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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Strohmeier, Paul Vidal, Laia Turmo Vega, Gabriela Reed, Courtney N. Mazursky, Alex AliAbbasi, Easa Tajadura-Jiménez, Ana Steimle, Jürgen Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany KTH Royal Institute of Technology Stockholm Sweden Sensorimotor Interaction Group Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Institute for Digital Technologies Loughborough University London London United Kingdom University of Chicago ChicagoIL United States Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid Leganés Madrid Spain Saarland University Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9798400713958

An emerging space in interface research is wearable devices that closely couple their sensing and actuation abilities. A well-known example is MetaLimbs [39], where sensed movements of the foot are directly mapped to the actuation of supernumerary robotic limbs. These systems are different from wearables focused on sensing, such as fitness trackers, or wearables focused on actuation, such as VR headsets. They are characterized by tight coupling between the user’s action and the resulting digital feedback from the device, in time, space, and mode. The properties of this coupling are critical for the user’s experience, including the user’s sense of agency, body ownership, and experience of the surrounding world. Understanding such systems is an open challenge, which requires knowledge not only of computer science and HCI, but also Psychology, Physiology, Design, engineering, Cognitive Neuroscience, and Control Theory. This workshop aims to foster discussion between these diverse disciplines and to identify links and synergies in their work, ultimately developing a common understanding of future research directions for systems that intrinsically couple sensing and action. © 2025 Copyright held by the owner/author(s).

关键词： Feedback control

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The Role of Non-Invasive Neural Devices in Modern Neurorehabilitation and Mental Health Treatment

The Role of Non-Invasive Neural Devices in Modern Neurorehab...

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International Conference on Trends in Electronics and informatics (ICEI)

作者： Adesh Pawar Palash Gourshettiwar Pradnyawant M. Gote Vishal Kumar Choudhary Prajyot Yesankar Shrikant Khadse Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U) Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India Department of Artificial Intelligence &Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (D.U.) Wardha Maharashtra India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Modern neurorehabilitation and mental health treatment have received transformative tools through non-invasive neural devices that offer effective and patient-friendly alternatives to traditional interventions. Technologies in this regard include transcranial magnetic stimulation, transcranial direct current stimulation, EEG-based neurofeedback, and functional near-infrared spectroscopy, where advances in neuroscience and bioengineering are utilised in modulating brain activity without risks associated with surgical procedures. Their applications in neurorehabilitation range from post-stroke motor recovery to cognitive rehabilitation and treatment of mental health disorders, including depression, anxiety, and PTSD. Focusing on personalised medicine, machine learning, and big data could be used in non-invasive neural devices to fine-tune outcomes tailored to an individual's needs. These are seminal challenges, such as accessibility, variability in patient responses, and regulatory hurdles, in the face of these devices offering much promise regarding enhancing the quality of life and improving cognitive functions, besides providing sustainable, long-term mental health. This paper underlines their potential and discusses future directions in integration.

关键词： Neuroplasticity Transcranial magnetic stimulation Surgery Mental health Machine learning Motors Market research Neurorehabilitation Transcranial direct current stimulation Neurofeedback

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Prediction of Favorability of Maxillary Canine Impaction Using Artificial Intelligence Algorithm

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Journal of Indian Orthodontic Society 2024年第3期58卷 291 - 302页

作者： Dilip Kumar M. Bhadrinath S. Balika J. Chelliah Umamageshwari A. Deepa S. Department of Orthodontics and Dentofacial Orthopaedics Sri Ramachandra Institute of Higher Education and Research (SRIHER) Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

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Multi-modal cooperative awareness of connected and automated vehicles in smart cities 5

Multi-modal cooperative awareness of connected and automated...

