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On the potential for human-centered, cognitively inspired AI to bridge the gap between optimism and reality for autonomous robotics in healthcare: a respectful critique

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2024年第1期13卷 106 - 112页

作者： Stephanie Tulk Jesso Christopher Greene Shiqi Zhang Amy Booth Michael DiFabio Grace Babalola Adeola Adegbemijo Sumantra Sarkar 1Department of Systems Science and Industrial Engineering SUNY Binghamton2Human-Centered Mindful Technologies Lab SUNY Binghamton 1Department of Systems Science and Industrial Engineering SUNY Binghamton 3Department of Computer Science SUNY Binghamton 4New York United Health Services 5School of Management SUNY Binghamton

Historically, the fields of computer science, cognitive science, and neuroscience have been tightly linked. To date, this collaboration has yielded major advances in how the brain and mind are understood, as well as the ways in which artificial minds can be constructed to serve as new collaborators to humans. Yet there are still significant gaps between the capabilities of state-of-the-art autonomous robots and the expectations developed by real users who are now encountering autonomous robots on the job. We present our views as well as a case study of our evaluation of two autonomous robots intended to aid nurses within hospital settings: Moxi and TUG. Both cobots were originally considered for procurement by our collaborating healthcare system,UHS, at which time our team began the process of trying to systematically vet each option to aid in the decision making process. What we found was a lack of evidence on either platform in academic literature, which led us to analyze user comments on social media. In order to improve the fit of autonomous robots into human environments, research must be conducted and evidence must be shared, and the Human Factors community can aid in this effort

关键词： Cognitively Plausible AI Autonomous Robots Cobots Healthcare Nursing 4E Cognition

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 IEEE.

关键词： Time series

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Numerical evaluation on sub-Nyquist spectrum reconstruction methods

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Frontiers of computer science 2023年第6期17卷 199-201页

作者： Zihang SONG Han ZHANG Sean FULLER Andrew LAMBERT Zhinong YING Petri MAHONEN Yonina ELDAR Shuguang CUI Mark DPLUMBLEY Clive PARINI Arumugam NALLANATHAN Yue GAO Department of Engineering King’s College LondonStrandLondon WC2R 2LSUK School of Computer Science and Electronic Engineering University of SurreyGuildfordSurrey GU27XHUK National Instruments Corporation(UK)Ltd. NewburyBerkshire RG142PZUK Electronic Media Services Ltd. BordonHampshire GU350FJUK Sony Research Center Sony CorporationLund 22188Sweden Institute for Networked Systems RWTH Aachen UniversityKackertstrasseAachen 52072Germany Faculty of Math&CS Weizmann Institute of ScienceRehovot 7610001Israel School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK School of Computer Science Fudan UniversityShanghai 200433China

1 Introduction As wireless technology continues to expand,there is a growing concern about the efficient use of spectrum *** though a significant portion of the spectrum is allocated to licensed primary users(PUs),studies indicate that their actual utilization is often limited to between 5%to 10%[1].The underutilization of spectrum has given rise to cognitive radio(CR)technology,which allows secondary users(SUs)to opportunistically access these underused resources[2].However,wideband spectrum sensing,the key of CR,is limited by the need for high-speed analog-to-digital converters(ADCs),which are costly and *** spectrum sensing(CSS)addresses this challenge by employing sub-Nyquist rate *** efficiency of active transmission detection heavily depends on the quality of spectrum reconstruction.

关键词： spectrum continue Nyquist

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Guest Editorial Special Issue on Green Open Radio Access Networks: Architecture, Challenges, Opportunities, and Use Cases

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第3期8卷 891-894页

作者： Dev, Kapal Chih-Lin, I. Boutaba, Raouf Khowaja, Sunder Ali Lien, Shao-Yu Wang, Yue Munster Technological University Department of Computer Science CorkT12 P928 Ireland China Mobile Research Institute Beijing100053 China University of Waterloo School of Computer Science WaterlooONN2L 3G1 Canada University of Sindh Faculty of Engineering and Technology Jamshoro76080 Pakistan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan 6G Research Centre Samsung Electronics SurreyKT16 0RS United Kingdom

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Fault-Tolerant Offloading Algorithm of Independent Tasks in Edge Networks

SSRN

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SSRN 2024年

作者： Meree, Ali Maray, Mohamed Jhumka, Arshad Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom College of Computer Science and Information Systems Department King Khalid University Abha61421 Saudi Arabia Department of Computer Science University of Leeds Woodhouse Lane LeedsLS2 9JT United Kingdom

