2021 |
Luckner, Marcin; Krzemińska, Izabella; Wawrzyniak, Piotr; Legierski, Jarosław Contravening Citizen's Privacy : Warsaw Use Case Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–13, 2021. Abstract | Links | BibTeX | Tags: Data models, Data privacy, Estimation, Privacy, Probes, Trajectory, Urban areas @article{Luckner2021, title = {Contravening Citizen's Privacy : Warsaw Use Case}, author = {Marcin Luckner and Izabella Krzemińska and Piotr Wawrzyniak and Jarosław Legierski}, url = {https://ieeexplore.ieee.org/document/9497518}, year = {2021}, date = {2021-01-01}, journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems}, pages = {1--13}, publisher = {IEEE}, abstract = {Spatial data on a cellular network load can be used to develop commercial and public services. However, such data is calculated based on individual users' behavior and can contravene their privacy rights. Moreover, direct tracking of individual devices violates the European Union's regulations. To solve this issue, we propose to use data aggregated in individual cells of the public land mobile network without tracking an individual mobile device in the entire process. To prove that the proposed data collection method is useful, we compared the obtained results with a closed-circuit television system in an estimation of the number of people. The proposed system is sensitive enough to detect untypical global events in an urban area and distinguish transport demand zones of various types as we showed on real data from the City of Warsaw.}, keywords = {Data models, Data privacy, Estimation, Privacy, Probes, Trajectory, Urban areas}, pubstate = {published}, tppubtype = {article} } Spatial data on a cellular network load can be used to develop commercial and public services. However, such data is calculated based on individual users' behavior and can contravene their privacy rights. Moreover, direct tracking of individual devices violates the European Union's regulations. To solve this issue, we propose to use data aggregated in individual cells of the public land mobile network without tracking an individual mobile device in the entire process. To prove that the proposed data collection method is useful, we compared the obtained results with a closed-circuit television system in an estimation of the number of people. The proposed system is sensitive enough to detect untypical global events in an urban area and distinguish transport demand zones of various types as we showed on real data from the City of Warsaw. |
2006 |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
Luckner, Marcin Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. Abstract | Links | BibTeX | Tags: Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition @inproceedings{Luckner2006d, title = {Recognition of Noised Patterns Using Non-Disruption Learning Set}, author = {Marcin Luckner}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021499}, doi = {10.1109/ISDA.2006.223}, isbn = {0-7695-2528-8}, year = {2006}, date = {2006-10-01}, booktitle = {Sixth International Conference on Intelligent Systems Design and Applications}, volume = {1}, pages = {557--562}, publisher = {IEEE}, abstract = {In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones}, keywords = {Computer networks, Delay, Geodesy, music symbols, Noise generators, noised pattern recognition, nondisruption learning set, nondisruption patterns, optical character recognition, Optical character recognition software, optical music recognition, Optical noise, Ordinary magnetoresistance, Pattern recognition, Probes, recognition system, strongly noised symbol recognition, supervised recognition, Testing, unsupervised recognition}, pubstate = {published}, tppubtype = {inproceedings} } In this paper the recognition of strongly noised symbols on the basis of non-disruption patterns is discussed taking music symbols as an example. Although Optical Music Recognition technology is not developed as successfully as OCR technology, several systems do recognize typical musical symbols to quite a good level. However, the recognition of non-typical fonts is still an unsolved issue. In this paper a model of a recognition system for unusual scores is presented. In the model described non-disruption symbols are used to generate a learning set that makes possible improved recognition as is presented on a real example of rests and accidentals recognition. Some techniques are presented with various recognition rates and computing times including supervised and unsupervised ones |
Publications
2021 |
Contravening Citizen's Privacy : Warsaw Use Case Journal Article IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–13, 2021. |
2006 |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |
Recognition of Noised Patterns Using Non-Disruption Learning Set Inproceedings Sixth International Conference on Intelligent Systems Design and Applications, pp. 557–562, IEEE, 2006, ISBN: 0-7695-2528-8. |