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Applied AI Letters 2021年第4期2卷

作者： Stefik, Mark Youngblood, Michael Pirolli, Peter Lebiere, Christian Thomson, Robert Price, Robert Nelson, Lester D. Krivacic, Robert Le, Jacob Mitsopoulos, Konstantinos Somers, Sterling Schooler, Joel Intelligent Systems Laboratory PARC A Xerox Company Palo Alto CA United States The Institute for Human and Machine Cognition Pensacola FL United States Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University Pittsburgh PA United States Army Cyber Institute United States Military Academy New York NY United States

COGLE (COmmon Ground Learning and Explanation) is an explainable artificial intelligence (XAI) system where autonomous drones deliver supplies to field units in mountainous areas. The mission risks vary with topography, flight decisions, and mission goals. The missions engage a human plus AI team where users determine which of two AI-controlled drones is better for each mission. This article reports on the technical approach and findings of the project and reflects on challenges that complex combinatorial problems present for users, machine learning, user studies, and the context of use for XAI systems. COGLE creates explanations in multiple modalities. Narrative “What” explanations compare what each drone does on a mission and “Why” based on drone competencies determined from experiments using counterfactuals. Visual “Where” explanations highlight risks on maps to help users to interpret flight plans. One branch of the research studied whether the explanations helped users to predict drone performance. In this branch, a model induction user study showed that post-decision explanations had only a small effect in teaching users to determine by themselves which drone is better for a mission. Subsequent reflection suggests that supporting human plus AI decision making with pre-decision explanations is a better context for benefiting from explanations on combinatorial tasks. © 2021 PARC, a Xerox Company. Applied AI Letters published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

关键词： autonomous systems explainable artificial intelligence hierarchical multifunction modeling

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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

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AlloHaptic: Robot-Mediated Haptic Collaboration for Learning Linear Functions*

AlloHaptic: Robot-Mediated Haptic Collaboration for Learning...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Hala Khodr Soheil Kianzad Wafa Johal Aditi Kothiyal Barbara Bruno Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Laboratory (CHILI) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Department of Computer Science University of British Columbia University of New South Wales Sydney Australia MOBOTS Group in BIOROB EPFL Lausanne

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

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Global guidance network for breast lesion segmentation in ultrasound images

arXiv

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arXiv 2021年

作者： Xue, Cheng Zhu, Lei Fu, Huazhu Hu, Xiaowei Li, Xiaomeng Zhang, Hai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Theoretical Physics University of Cambridge Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People's Hospital Second Clinical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks. © 2021, CC BY.

关键词： Deep neural networks

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A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detecti...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhihao Chen Lei Zhu Liang Wan Song Wang Wei Feng Pheng-Ann Heng College of Intelligence and Computing Tianjin University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Engineering University of South Carolina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.

关键词： Task analysis Image edge detection Feature extraction Training Radio frequency Fuses Solid modeling

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Instance Shadow Detection

Instance Shadow Detection

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianyu Wang Xiaowei Hu Qiong Wang Pheng-Ann Heng Chi-Wing Fu Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing.

关键词： Head Feature extraction Proposals Image segmentation Image color analysis Training computer vision

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Learning meters of arabic and english poems with recurrent neural networks: A step forward for language understanding and synthesis

arXiv

引用

arXiv 2019年

作者： Yousef, Waleed A. Ibrahime, Omar M. Madbouly, Taha M. Mahmoud, Moustafa A. Human Computer Interaction Laboratory Department of Computer Science Faculty of Computers and Information Helwan University Egypt

Recognizing a piece of writing as a poem or prose is usually easy for the majority of people;however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded at the character level and directly fed to the models without feature handcrafting. This is a step forward for machine understanding and synthesis of languages in general, and Arabic language in particular. Among the 16 poem meters of Arabic and the 4 meters of English the networks were able to correctly classify poem with an overall accuracy of 96.38% and 82.31% respectively. The poem datasets used to conduct this research were massive, over 1.5 million of verses, and were crawled from different nontechnical sources, almost Arabic and English literature sites, and in different heterogeneous and unstructured formats. These datasets are now made publicly available in clean, structured, and documented format for other future research. To the best of the authors' knowledge, this research is the first to address classifying poem meters in a machine learnin g approach, in general, and in RNN featureless based approach, in particular. In addition, the dataset is the first publicly available dataset ready for the purpose of future computational research. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation 9th

Attention Based Hierarchical Aggregation Network for 3D Left...

引用

9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and computer-Assisted Intervention, MICCAI 2018

作者： Li, Caizi Tong, Qianqian Liao, Xiangyun Si, Weixin Sun, Yinzi Wang, Qiong Heng, Pheng-Ann School of Computer Science Wuhan University Wuhan430072 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9783030120283

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. The atrial segmentation is essential for the understanding of the human atria structure which is vital to the AF treatment. In this paper, we propose a novel three-dimensional (3D) segmentation network combining hierarchical aggregation and attention mechanism for 3D left atrial segmentation, named attention based hierarchical aggregation network (HAANet). In our network, the shallow and deep feature fusion capability of encoder-decoder convolutional neural networks is enhanced through hierarchical aggregation. Besides, attention mechanism is adopted to promote the ability of extracting efficient features. Experimental results demonstrate the HAANet can produce good results for 3D left atrial segmentation and the dice score of our HAANet reaches 92.30. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Task-Specific versus Free-Choice Non-Driving-Related Tasks: Enhancing Automated Driving Takeover Research Through Varied Task Engagement

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iscience 2025年

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 behaviour, 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 behaviours 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： Distractions and interruptions Task switching Driver behaviour Trust in automation human-automation interactions

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DVP: Data visualization platform

arXiv

引用

arXiv 2019年

作者： Yousefa, Waleed A. Abouelkahire, Ahmed A. Marzouk, Omar S. Mohame, Sameh K Alaggan, Mohammad Human Computer Interaction Laboratory HCI Lab Computer Science Department Faculty of Computers and Information Helwan University

We identify two major steps in data analysis, data exploration for understanding and observing patterns/relationships in data;and construction, design and assessment of various models to formalize these relationships. For each step, there exists a large set of tools and software. For the first step, many visualization tools exist, such as, GGobi, Parallax, and Crystal Vision, and most recently tableau and plottly. For the second step, many Scientific Computing Environments (SCEs) exist, such as, Matlab, Mathematica, R and Python. However, there does not exist a tool which allows for seamless two-way interaction between visualization tools and SCEs. We have designed and implemented a data visualization platform (DVP) with an architecture and design that attempts to bridge this gap. DVP connects seamlessly to SCEs to bring the computational capabilities to the visualization methods in a single coherent platform. DVP is designed with two interfaces, the desktop stand alone version and the online interface. To illustrate the power of DVP design, a free demo for the online interface of DVP is available [1] and very low-level design details are explained in this article. Since DVP was launched, circa 2012, the present manuscript was not published since today for commercialization and patent considerations. Copyright © 2019, The Authors. All rights reserved.

关键词： Data visualization

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