Cloud Computing has facilitated the provision of computing resources and capabilities to users on an as-needed basis. The cloud resources available today are predominantly characterized by their distributed functions across various locations, far removed from the actual users. To address communication latency issues, these services have been moved to the network’s edge, a practice now referred to as edge computing. In this setup, when services are accessed by users at the edge, the network is termed an edge network. An edge network is composed of several base stations or edge nodes that perform tasks for users. That is, users delegate tasks to these edge nodes, which then process and complete them before sending the results back to the users. This chapter delves into the emerging challenge of supporting the offloading of independent tasks onto unsecured edge networks with the aim of minimizing job completion times during failures. We define this issue as a constraint satisfaction problem and introduce a spatial redundancy fault tolerance offloading strategy. Simulation tests are carried out using the ns-3 network simulator to evaluate the effectiveness of our suggested algorithm. The findings indicate that (i) job completion times lengthen as the fault rate rises, (ii) response times grow with an increase in the fault rate, (iii) job completion times decrease with higher user mobility speeds, and (iv) job completion times augment with a rise in the number of edge users and the fault rate. © 2024, The Authors. All rights reserved.

关键词： Constraint satisfaction problems

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Exploring Help-Seeking Behavior, Performance, and Cognitive Load in Individual Tutoring: A Comparative Study between Human Tutors and Social Robots

Exploring Help-Seeking Behavior, Performance, and Cognitive ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Social robots have become increasingly prevalent in the context of one-on-one tutoring, serving as effective educational aids. In response to this trend, the present study was devised to conduct a comparative analysis between human tutors and robot tutors. Additionally, the study aims to investigate how varying previous knowledge in robots influence students’ tendencies for help-seeking. By examining the performance and physiological signals of participants, this research seeks to provide valuable insights into the effectiveness of social robots in educational contexts. 21 participants were divided into three groups, each seeking assistance from a human tutor (HT), seeking help from a robot without any prior knowledge of robots (RT1), and seeking help from a robot after gaining some understanding of its capabilities (RT2). Our results demonstrated that participants sought more help from robot than from human and participants in Group RT2 performed better than participants in Group RT1. However, participants experienced greater cognitive load when interacting with a robot tutor compared to interacting with a human tutor. Future work could focus on developing interventions to alleviate students’ cognitive load during interactions with robot tutors.

关键词： Social robots Robot vision systems Cognitive load Market research Cameras Physiology Behavioral sciences Robots Thermal stresses Stress

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Estimating Complexity for Perception-based ADAS in Unstructured Road Environments

Estimating Complexity for Perception-based ADAS in Unstructu...

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IEEE Symposium on Intelligent Vehicle

作者： Imane Taourarti Ayesha Choudhary Vivek Kumar Paswan Aditya Kumar Arunkumar Ramaswamy Javier Ibanez-Guzman Bruno Monsuez Adriana Tapus Autonomous Systems and Robotics Lab/U2IS Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris France Research Department Renault Group France School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Advanced Driver Assistance systems (ADAS) are rapidly becoming a standard feature in modern road vehicles, enhancing safety and driver comfort. As ADAS adoption expands across diverse geographical and cultural regions, the performance of camera-based perception systems may vary significantly due to environmental and expected social behaviour of the different actors. This paper explores the referred factors and evaluates the traffic environment complexity for vehicles with different levels of automation. In particular, we propose a novel modeling and quantitative assessment approach for environment complexity. Specifically, we compare a perception model trained on United States dataset with a dataset from India, a nation characterized by unique traffic patterns, signage conventions, and cultural norms to assess its performance variation, and to lay the basis for proposing influencing factors of traffic environment complexity. We establish a scheme of referential and additional static factors and based on an expert evaluation, environment complexity is established. The effectiveness of the proposed approach is testified by naturalistic driving data. These findings pave the way for future research in intelligent driving and emphasize the importance of addressing cultural nuances as vehicle automation levels increase.

关键词： Measurement Automation Roads Road vehicles Globalization Complexity theory Safety

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Portable comprehensive two-dimensional microgas chromatography using an integrated flowrestricted pneumatic modulator

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Microsystems & Nanoengineering 2022年第6期8卷 61-72页

作者： Xiaheng Huang Maxwell Wei-hao Li Wenzhe Zang Xiaolu Huang Anjali Devi Sivakumar Ruchi Sharma Xudong Fan Department of Biomedical Engineering University of MichiganAnn ArborMI 48109USA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA Center for Wireless Integrated MicroSensing and Systems(WIMS2) University of MichiganAnn ArborMI 48109USA Max Harry Weil Institute for Critical Care Research and InnovationUniversity of Michigan Ann ArborMI 48109USA

