internal variance of objects without considering the background disturbance. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater’s Condition

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arXiv 2023年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize network regret and cumulative constraint violation. A novel distributed online algorithm is proposed and it achieves an O(T max{c,1−c}) network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the number of rounds and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T 1−c). Moreover, if the loss functions are strongly convex, then the network regret bound is reduced to O(log(T)), and the network cumulative constraint violation bound is reduced to O(√log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is the first to achieve reduced (network) cumulative constraint violation bounds for (distributed) online convex optimization with adversarial constraints under Slater’s condition. Finally, the theoretical results are verified through numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

Using artificial intelligence to predict the next deceptive movement based on video sequence analysis: A case study on a professional cricket player's movements

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Journal of Engineering Research (Kuwait)

Journal of Engineering research (Kuwait) 2025年

作者： Mutawa, A.M. Rajesh Kumar, Korupalli V. K, Hemachandran Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis Perlis 02600 Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70 % accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports. © 2025 The Authors

关键词： Artificial intelligence Cricket Deceptive movements Machine learning Performance optimization