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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Bortnikova, Anna V. Bakaev, Maxim A. Novosibirsk State Technical University Department of Automated Control Systems Novosibirsk Russia Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

User Experience (UX) researchers and designers who seek to predict users' subjective impressions nowadays turn to Machine Learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-Computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users' impressions of web pages' Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R2s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling. © 2024 IEEE.

关键词： Web Design

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Discount Eye-Tracking for Usability Testing with Webcam, CNN, and the Selected Training data 3

Discount Eye-Tracking for Usability Testing with Webcam, CNN...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Kolidov, Ivan A. Bakaev, Maxim Novosibirsk State Technical University Department of Automated Control Systems Novosibirsk Russia Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia

ISBN: (纸本)9798331516321

Not many people realize that eye-tracking might be a fruitful addition to the widely established usability testing, at the same time not becoming prohibitively expensive for smaller companies. Unlike in research applications of the human gaze training technologies that require high precision, usability engineering professionals are mostly interested in concrete improvements to the user interfaces they develop. Correspondingly, affordable budgets and more frequent testing sessions yield better practical results. This paper presents a 'discount' approach implementing 'good enough' eye-tracking for usability testing with a standard webcam. A convolutional neural network was trained to predict screen coordinates from eye images, compensating for the precision provided by the specialized equipment. To improve the accuracy, three different data collection methods were tested, and the combined mesh-point based one allowed the best error (Root Mean Square Error, RMSE) of 78 pixels on a 1920xl080 screen. Although the error was about 2-3 inferior to the one obtained for the professional equipment, our results demonstrate that a webcam combined with deep learning techniques can serve as an affordable alternative for real-time eye-tracking. This approach has the potential to significantly reduce costs and make eye-tracking technology more accessible to a broader community of software developers and user interface designers. © 2024 IEEE.

关键词： Budget control

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The Novel Band-Pass Re-Entrant Filters to Eliminate the Scan Blindness in Phased Array Antennas on a Base of the Center-End-Fed Dipole-Like Radiators 3

The Novel Band-Pass Re-Entrant Filters to Eliminate the Scan...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Alekseytsev, Sergey A. Gorbachev, Anatoly P. Parshin, Yury N. Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia Novosibirsk State Technical University Department of Radio Receiving and Radio Transmitting Devices Novosibirsk Russia Novosibirsk State Technical University Laboratory for the development of broadband microwave systems and devices Novosibirsk Russia

ISBN: (纸本)9798331516321

This presentation deals with the novel band-pass filter structures, containing the full screening fragments. The novel band-pass filter proposed behaves the elliptic-function-like frequency response related to the insertion loss or, in other words, to the input reflection coefficient. These results were performed in the 'Radio and Electronic department' of the Novosibirsk State Technical University. As would be described, the band-pass filter incorporated into radio frequency assembly of the identical radiators (in other words: into the phased array antennas), opens up a potential opportunity to reduce the mutual coupling and widen the scan range in terms of the blindness and suppression of the quasi-transverse electromagnetic mode along the E-plane in the linear dipole-like phased arrays. To understand better, the nontraditional dipole-like radiator is described in details. Its three-dimensional architecture contains the novel re-entrant two-way power divider that is based on the metallic fragment like the hollow pipe with rectangular cross-section. The below-described methods in the manufacturing of filters and dipole-like radiators will allow to be used in the modified version of the radio frequency telecommunication systems including satellite equipment. © 2024 IEEE.

关键词： Dipole antennas

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Gender Specificity in Exergaming for Therapy and Rehabilitation 16

Gender Specificity in Exergaming for Therapy and Rehabilitat...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

作者： Razumnikova, Olga M. Eremeev, Sergey A. Bakaev, Maxim A. Zhang, Wei Department of Psychology and Pedagogy Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia School of Medicine and Health Management Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350330885

