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arXiv 2024年

作者： Koosheshi, MohammadHossein Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Yazdi, Mohammad Reza Hairi Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

humans, this species expert in grasp detection, can grasp objects by taking into account hand-object positioning information. This work proposes a method to enable a robot manipulator to learn the same, grasping objects in the most optimal way according to how the gripper has approached the object. Built on deep learning, the proposed method consists of two main stages. In order to generalize the network on unseen objects, the proposed Approach-based Grasping Inference involves an element decomposition stage to split an object into its main parts, each with one or more annotated grasps for a particular approach of the gripper. Subsequently, a grasp detection network utilizes the decomposed elements by Mask R-CNN and the information on the approach of the gripper in order to detect the element the gripper has approached and the most optimal grasp. In order to train the networks, the study introduces a robotic grasping dataset collected in the Coppeliasim simulation environment. The dataset involves 10 different objects with annotated element decomposition masks and grasp rectangles. The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment. Lastly, simulation-to-reality domain adaptation is performed by applying transformations on the training set collected in simulation and augmenting the dataset, which results in a 70% physical grasp success performance using a Delta parallel robot and a 2-fingered gripper. © 2024, CC BY.

关键词： Deep learning

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SIAMIXFORMER: A FULLY-TRANSFORMER SIAMESE NETWORK WITH TEMPORAL FUSION FOR ACCURATE BUILDING DETECTION AND CHANGE DETECTION IN BI-TEMPORAL REMOTE SENSING IMAGES

arXiv

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arXiv 2022年

作者： Mohammadian, Amir Ghaderi, Foad Human-Computer interaction lab Electrical and Computer Engineering Department Tarbiat Modares University Tehran Iran

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model’s performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Disasters

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Development and Initial Evaluation of a Digital Phenotype Collection System for Adolescents: Proof-of-Concept Study

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JMIR Formative Research 2024年 8卷 e59623页

作者： Cho, Minseo Park, Doeun Choo, Myounglee Kim, Jinwoo Han, Doug Hyun Human Computer Interaction Lab Department of Cognitive Science Yonsei University Seoul South Korea Human Computer Interaction Lab School of Business Yonsei University Seoul South Korea HAII Corp Seoul South Korea College of Medicine Chung-Ang University Seoul South Korea

Background: The growing concern on adolescent mental health calls for proactive early detection and intervention strategies. There is a recognition of the link between digital phenotypes and mental health, drawing attention to their potential use. However, the process of collecting digital phenotype data presents challenges despite its promising prospects. Objective: This study aims to develop and validate system concepts for collecting adolescent digital phenotypes that effectively manage inherent challenges in the process. Methods: In a formative investigation (N=34), we observed adolescent self-recording behaviors and conducted interviews to develop design goals. These goals were then translated into system concepts, which included planners resembling interfaces, simplified data input with tags, visual reports on behaviors and moods, and supportive ecological momentary assessment (EMA) prompts. A proof-of-concept study was conducted over 2 weeks (n=16), using tools that simulated the concepts to record daily activities and complete EMA surveys. The effectiveness of the system was evaluated through semistructured interviews, supplemented by an analysis of the frequency of records and responses. Results: The interview findings revealed overall satisfaction with the system concepts, emphasizing strong support for self-recording. Participants consistently maintained daily records throughout the study period, with no missing data. They particularly valued the recording procedures that aligned well with their self-recording goal of time management, facilitated by the interface design and simplified recording procedures. Visualizations during recording and subsequent report viewing further enhanced engagement by identifying missing data and encouraging deeper self-reflection. The average EMA compliance reached 72%, attributed to a design that faithfully reflected adolescents’ lives, with surveys scheduled at convenient times and supportive messages tailored to their dai

关键词： adolescent mental health adolescents digital phenotyping ecological momentary assessment phenotype data phenotypes proof of concept qualitative research self-monitoring smartphone apps

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Analyzing Midair Object Pointing Mappings for Smart Display Input

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Proceedings of the ACM on human-computer interaction 2021年第ISS期5卷 1–18页

作者： Zhang, Futian Mizobuchi, Sachi Zhou, Wei Lank, Edward Cheriton School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Human-Machine Interaction Lab Huawei TorontoON Canada

One common task when controlling smart displays is the manipulation of menu items. Given current examples of smart displays that support distant bare hand control, in this paper we explore menu item selection tasks with three different mappings of barehand movement to target selection. Through a series of experiments, we demonstrate that Positional mapping is faster than other mappings when the target is visible but requires many clutches in large targeting spaces. Rate-based mapping is, in contrast, preferred by participants due to its perceived lower effort, despite being slightly harder to learn initially. Tradeoffs in the design of target selection in smart tv displays are discussed. © 2021 ACM.

