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Movement acts in breakdown situations: How a robot's recovery procedure affects participants' opinions

Paladyn 2021年第1期12卷 336-355页

作者： Schulz, Trenton Soma, Rebekka Holthaus, Patrick Department of ICT Research Norwegian Computing Center Oslo Norway Department of Informatics University of Oslo Oslo Norway School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

Recovery procedures are targeted at correcting issues encountered by robots. What are people's opinions of a robot during these recovery procedures? During an experiment that examined how a mobile robot moved, the robot would unexpectedly pause or rotate itself to recover from a navigation problem. The serendipity of the recovery procedure and people's understanding of it became a case study to examine how future study designs could consider breakdowns better and look at suggestions for better robot behaviors in such situations. We present the original experiment with the recovery procedure. We then examine the responses from the participants in this experiment qualitatively to see how they interpreted the breakdown situation when it occurred. Responses could be grouped into themes of sentience, competence, and the robot's forms. The themes indicate that the robot's movement communicated different information to different participants. This leads us to introduce the concept of movement acts to help examine the explicit and implicit parts of communication in movement. Given that we developed the concept looking at an unexpected breakdown, we suggest that researchers should plan for the possibility of breakdowns in experiments and examine and report people's experience around a robot breakdown to further explore unintended robot communication. © 2021 Trenton Schulz et al., published by De Gruyter.

关键词： Human robot interaction

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Automatic Segmentation and Alignment of Uterine Shapes from 3D Ultrasound Data

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computers in Biology and Medicine 2024年 178卷 108794-108794页

作者： Boneš, Eva Gergolet, Marco Bohak, Ciril Lesar, Žiga Marolt, Matija University of Ljubljana Faculty of Computer and Information Science Večna pot 113 Ljubljana1000 Slovenia University of Ljubljana Faculty of Medicine Vrazov trg 2 Ljubljana1000 Slovenia King Abdullah University of Science and Technology Visual Computing Center Thuwal23955-6900 Saudi Arabia

Background: The uterus is the most important organ in the female reproductive system. Its shape plays a critical role in fertility and pregnancy outcomes. Advances in medical imaging, such as 3D ultrasound, have significantly improved the exploration of the female genital tract, thereby enhancing gynecological healthcare. Despite well-documented data for organs like the liver and heart, large-scale studies on the uterus are lacking. Existing classifications, such as VCUAM and ESHRE/ESGE, provide different definitions for normal uterine shapes but are not based on real-world measurements. Moreover, the lack of comprehensive datasets significantly hinders research in this area. Our research, part of the larger NURSE study, aims to fill this gap by establishing the shape of a normal uterus using real-world 3D vaginal ultrasound scans. This will facilitate research into uterine shape abnormalities associated with infertility and recurrent miscarriages. Methods: We developed an automated system for the segmentation and alignment of uterine shapes from 3D ultrasound data, which consists of two steps: automatic segmentation of the uteri in 3D ultrasound scans using deep learning techniques, and alignment of the resulting shapes with standard geometrical approaches, enabling the extraction of the normal shape for future analysis. The system was trained and validated on a comprehensive dataset of 3D ultrasound images from multiple medical centers. Its performance was evaluated by comparing the automated results with manual annotations provided by expert clinicians. Results: The presented approach demonstrated high accuracy in segmenting and aligning uterine shapes from 3D ultrasound data. The segmentation achieved an average Dice similarity coefficient (DSC) of 0.90. Our method for aligning uterine shapes showed minimal translation and rotation errors compared to traditional methods, with the preliminary average shape exhibiting characteristics consistent with expert finding

关键词： Alignment

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NanoMi: Progress on an Open-source Electron Microscope

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 489-490页

作者： Schreiber, Makoto Malac, Marek Salomons, Mark Homeniuk, Darren Ruttiman, Sam Wang, Xuanhao Adkin-Kaya, Olivier Kamal, Mohammad Calzada, Jesus Alejandro Marin Price, Patrick Cloutier, Martin Hayashida, Misa Egerton, Ray Harada, Ken Takahashi, Yoshio Müller, Heiko Edmonton AB Canada Department of Physics University of Alberta Edmonton AB Canada Department of Computing Science University of Alberta Edmonton AB Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Center for Emergent Matter Science RIKEN Hatoyama Saitama Japan Research & Development Group Ltd. Hatoyama Hitachi Saitama Japan CEOS GmbH Heidelberg Germany

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Fast and Accurate Bolt Classification for a Retail Motorcycle Shop in Thailand

Fast and Accurate Bolt Classification for a Retail Motorcycl...

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International computer science and Engineering Conference (ICSEC)

作者： Monchai Angkapiyasiri Suphakant Phimoltares Department of Mathematics and Computer Science Advanced Virtual and Intelligent Computing (AVIC) Research Center Faculty of Science Chulalongkorn University Bangkok Thailand

People have begun to improve their purchase of products and food via online applications because of the ongoing global pandemic. Especially, purchasing products through online applications is popular due to their convenience. Hence, numerous riders need motorcycles to work for delivery. As a result, critical spare parts such as durable and beautiful bolts are highly demanded for maintenance. We collect the best-selling bolts of 19 classes from a motorcycle shop to develop a bolt classification procedure and improve business opportunities. A combination of principal component analysis, median cut algorithm, and structural analysis, is used for extracting a set of features. Then, five classification models, which are multi-layer perceptron, random forest, decision tree, support vector machine, and logistic regression, are used to assign the most appropriate class for each bolt. The experiment results show that the multi-layer perceptron performs better than the other classification models.