As IT products' developers seek to enhance the level of user experience, more attention is given to consideration the personal characteristics and preferences of the target audience. For exergaming software that is increasingly employed for medical therapy and rehabilitation, motivation and satisfaction of its users is of particular importance, due to the need to ensure its regular usage. The novelty of the VR/AR environment is seen as a positive factor for the above, but the potentially negative reactions to the VR immersion do not gain enough research attention. In our paper, we explore gender differences in users' experience with VR-based exergaming (VRE). We describe the experimental study that we performed with 54 subjects who engaged with Beat Saber game in VR, tasked with crashing various flying blocks with two light sabers. The game score reflected the objective exergaming performance, while we also registered the subjects' self-assessment emotional state data during the VRE experience and their personal characteristics. Our results suggest that the gender-related differences were rather minor: for men, frustration affected the coordination of sensory and motor activities;while for women frustration, personal anxiety and rigidity were related to the decrease in anxiety caused by exergaming. We believe that our findings could be of usage to VR/AR content designers seeking to cater more personalized products target audience and may help to improve VR programs of neurorehabilitation due to mental disorders or cardiovascular diseases. © 2023 IEEE.

关键词： Virtual reality

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Who Is to Err? A Limited Effect of data Labeling in Prediction of Users' Subjective Impressions of Web Designs

Who Is to Err? A Limited Effect of Data Labeling in Predicti...

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Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Anna V. Bortnikova Maxim A. Bakaev Department of Automated Control Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

User Experience (UX) researchers and designers who seek to predict users’ subjective impressions nowadays turn to Machine Learning (ML) models trained on marked-up data. Labeling of graphical user interfaces (UIs) is a rather time-consuming process that is also prone to errors and inaccuracies, particularly if motivation of the human labelers is low. Correspondingly, the size of datasets in Human-Computer Interaction (HCI) rarely exceeds the order of 10,000, and the field largely relies on feature-based models. In our paper, we compare two principal ways of obtaining the features, with respect to their effect on the quality of the models that predict users’ impressions of web pages’ Complexity, Aesthetics and Orderliness. First, we calculated several layout-related metrics - balance, equilibrium, symmetry - for 368 website screenshots labeled by 40 human volunteers. Second, we obtained the similar metrics from ML-based online services working with raw web UI images. Our results suggest that despite the limitations of the current automated web UI parametrization tools, their metrics can actually yield superior models (having R 2 s higher by 107% on average). Our key implication for UI/UX designers and ML researchers is that the expected ROI should be carefully weighted before committing to the tedious manual web UI data labeling.

关键词： Measurement Human computer interaction Web design Manuals Machine learning Predictive models data models User experience Labeling Informatics

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Discount Eye-Tracking for Usability Testing with Webcam, CNN, and the Selected Training data

Discount Eye-Tracking for Usability Testing with Webcam, CNN...

引用

Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Ivan A. Kolidov Maxim Bakaev Department of Automated Control Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

Not many people realize that eye-tracking might be a fruitful addition to the widely established usability testing, at the same time not becoming prohibitively expensive for smaller companies. Unlike in research applications of the human gaze training technologies that require high precision, usability engineering professionals are mostly interested in concrete improvements to the user interfaces they develop. Correspondingly, affordable budgets and more frequent testing sessions yield better practical results. This paper presents a “discount” approach implementing “good enough” eye-tracking for usability testing with a standard webcam. A convolutional neural network was trained to predict screen coordinates from eye images, compensating for the precision provided by the specialized equipment. To improve the accuracy, three different data collection methods were tested, and the combined mesh-point based one allowed the best error (Root Mean Square Error, RMSE) of 78 pixels on a 1920xl080 screen. Although the error was about 2–3 inferior to the one obtained for the professional equipment, our results demonstrate that a webcam combined with deep learning techniques can serve as an affordable alternative for real-time eye-tracking. This approach has the potential to significantly reduce costs and make eye-tracking technology more accessible to a broader community of software developers and user interface designers.