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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Piperigkos, Nikos Lalos, Aris S. Berberidis, Kostas Computer Engineering and Informatics Department University of Patras Greece Industrial Systems Institute Athena Research Center Patras Greece

ISBN: (纸本)9781665445115

Cooperative autonomous driving in 5G and smart cities environment is expected to further improve safety, security and efficiency of transportation systems. To this end, involved vehicles is imperative to have accurate knowledge of both their own and neighboring vehicles’ location, a task known as cooperative awareness. In this paper, we have formulated two novel distributed localization and tracking schemes, based on Gradient Descent and Extended Kalman Filter algorithms, to cope with erroneous GPS location. Sensor-rich vehicles exploit Vehicle-to-Vehicle communications and a multitude of integrated sensors, like LIDAR and Cameras, to generate and fuse heterogeneous data. Each vehicle interacts only with its own connected neighboring vehicles, formulating individual star topologies. Extensive simulation studies using CARLA autonomous driving simulator, verify the significant reduction of GPS error achieved by the two methods in various experimental conditions. Distributed tracking proves to be much superior than Gradient descent algorithm, both in the case of self (58% reduction of GPS error) and neighboring vehicles location estimation (38% reduction of average GPS error). © 2021 IEEE.

关键词： 5G mobile communication systems

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization

A Framework for Democratizing Open-Source Decision-Making us...

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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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Design, Analysis and Fabrication of Dual Band Microstrip Patch Antenna for (L2) Band GPS and WiFi Applications

Design, Analysis and Fabrication of Dual Band Microstrip Pat...

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Oo, Wai Mar Tun, Hla Myo Nway, Tint May Pradhan, Devasis Sahu, Prasanna Kumar Naing, Zaw Min Yangon Technological University Department of Electronic Engineering Yangon Myanmar Yangon Technological University Faculty of Electrical and Computer Engineering Yangon Myanmar Acharya Institute of Technology Department of Electronics and Communication Engineering Bengaluru India National Institute of Technology Department of Electrical Engineering Odisha Rourkela India Ministry of Science and Technology Department of Research and Innovation Yangon Myanmar

ISBN: (纸本)9781665425773

Dual-frequency antennas are popular for many applications in communication systems. This work proposes a dual-band microstrip patch antenna design based on two-layer stacked patch for GPS and WiFi purposes. The targeted antenna is created to maneuver at the frequency range of 1.227GHz GPS L2 band (1.215GHz to 1.239GHz) and the 2.4GHz WiFi band frequency (2.4GHz to 2.48GHz). The antenna is fabricated on substrate of FR4 which dielectric constant 4.4 and the substrate material's height is 1.6mm. The created two-layer stacked patch antenna is compared with a conformist single patch antenna and analyzed the parameters of performance specifications for return loss (RL), bandwidth, VSWR, gain and pattern of radiation. It is recognized that the performance of the fabricated antenna improves when compared to the single-layer conventional antenna. © 2022 IEEE.

关键词： Global positioning system

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation 20

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Lugsam: A Novel Framework for Integrating Text Prompts to Segment Anything Model (Sam) for Segmentation Tasks of Icu Chest X-Rays

SSRN

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SSRN 2024年

作者： Ramesh, Dhanush Babu Sridhar, Rishika Iytha Upadhyaya, Pulakesh Kamaleswaran, Rishikesan Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States Department of Biomedical Informatics Emory University School of Medicine AtlantaGA United States

Chest radiography is a commonly utilized imaging technique for acquiring Chest X-Ray (CXR) images due to its cost-effectiveness and its role in diagnosing lung-related disorders. Nevertheless, interpreting CXR images can be challenging, and the process of separating the lung field from CXR images can be a valuable tool for assessing and diagnosing lung diseases. While various segmentation methods exist, this study primarily focuses on META’s latest Segment Anything Model (SAM). SAM is an Artificial Intelligence (AI) model designed to segment objects within an image. This research aims to harness SAM’s capabilities for segmenting CXR images. Additionally, we explore the potential of another novel model called Grounding DINO. Grounding DINO is a zero-shot object detection model that utilizes a Swin (Shifted Windows) transformer for extracting image features and BERT (Bidirectional Encoder Representations from Transformers) for extracting textual information. It is primarily employed to detect objects in an image based on a provided text prompt, creating bounding boxes around the objects when certain text and box thresholds are met. These bounding boxes are then used as prompts for SAM to generate segmentation masks. The proposed framework has been assessed on CXRs obtained from patients at Emory Hospital in Atlanta, Georgia, USA and further evaluated using NIH clinical center’s CXR image dataset. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Optimizing complex event forecasting 22

