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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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TRANSLOG: A Unified Transformer-based Framework for Log Anomaly Detection

arXiv

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arXiv 2021年

作者： Guo, Hongcheng Lin, Xingyu Yang, Jian Zhuang, Yi Bai, Jiaqi Zhang, Bo Zheng, Tieqiao Li, Zhoujun State Key Lab of Software Development Environment Beihang University China Bio-Robot and Human-Computer Interaction Laboratory Waseda University Japan Cloudwise Research University of Southern California United States

Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps). Considering log data of variant domains, retraining the whole network for unknown domains is inefficient in real industrial scenarios especially for low-resource domains. However, previous deep models merely focused on extracting the semantics of log sequence in the same domain, leading to poor generalization on multi-domain logs. Therefore, we propose a unified Transformer-based framework for Log anomaly detection (TRANSLOG), which is comprised of the pretraining and adapter-based tuning stage. Our model is first pretrained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer the pretrained model to the target domain via the adapter-based tuning. The proposed method is evaluated on three public datasets including one source domain and two target domains. The experimental results demonstrate that our simple yet efficient approach, with fewer trainable parameters and lower training costs in target domain, achieves the state-of-the-art performance on three benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao Robot: A Closed-Loop Approach

arXiv

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arXiv 2025年

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan United States

—This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2025, CC BY-NC-ND.

关键词： Anthropomorphic robots

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SceneSketcher: Fine-Grained Image Retrieval with Scene Sketches 16th

SceneSketcher: Fine-Grained Image Retrieval with Scene Sketc...

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16th European Conference on computer vision, ECCV 2020

作者： Liu, Fang Zou, Changqing Deng, Xiaoming Zuo, Ran Lai, Yu-Kun Ma, Cuixia Liu, Yong-Jin Wang, Hongan State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China HMI Laboratory Huawei Technologies Shenzhen China Cardiff University Cardiff United Kingdom Tsinghua University Beijing China

ISBN: (纸本)9783030585280

Sketch-based image retrieval (SBIR) has been a popular research topic in recent years. Existing works concentrate on mapping the visual information of sketches and images to a semantic space at the object level. In this paper, for the first time, we study the fine-grained scene-level SBIR problem which aims at retrieving scene images satisfying the user’s specific requirements via a freehand scene sketch. We propose a graph embedding based method to learn the similarity measurement between images and scene sketches, which models the multi-modal information, including the size and appearance of objects as well as their layout information, in an effective manner. To evaluate our approach, we collect a dataset based on SketchyCOCO and extend the dataset using Coco-stuff. Comprehensive experiments demonstrate the significant potential of the proposed approach on the application of fine-grained scene-level image retrieval. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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An Explainable Deep Neural Network Model Based on Multi-Granularity Co-Attention to Improve Prediction of Drug-Target Binding Affinity

SSRN

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SSRN 2024年

作者： Lin, Yanmei Cao, Aiqing Deng, Lei Liao, Mingzhi Zhang, Ru Zhang, Hao Peng, Yuzhong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Guangxi Academy of Sciences Nanning530007 China School of Computer Science and Engineering Central South University Changsha410083 China Center of Bioinformatics College of Life Sciences Northwest A&F University Shaanxi Yangling712100 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518000 China Center for Applied Mathematics of Guangxi Nanning Normal University Nanning530100 China

The prediction of drug-target interactions is pivotal in modern new drug development and drug repositioning. Nonetheless, current methods for predicting drug-target interactions often lose essential molecular structure information during the modeling process, leading to low prediction accuracy. This work proposes rMolGIN, a novel graph neural network with residual connectivity for molecular feature learning. rMolGIN not only takes into account the differences in chemical bonding properties and atomic neighborhood interactions, but also captures global and local structural information of drug molecules. Furthermore, we propose a novel drug-target affinity model based on rMolGIN and deep neural networks, coupled with a multi-granularity co-attention mechanism called HMCoDTA to capture comprehensive structural insights from both drugs and proteins. The proposed innovative model allows for comprehensive insight into the structural relationships between drugs and proteins, facilitating a more accurate prediction of binding affinities. To validate the effectiveness of our proposed methods, we conducted extensive experiments on three benchmark datasets of drug-target binding affinity, comparing HMCoDTA against 19 state-of-the-art models. Notably, the results show that HMCoDTA outperforms existing methods, demonstrating the effectiveness and advantages of our model in predicting drug-target binding affinity. © 2024, The Authors. All rights reserved.

关键词： Prediction models

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IoT based Smart Water Quality Prediction for Biofloc Aquaculture

arXiv

引用

arXiv 2022年

作者： Mamunur Rashid, Md. Nayan, Al-Akhir Obaidur Rahman, Md. Simi, Sabrina Afrin Saha, Joyeta Kibria, Muhammad Golam Department of CSE University of Liberal Arts Bangladesh Dhaka Bangladesh Department of CSE European University of Bangladesh Dhaka Bangladesh Dept. of Human Computer Interaction University of Siegen Siegen Germany Department of ECE North South University Dhaka Bangladesh Department of CSE IoT Lab University of Liberal Arts Bangladesh Dhaka Bangladesh

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result. © 2022, CC BY.