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BFT-Metronome: A Novel Byzantine Fault-Tolerant Clock Synchronization Algorithm

BFT-Metronome: A Novel Byzantine Fault-Tolerant Clock Synchr...

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Blockchain computing and Applications (BCCA), International Conference on

作者： Conner Fulford Sai Medury Amani Altarawneh Anthony Skjellum Department of Computer Science Engineering University of Tennessee at Chattanooga Chattanooga TN USA The Advanced Computing Center for Research and Education Vanderbilt University Nashville TN USA Department of Computer Science Tennessee Tech University Cookeville TN USA

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Distributed systems and blockchain consensus protocols rely on a clock synchronization algorithm to resolve any conflicts related to the state of data or resources. Faulty and adversarial peers make it challenging to achieve clock synchronization in a peer-to-peer distributed system. Clock-synchronization protocols such as the Network Time Protocol (NTP) and the Precision Time Protocol (PTP) are effective but only in a partially or fully trusted network. They fail to synchronize clocks in the presence of misbehaving peers who try to rewind or fast-forward time or introduce deadlocks *** paper introduces BFT-Metronome, a Byzantine fault-tolerant clock synchronization algorithm for peer-to-peer distributed systems. The novel BFT-Metronome algorithm combines a sensor-fusion algorithm with a statistical outlier detection algorithm to enable participating peers to determine the most likely skew based on broadcast timestamps. The algorithm is designed to withstand up to $\frac{N}{3} - 1$ misbehaving participants in a network of N total participants, and the independently calculated offset is observed to be within 10 ms of precision vs. 40-100 ms in peer-to-peer distributed *** BFT-Metronome clock synchronization algorithm can also be applied to blockchain consensus protocols (such as Proof-of-Stake) that rely on weakly synchronized clocks. This is shown by integrating BFT-Metronome with the Lightweight Mining Algorithm to achieve consensus-round synchronization and timeouts. Then, generalization to other protocols is argued qualitatively. It can also be applied to large distributed systems to secure against insider threats during clock synchronization.

关键词： Fault tolerance Proof of stake Fault tolerant systems Resists System recovery Peer-to-peer computing Consensus protocol Synchronization Anomaly detection Clocks

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of computer science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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Approximate Tree Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees

arXiv

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arXiv 2025年

作者： Veldt, Nate Stanley, Thomas Priest, Benjamin W. Steil, Trevor Iwabuchi, Keita Jayram, T.S. Sanders, Geoffrey Department of Computer Science and Engineering Texas A&M University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Finding a minimum spanning tree (MST) for n points in an arbitrary metric space is a fundamental primitive for hierarchical clustering and many other ML tasks, but this takes Ω(n2) time to even approximate. We introduce a framework for metric MSTs that first (1) finds a forest of disconnected components using practical heuristics, and then (2) finds a small weight set of edges to connect disjoint components of the forest into a spanning tree. We prove that optimally solving the second step still takes Ω(n2) time, but we provide a subquadratic 2.62-approximation algorithm. In the spirit of learning-augmented algorithms, we then show that if the forest found in step (1) overlaps with an optimal MST, we can approximate the original MST problem in subquadratic time, where the approximation factor depends on a measure of overlap. In practice, we find nearly optimal spanning trees for a wide range of metrics, while being orders of magnitude faster than exact algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Approximation algorithms

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Neuro-symbolic approaches in artificial intelligence

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National science Review 2022年第6期9卷 35-37页

作者： Pascal Hitzler Aaron Eberhart Monireh Ebrahimi Md Kamruzzaman Sarker Lu Zhou Department of Computer Science Kansas State University Center for Open Source Data and AI Technologies (CODAIT) IBM Watson Department of Computing Sciences University of Hartford

Neuro-symbolic artificial intelligence refers to a field of research and applications that combines machine learning methods based on artificial neural networks, such as deep learning, with symbolic approaches to computing and artificial intelligence(AI), as can be found for example in the AI subfield of knowledge representation and reasoning.

关键词： artificial intelligence deep learning knowledge representation and reasoning neuro-symbolic integration

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3D Shape Completion on Unseen Categories: A Weakly-supervised Approach

arXiv

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arXiv 2024年

作者： Wu, Lintai Hou, Junhui Song, Linqi Xu, Yong The Department of Computer Science City University of Hong Kong Hong Kong The Bio-Computing Research Center Harbin Institute of Technology Shenzhen Guangdong Shenzhen518055 China The Shenzhen Key Laboratory of Visual Object Detection and Recognition Guangdong Shenzhen518055 China

3D shapes captured by scanning devices are often incomplete due to occlusion. 3D shape completion methods have been explored to tackle this limitation. However, most of these methods are only trained and tested on a subset of categories, resulting in poor generalization to unseen categories. In this paper, we propose a novel weakly-supervised framework to reconstruct the complete shapes from unseen categories. We first propose an end-to-end prior-assisted shape learning network that leverages data from the seen categories to infer a coarse shape. Specifically, we construct a prior bank consisting of representative shapes from the seen categories. Then, we design a multi-scale pattern correlation module for learning the complete shape of the input by analyzing the correlation between local patterns within the input and the priors at various scales. In addition, we propose a self-supervised shape refinement model to further refine the coarse shape. Considering the shape variability of 3D objects across categories, we construct a category-specific prior bank to facilitate shape refinement. Then, we devise a voxel-based partial matching loss and leverage the partial scans to drive the refinement process. Extensive experimental results show that our approach is superior to state-of-the-art methods by a large margin. We will make the source code publicly available at https://***/ltwu6/WSSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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