Optimizing complex event forecasting

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16th ACM International Conference on Distributed and Event-Based Systems, DEBS 2022

作者： Stavropoulos, Vasileios Alevizos, Elias Giatrakos, Nikos Artikis, Alexander Institute of Informatics and Telecommunications Ncsr Demokritos Greece School of Electrical and Computer Engineering Technical University of Crete Greece Athena Research Center Institute for Management of Information Systems Greece Department of Maritime Studies University of Piraeus Greece

ISBN: (纸本)9781450393089

In Complex Event Recognition (CER), applications express business rules in the form of patterns and deploy them in a CER Engine which seeks the occurrence of such patterns on incoming streams. This is useful for practical applications which rely on the timely detection of patterns to support critical decisions. One step further, stakeholders want to act proactively, accurately forecasting the occurrence of patterns on raw streams well ahead of time to better schedule their decisions. This calls for making the transition from CER to Complex Event Forecasting (CEF). In CEF, stochastic models of future behavior are embedded into the event processing loop to project into the future the sequence of events that have occurred so far and to estimate the likelihood of the imminent occurrence of more complex patterns. CEF performance engages the stochastic model's training speed and forecast accuracy. In turn, these performance dimensions are affected by few parameters. However, CEF parameter tuning so that optimal CEF performance is achieved is a non-trivial task. This is due to the fact that there is an infinite number of possible parameter combinations, each affecting CEF performance in ways which are hard to predict. In this work, we introduce the first CEF Optimizer that gracefully automates CEF parameter tuning decisions, rapidly cherry picking good CEF configurations. We detail the novel internal architecture of our CEF Optimizer and present an elaborate empirical analysis on two applications that illustrates the effectiveness of our optimization approach. © 2022 ACM.

关键词： Stochastic models

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EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Algorithm Using Backtracking Technique 5

EHHGSO: Enhancing Hybrid Hugo Gas Solubility Optimization Al...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Mahanta, Ajoy Bin Nur, Md. Oshama Islam, Rakibul Azad, Saiful Wadith, Shadman Mahmud, Mufti Kaiser, M. Shamim Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Nottingham Trent University Department of Computer Science NottinghamNG118NS United Kingdom Nottingham Trent University Computing and Informatics Research Centre NottinghamNG118NS United Kingdom Nottingham Trent University Medical Technologies Innovation Facility NottinghamNG118NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh

ISBN: (纸本)9798350394290

Metaheuristic algorithms play a pivotal role in solving complex optimization problems. This study addresses a common challenge faced by such algorithms, i.e., getting trapped in local optima. This problem hampers the efficacy of metaheuristic algorithms in achieving globally optimal solutions. This limitation is also observed in Hybrid Henry Gas Solubility Optimization Algorithm (HHGSO)-a new metaheuristic based algorithm-which employs a cluster-based approach for population management. While this approach fosters exploration by diversifying the search process, it lacks adaptiveness in cluster assignments and the potentiality of hybridization with other algorithms. These shortcomings hinder the algorithm's ability to effectively escape local optima and harness the strengths of diverse optimization techniques. To mitigate these challenges, this study proposed an Enhanced HHGSO (EHHGSO) algorithm, an advanced hybridization of HHGSO. Here, a novel backtracking based technique is incorporated that can detect the trapping into local optima and can escape from it. Empirical findings substantiate the efficacy of EHHGSO in comparison to its ancestor, HHGSO for various benchmark functions. © 2023 IEEE.

关键词： Solubility

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