关键词： Accuracy Costs Webcams Gaze tracking data collection User interfaces data models Convolutional neural networks Usability Testing

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Random Error Reduction in Phase Shift Interferometry 15

Random Error Reduction in Phase Shift Interferometry

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15th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2021

作者： Guzhov, Vladimir I. Ilinykh, Sergey P. Novosibirsk State Technical University Department of Data Collection and Processing Systems Novosibirsk Russia Novosibirsk State Technical University Department of Computer Engineering Novosibirsk Russia

ISBN: (纸本)9781665434089

The object of research in this article is a new approach to reducing the error of phase demodulation in phase-shifting interferometry. It is based on averaging a combination of functionally related sequences of interference patterns intensities, with the help of which the phase is demodulated. It is possible to average both individual phase values at separate points of the interference pattern and their differences between different points. The method is based on finding the phase difference at three arbitrary points of the interference pattern. Fixing one or two points of the interference pattern with other points, scanning is carried out over its entire surface. Thus, the authors obtain a set of phase differences between different points. Averaging them allows to reduce the influence of noise on the measurement result. Thus, for each point of the interference pattern, a set of phase values or phase differences is obtained. Using the found phase differences, it is also possible to determine the phase directly at each point of the interference pattern. In contrast to the known methods, the proposed approach does not require registration of additional interference patterns. This allows to shorten the measurement time without losing their accuracy. © 2021 IEEE.

关键词： Interferometry

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The Novel Band-Pass Re-Entrant Filters to Eliminate the Scan Blindness in Phased Array Antennas on a Base of the Center-End-Fed Dipole-Like Radiators

The Novel Band-Pass Re-Entrant Filters to Eliminate the Scan...

引用

Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Sergey A. Alekseytsev Anatoly P. Gorbachev Yury N. Parshin Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Radio Receiving and Radio Transmitting Devices Novosibirsk State Technical University Novosibirsk Russia Laboratory for the development of broadband microwave systems and devices Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

This presentation deals with the novel band-pass filter structures, containing the full screening fragments. The novel band-pass filter proposed behaves the elliptic-function-like frequency response related to the insertion loss or, in other words, to the input reflection coefficient. These results were performed in the “Radio and Electronic department” of the Novosibirsk State Technical University. As would be described, the band-pass filter incorporated into radio frequency assembly of the identical radiators (in other words: into the phased array antennas), opens up a potential opportunity to reduce the mutual coupling and widen the scan range in terms of the blindness and suppression of the quasi-transverse electromagnetic mode along the E-plane in the linear dipole-like phased arrays. To understand better, the nontraditional dipole-like radiator is described in details. Its three-dimensional architecture contains the novel re-entrant two-way power divider that is based on the metallic fragment like the hollow pipe with rectangular cross-section. The below-described methods in the manufacturing of filters and dipole-like radiators will allow to be used in the modified version of the radio frequency telecommunication systems including satellite equipment.

关键词： Phased arrays Band-pass filters Filters Three-dimensional displays Dipole antennas Satellite broadcasting Blindness Telecommunications Reflection coefficient Linear antenna arrays

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Gender Specificity in Exergaming for Therapy and Rehabilitation

Gender Specificity in Exergaming for Therapy and Rehabilitat...

引用

International Conference on Actual Problems of Electronic Instrument Engineering (APEIE)

作者： Olga M. Razumnikova Maxim A. Bakaev Sergey A. Eremeev Wei Zhang Department of Psychology and Pedagogy Novosibirsk State Technical University Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia School of Medicine and Health Management Huazhong University of Science and Technology Wuhan China

As IT products' developers seek to enhance the level of user experience, more attention is given to consideration the personal characteristics and preferences of the target audience. For exergaming software that is increasingly employed for medical therapy and rehabilitation, motivation and satisfaction of its users is of particular importance, due to the need to ensure its regular usage. The novelty of the VR/AR environment is seen as a positive factor for the above, but the potentially negative reactions to the VR immersion do not gain enough research attention. In our paper, we explore gender differences in users' experience with VR-based exergaming (VRE). We describe the experimental study that we performed with 54 subjects who engaged with Beat Saber game in VR, tasked with crashing various flying blocks with two light sabers. The game score reflected the objective exergaming performance, while we also registered the subjects' self-assessment emotional state data during the VRE experience and their personal characteristics. Our results suggest that the gender-related differences were rather minor: for men, frustration affected the coordination of sensory and motor activities; while for women frustration, personal anxiety and rigidity were related to the decrease in anxiety caused by exergaming. We believe that our findings could be of usage to VR/AR content designers seeking to cater more personalized products target audience and may help to improve VR programs of neurorehabilitation due to mental disorders or cardiovascular diseases.

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Large language models surpass human experts in predicting neuroscience results

arXiv

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

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