关键词： Mapping

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Robust PID Control of Robot Manipulator Using Time Delay Control Design Approach

Robust PID Control of Robot Manipulator Using Time Delay Con...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Alireza Fateh Hamidreza Momeni Mehdi Tale Masouleh dept. of Electrical and Computer Eng Tarbiat Modares University Tehran Iran Human and Robot Interaction Lab School of Electrical and Computer Eng University of Tehran Tehran Iran

Time Delay Control (TDC) is recognized as a simple yet effective alternative to model-based or intelligent-based control due to its simplicity and robustness. Proportional-Integral-Derivative (PID) control is one of the industry’s most used and straightforward control schemes. While this approach has proven to be effective, the tuning of its coefficients has remained a difficult task. Though showing similar performance, a definite connection between the two approaches has yet to be defined. In this paper, by taking advantage of padé approximation, a direct connection between the two control methods coefficients is obtained. As a result, a new approach for choosing the classical PID controller coefficients is introduced using TDC as the primary control scheme design framework, which has led to a decrease in the number of required control design coefficients of the PID control system. Further on, the uniformly ultimately bounded stability of the control system under bounded external disturbances is proven. Finally, via simulations on the SCARA robot, the obtained results and the validity of the proposed connection are explored.

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Autonomous vision-based Robotic Grasping of Household Objects: A Practical Case Study

Autonomous Vision-based Robotic Grasping of Household Object...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Asal Roudbari Mahdi Dalvand Navid Pasiar Navid Asadi Mohammad Yousefi Mehdi Tale Masouleh Electrical Engineering Department Sharif University of Technology Tehran Iran Human & Robot Interaction Lab University of Tehran Tehran Iran Human & Robot Interaction Lab School of Electrical&Computer Engineering University of Tehran Tehran Iran

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-.95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks 1 . 1 A video of real-time grasp performance is uploaded as supplementary material.

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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Correction to: Many are the ways to learn: Identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

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International Journal of computer-Supported Collaborative Learning 2022年 1-2页

作者： Nasir, Jauwairia Kothiyal, Aditi Bruno, Barbara Dillenbourg, Pierre Computer‑Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Lausanne Switzerland

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Trans4Trans: Efficient transformer for transparent object and semantic scene segmentation in real-world navigation assistance

arXiv

引用

arXiv 2021年

作者： Zhang, Jiaming Yang, Kailun Constantinescu, Angela Peng, Kunyu Müller, Karin Stiefelhagen, Rainer Computer Vision for Human-Computer Interaction Lab Center for Digital Accessibility and Assistive Technology Karlsruhe Institute of Technology Germany

Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space behind glass doors is inaccessible, unless it is correctly perceived and interacted with. However, traditional assistive technologies rarely cover the segmentation of these safety-critical transparent objects. In this paper, we build a wearable system with a novel dualhead Transformer for Transparency (Trans4Trans) perception model, which can segment general- and transparent objects. The two dense segmentation results are further combined with depth information in the system to help users navigate safely and assist them to negotiate transparent *** propose a lightweight Transformer Parsing Module (TPM) to perform multi-scale feature interpretation in the transformer-based decoder. Benefiting from TPM, the double decoders can perform joint learning from corresponding datasets to pursue robustness, meanwhile maintain efficiency on a portable GPU, with negligible calculation increase. The entire Trans4Trans model is constructed in a symmetrical encoder-decoder architecture, which outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining mIoU of 45:13% and 75:14%, respectively. Through a user study and various pre-tests conducted in indoor and outdoor scenes, the usability and reliability of our assistive system have been extensively verified. Meanwhile, the Tran4Trans model has outstanding performances on driving scene datasets. On Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments, adverse weather, and traffic accident scenarios, mIoU scores of 81:5%, 76:3%, and 39:2% are obtained, demonstrating its high efficiency and robustness for real-world transportation applications. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Chinese Text Classification Method Based on Double Pre-Processed Graph Aggregation Network 1

Chinese Text Classification Method Based on Double Pre-Proce...

引用

1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： He, Yixian Jiang, Jianhui Chen, Dingjia Tang, Molan Chen, Biquan Chen, Weiqing School of System and Computer Engineering North China Electric Power University Beijing China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Academy Sciences Nanning China Communication Industry Service Co. Ltd. Guangxi Zhuang Autonomous Region Nanning China Vocational College Guangxi Technology and Business Nanning China

ISBN: (纸本)9798331504434

To address the problem of insufficient node feature recognition and shallow network layers in Chinese text classification, this paper proposes a Chinese text classification method called Dual Preprocessing Graph Information Aggregation Network (DPGIAN). This method solves the problem of insufficient initial feature recognition of nodes and the inability of edge weights to highlight the weight ratio between keywords through a dual preprocessing strategy of node preprocessing and edge weight preprocessing;At the same time, a deeper graph information aggregation feature extraction method was used to solve the problem of excessive feature smoothing when obtaining deep semantic features in graph structured networks, improving the accuracy of Chinese text classification. By conducting experiments on multiple Chinese text classification datasets and comparing the experimental results with multiple mainstream text classification methods on the same dataset, our method outperforms the current mainstream text classification methods on the same dataset. Multiple ablation comparison experiments were also conducted to further verify the effectiveness of our method. © 2024 IEEE.

关键词： Chinese text classification Deep Learning Graph Network Node preprocessing Pre-processing of edge rights

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