关键词： Aquaculture

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Augmented reality and robotics in education: A systematic literature review

Computers in Human Behavior: Artificial Humans

引用

computers in human Behavior: Artificial humans 2025年 4卷

作者： Christina Pasalidou Chris Lytridis Avgoustos Tsinakos Nikolaos Fachantidis Laboratory of Informatics & Robotic Applications in Education and Society Department of Educational and Social Policy University of Macedonia Thessaloniki Greece Human-Machines Interaction (HUMAIN) Lab Department of Computer Science Democritus University of Thrace Kavala Greece Advanced Educational Technologies and Mobile Applications (AETMA) Lab Department of Computer Science Democritus University of Thrace Kavala Greece

Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. Augmented Reality (AR) and robotics are two emerging technologies that have shown promise in transforming educational environments. This paper presents a systematic review of the literature on the combination of AR and robotics for educational purposes, identifying key applications, benefits, and trends. Using the PRISMA methodology, 69 relevant studies from five major databases were analysed and categorised into three themes: (a) AR and Socially Assistive Robots (SAR), (b) AR-assisted educational robotics, and (c) AR in robotics/engineering education. The review provides insights into how different AR-enhanced robotics applications across primary, secondary, and higher education, provide visualizations, multimodal feedback, and immersive experiences. Key findings suggest that while interactive features of AR and the embodiment of robots show promising results for learning, fostering motivation, excitement, positive attitudes, and enriched educational experiences, challenges such as technological complexity and cost remain barriers to widespread adoption. Future research should focus on pedagogical frameworks and large-scale implementations to optimize AR-robotics integration in diverse educational settings.

关键词： Robotics Augmented reality HRI Education

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Artificial intelligence - The third wave 50

Artificial intelligence - The third wave

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50. Jahrestagung der Gesellschaft fur Informatik, INFORMATIK 2020 - 50th Annual Conference of the German Informatics Society, INFORMATIK 2020

作者： Schmid, Ute Tresp, Voker Bethge, Matthias Kersting, Kristian Stiefelhagen, Rainer Universität Bamberg Kognitive Systeme Germany Ludwig Maximilian Universität München and Distinguished Research Scientist at Siemens AG Corporate Technology Germany Universität Tübingen and Amazon Scholar Computational Neuroscience and Machine Learning Germany Technische Universität Darmstadt Künstliche Intelligenz und Maschinelles Lernen Germany Karlsruhe Institute of Technology Computer Vision for Human-Computer Interaction Germany

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Reconsidering Accountability in the Present and Future of Work

Reconsidering Accountability in the Present and Future of Wo...

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Companion Publication of the 2022 Conference on computer Supported Cooperative Work and Social Computing

作者： Sarah Elizabeth Fox Daniel Marcus Greene Karen Gregory Mohammad Hossein Jarrahi Vera Khovanskaya Human-Computer Interaction Institute Carnegie Mellon University United States College of Information Studies University of Maryland United States School of Social and Political Science University of Edinburgh Scotland School of Information and Library Science University of North Carolina at Chapel Hill United States Feminist Labor Lab University of California San Diego United States

ISBN: (纸本)9781450391900

There is a growing effort within CSCW and related fields to understand the effects of artificial intelligence, algorithmic management, and other contemporary and near-future transformations of digitally-mediated work. This panel builds on this effort by asking three guiding questions: how should academics more fully account for the consequences of new transformations in the workplace, how should we engage with workers who are already accounting for the ways that these systems impact their labor conditions and experience of work, and how should we be made accountable to workers for the design interventions we propose or deploy. The panelists will discuss topics including the impact of AI technology on frontline and essential workers, the transformative effect of algorithmic management on organizational practices within and beyond platform work, and how demands and efforts to attain algorithmic transparency by platform/gig-workers both reveal power imbalances within the workplaces and point to avenues for worker advocacy. We will also draw on scholarship that analyzes the shifting labor conditions of CSCW to situate contemporary concerns within the field's disciplinary history. We aim to generate conversation about the ways that these forms of accountability have already been unfolding, identify shared themes and concerns across parallel topics, and identify future directions for researchers moving forward in this space.

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Sonify: Making visual graphs accessible 1st

Sonify: Making visual graphs accessible

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1st International Conference on human interaction and Emerging Technologies, IHIET 2019

作者： Ali, Safinah Muralidharan, Laya Alfieri, Felicia Agrawal, Monali Jorgensen, Jacob MIT Media Lab 77 Massachusetts Avenue E14/E15 CambridgeMA02139 United States Human Computer Interaction Institute Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States

ISBN: (纸本)9783030256289

Data visualizations are an essential strategy for summarizing and comprehending large datasets. However, it is challenging for visually impaired people (VIP) to access visual representations of data. Current accessibility solutions fall short in providing comprehensive access to such visualizations. Most VIP must listen to datasets one cell at a time with a text-to-speech tool, which does not allow the user to gather the "gist" of the dataset. To address this gap, we designed and evaluated Sonify. Sonify is a sonification tool that makes data visualizations accessible to VIP through audio and tactile interactions on a mobile app. VIP use Sonify’s scrubbing interaction to explore patterns in data through pitch changes in sound. We evaluated Sonify’s interactions through contextual inquiries, with (n = 8) VIP. Through Sonify, we demonstrate how a novel scrubbing interaction technique makes 2-variable linear graphs accessible to a broader range of people, including VIP. © 2020, Springer Nature Switzerland AG.

关键词： Data visualization

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