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iscience 2025年第6期28卷

作者： Wenjun Wang Zhenyuan Wang Qingkun Li Yizi Su Chao Zeng Bo Cheng State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Tsinghua University Beijing 100084 China Beijing Key Laboratory of Human–Computer Interaction Institute of Software Chinese Academy of Sciences Beijing 100190 China Automotive Software Innovation Center (Chongqing) Chongqing 401331 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Hami Vocational and Technical College Hami 839001 China

Level 3 automated driving systems require drivers to occasionally retake control, making it crucial to understand factors influencing takeover performance. This study investigates how single versus multiple non-driving-related tasks (NDRTs) affect driver behavior, perceptions, and takeover capability. Using a high-fidelity driving simulator with 32 participants, conditions varying NDRT types and task-switching frequency were examined. Results indicate that diverse NDRT engagement and frequent task switching significantly influence takeover performance. Moreover, our decision tree models highlighted the number of NDRT types and task-switching frequency as critical predictors of driver response effectiveness. These findings underscore the importance of accounting for task engagement diversity and switching behaviors in automated vehicle system design. This study provides practical implications for optimizing human-automation interactions and enhancing the safety of automated driving systems by managing drivers’ attentiveness for their in-cabin activities.

关键词： Applied sciences computer science Artificial intelligence

A Practical and High-Performance Nitrogen Dioxide Gas Sensor that Can Be Rapidly Mass-Produced Based on Gallium Nitride Quantum Dots Film

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SSRN

SSRN 2022年

作者： Han, Dan Chen, Yi Li, Donghui Dong, Hailiang Xu, Bingshe Sang, Shengbo He, Xiuli Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

Gallium nitride (GaN) has some challenges for gas sensing, such as high detection lower limit (LOD) or controlling the epitaxy of nanostructures. To address these issues, we exclusively designed a device to enhance the gas interaction capabilities of GaN quantum dots (QDs) film fabricated by metal-organic chemical vapor deposition (MOCVD). The GaN QDs film gas sensor exhibited a low LOD (5 ppb) to NO2 at room temperature (RT), which has much superior gas performance than other GaN-based sensors. The results manifest that many ultra-small QDs around 2 nm in diameter are stacked to form QDs of larger size, and confirm the existence of vacancies. The sensor displays a ultra-high electron mobility of 1237.37 cm2 V−1s−1, demonstrate that great shallow donor impurities in GaN QDs film that ionize easily at RT, which is greatly beneficial for gas sensing. The sensing mechanisms were analyzed based on its abundant active sites, ultrahigh carrier mobility, high surface energy and chemical activity, grain boundary barrier and good electrical conductivity. Moreover, the sensor processes high selectivity, repeatability, stability and reusability based on MOCVD technology which can realize precise control of structure, morphology and surface energy level, which is expected to achieve rapid industrialization. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

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arXiv 2023年

作者： Cheng, Xiang Zhang, Haotian Zhang, Jianan Gao, Shijian Li, Sijiang Huang, Ziwei Bai, Lu Yang, Zonghui Zheng, Xinhu Yang, Liuqing The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China The Samsung Semiconductor Samsung SoC Research and Development Lab San DiegoCA92121 United States Shandong University Jinan250100 China Guangzhou510000 China Guangzhou510000 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based app

关键词： Beamforming

A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis

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arXiv 2023年

作者： Liu, Yaochen Zhang, Yazhou Song, Dawei School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Computing and Communications The Open University Walton Hall United Kingdom School of Nursing The Hong Kong Polytechnic University Hong Kong Ministry of Education China Artificial Intelligence Laboratory China Mobile Communication Group Tianjin Co. Ltd. China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Zhengzhou University of Light Industry China

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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

Pruning random resistive memory for optimizing analogue AI

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arXiv 2023年

作者： Li, Yi Wang, Songqi Zhao, Yaping Wang, Shaocong Zhang, Woyu He, Yangu Lin, Ning Cui, Binbin Chen, Xi Zhang, Shiming Jiang, Hao Lin, Peng Zhang, Xumeng Qi, Xiaojuan Wang, Zhongrui Xu, Xiaoxin Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong State Key Lab of Fabrication Technologies for Integrated Circuits Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China Frontier Institute of Chip and System Fudan University Shanghai200433 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences Beijing100049 China Institute of Mind The University of Hong Kong Hong Kong