Two-dimensional(2D)gas chromatography(GC)provides enhanced vapor separation capabilities in contrast to conventional one-dimensional GC and is useful for the analysis of highly complex chemical *** developed a microfabricated flow-restricted pneumatic modulator(FRPM)for portable comprehensive 2D micro-GC(μGC),which enables rapid ^(2)D injection and separation without compromising the ^(1)D separation speed and eluent peak profiles.^(2)D injection characteristics such as injection peak width and peak height were fully characterized by using flow-through micro-photoionization detectors(μPIDs)at the FRPM inlet and outlet.A ^(2)D injection peak width of~25 ms could be achieved with a ^(2)D/^(1)D flow rate ratio over *** FRPM was further integrated with a 0.5-m long ^(2)D μcolumn on the same chip,and its performance was ***,we developed an automated portable comprehensive 2D μGC consisting of a 10m OV-11D μcolumn,an integrated FRPM with a built-in 0.5m polyethylene glycol ^(2)D μcolumn,and two μ*** separation of 40 volatile organic compounds in~5 min was demonstrated.A hybrid 2D contour plot was constructed by using both ^(1)D and ^(2)D chromatograms obtained with the two μPIDs at the end of the ^(1)D and ^(2)D μcolumns,which was enabled by the presence of the flow resistor in the FRPM.

关键词： flow restricted comprehensive

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Blockchain-Based Framework for Stock Market Operations: IPOs, Trading, and Dividend Distribution

Blockchain-Based Framework for Stock Market Operations: IPOs...

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Muhammad Sharshar Kareem Elzeky Menna Othman SeifAldin Khaled Hosam Moubarak Walid Gomaa Department of Computer Science Center for Secure Cyber-Physical Systems (C2PS) Khalifa University UAE Department of Natural Language Processing MBZUAI UAE Department of Computer Engineering Cyber-Physical Lab Egypt-Japan University of Science and Technology Egypt Accounting Department Egypt-Japan University of Science and Technology Egypt

ISBN: (数字)9798331507589

ISBN: (纸本)9798331507596

This study examines blockchain technology's potential to enhance the stock market by offering a decentralized alternative to the traditional system. The study explores the challenges of the current traditional stock market, such as long settlement times, weak transparency, dependency on centralized clearing houses, and heightened fees. The proposed novel system leverages blockchain technology to create a fully decentralized stock market encompassing all fundamental operations from Initial Public Offerings (IPOs) to stock trading and extending to distributing profits or dividends. It connects all market participants through an Ethereum consortium blockchain fuelled by smart contracts. This approach replaces traditional intermediaries like brokers with automated regulations, ensuring more rapid transaction settlements, reduced operational errors and fees, and strengthened security measures. The proposed blockchain-based system, consisting of four smart contracts for IPO, stock exchange, dividend distribution, and participant management, demonstrates remarkable performance, showing promise in efficiently handling the current load of the largest stock markets like the London Stock Exchange (LSE). Through theoretical and practical testing, the system achieved a remarkable throughput of 120 transactions per second and latency or finality of just 4 seconds, underscoring its capability to significantly enhance the efficiency of stock market operations. 1 1 The implementation code is available via GitHub upon request.

关键词： Cloud computing Smart contracts Transforms Throughput Regulation Blockchains Security Stock markets Testing Software development management

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Automated Heartbeat Classification for Arrhythmia Patients Using a Deep Convolutional Neural Network

Automated Heartbeat Classification for Arrhythmia Patients U...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Saoueb Kerdoudi Larbi Guezouli Abdessalam Hattab Tahar Dilekh Computer Science Department LaSTIC Laboratory University of Batna 2 Batna Algeria LEREESI Laboratory of Renewable Energy Energy Efficiency and Smart Systems Higher National School of Renewable Energies Environment and Sustainable Development Batna Algeria Computer Science Department LAMIE Laboratory University of Batna 2 Batna Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

The electrocardiogram (ECG) is widely used for diagnosing heart diseases, including arrhythmia, due to its noninvasive nature and simplicity. Accurate detection and classification of arrhythmic types are crucial in preventing heart disease and reducing mortality. To enhance ECG heartbeat classification, we propose an automated system based on a fine-tuned deep convolutional neural network consisting of 13 residual blocks with 28 layers in total. This model was trained using a dataset of 103247 single-lead ECG recordings. Our deep convolutional neural network achieved remarkable results by validating the independent test dataset from the MIT-BIH Arrhythmia Database. It demonstrated an Accuracy of 99%, a Macro avg Precision of 96%, a Macro avg Recall of 93%, a Weighted avg of 99%, and an impressive Macro avg F1-score of 94%. Notably, our approach has demonstrated superior performance to the original architecture, with a 3% increase in F1-score. These findings showcase the potential of our improved deep learning approach to effectively classify a wide range of arrhythmias from single-lead ECGs, yielding high diagnostic performance comparable to that of cardiologists.

关键词： Heart Heart beat Databases Arrhythmia Electrocardiography Network architecture Probability distribution

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