The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unprecedented challenges to energy consumption and environmental sustainability, which are exacerbated by the increasing number of users and the development of even larger models. One promising solution is to revisit analogue computing, a technique that predates digital computing and exploits emerging analogue electronic devices, such as resistive memory, which features in-memory computing, high scalability, and nonvolatility that addresses the von Neumann bottleneck, slowdown of Moore’s law, and volatile DRAM of conventional digital hardware. However, analogue computing still faces the same challenges as before: programming nonidealities and expensive programming due to the underlying devices physics. Therefore, leveraging the efficiency advantage while mitigating the programming disadvantage of analogue computing with resistive memory is a major open problem in AI hardware and electronics communities. Here, we report a universal solution, software-hardware co-design using structural plasticity-inspired edge pruning to optimize the topology of a randomly weighted analogue resistive memory neural network. Software-wise, the topology of a randomly weighted neural network is optimized by pruning connections rather than precisely tuning resistive memory weights. Hardware-wise, we reveal the physical origin of the programming stochasticity using transmission electron microscopy, which is leveraged for large-scale and low-cost implementation of an overparameterized random neural network containing high-performance sub-networks. We implemented the co-design on a 40nm 256K resistive memory macro, observing 17.3% and 19.9% accuracy improvements in image and audio classification on FashionMNIST and Spoken digits datasets, as well as 9.8% (2%) improvement in PR (ROC) in image segmentation on DRIVE datasets, r

关键词： Energy utilization

An intelligent file transfer optimization for poor network conditions 6th

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An intelligent file transfer optimization for poor network c...

6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Yan, Ming Zhang, Bo Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Information Center of Ministry of Human Resources and Social Security Beijing100090 China National Key Laboratory of Science and Technology on Information System Security Beijing100091 China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9789811590306

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Transmission control protocol

QA-KGNet: A Language Model-Driven Knowledge Graph Question-Answering Model

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Ruan Jian Xue Bao/Journal of Software 2023年第10期34卷

作者： Qiao, Shao-Jie Yang, Guo-Ping Yu, Yong Han, Nan Qin, Xiao Qu, Lu-Lu Ran, Li-Qiong Li, He School of Software Engineering Chengdu University of Information Technology Chengdu610225 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China School of Management Chengdu University of Information Technology Chengdu610225 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School of Computer Science and Technology XiDian University Xi’an710071 China

The question-answering system based on knowledge graphs can analyze user questions, and has become an effective way to retrieve relevant knowledge and automatically answer the given questions. The knowledge graph-based question-answering system usually uses a neural program induction model to convert natural language question into a logical form, and the answer can be obtained by executing the logical form on the knowledge graph. However, the knowledge question-answering system by using pre-trained language models and knowledge graphs involves two challenges: 1) given the QA (Question-answering) context, relevant knowledge needs to be identified from a large KG (Knowledge Graph);2) we need to perform the joint reasoning on QA context and KG. Based on these challenges, a language model-driven knowledge graph question-answering model is proposed, which connects the QA context and KG to form a joint graph, and uses a language model to calculate the relevance of the given QA context nodes and KG nodes, and a multi-head graph attention network is employed to update the node representation. Extensive experiments on the CommonsenseQA, OpenBookQA and MedQA-USMLE real datasets are conducted to evaluate the performance of QA-KGNet and the experimental results show that QA-KGNet outperforms existing benchmark models and exhibits excellent structured reasoning capability. © 2023 Chinese Academy of sciences. All rights reserved.

关键词： Knowledge graph

Real-time suturing simulation for virtual reality medical training

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Real-time suturing simulation for virtual reality medical tr...

作者： Yu, Peng Pan, Junjun Qin, Hong Hao, Aimin Wang, Haipeng State Key Laboratory of Virtual Reality Technology & Systems Beihang University Beijing China Peng Cheng Lab Shenzhen China Faculty of Media and Communication Bournemouth University Dorset United Kingdom Department of Computer Science State University of New York at Stony Brook New YorkNY United States Beijing Aerospace General Hospital Beijing China

At present, virtual reality (VR) -based medical simulators provide an efficient and cost-effective alternative without exposing risk to the traditional training approaches. As an essential and indispensable task in fundamental surgical skills training, the research of suturing simulation still remains insufficient in the field of virtual surgery. In this paper, we present a real-time suturing simulation framework which can handle the complex interactions between surgical instruments and soft tissue. The simulation consists of two stages: external interaction and internal coupling. External interaction involves the interplay between needle/suture and the soft tissue, which are both deformed by position-based dynamics (PBD) with different constraints. At the internal coupling stage, once the force exceeds a threshold, the needle tip will puncture and penetrate into the soft tissue and generate a path. To guarantee the needle/suture accurately following the path inside the soft tissue, we propose a novel coupling method by matching and generating the constraints among needle, suture, and penetration path. We have applied this suturing simulation into a VR laparoscopic surgery simulator with haptic force. Our experimental results demonstrate that our approach can achieve real-time performance with a high degree of visual realism and haptic fidelity. © 2020 John Wiley & Sons, Ltd.

关键词： Needles

human-computer interactions for virtual reality

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 I0001-I0002页

作者： Feng TIAN Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China

human-computer interactions constitute an important subject for the development and popularization of information technologies,as they are not only an important frontier technology in computer science but also an important auxiliary technology in virtual reality(VR).In recent years,Chinese researchers have made significant advances in human-computer *** systematically display China's latest advances in human-computer interactions and thus provide an impetus for the development of VR and other related fields,we have solicited articles for this special issue from experts in this area to participate in the review *** following articles have been selected for publication in this special issue.

关键词： computer human frontier