Cloud storage services have promising characteristics for personal and corporate use as consumers' data can be stored on shared pools of storage hosted by cloud providers, while consumers can access and manage the...
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Cloud storage services have promising characteristics for personal and corporate use as consumers' data can be stored on shared pools of storage hosted by cloud providers, while consumers can access and manage their data anywhere via an Internet connection. Nevertheless, protection of data privacy is one of the major issues, and at the time of storage service selection, prospective consumers are concerned with how the cloud storage providers handle their personal data. To help a consumer with storage service selection, this paper proposes a methodology to assess the risk of privacy loss when consumer data are stored with a particular cloud storage service. The risk of privacy loss is viewed from two aspects. First, sensitivity of the personal data to be stored in the cloud and sensitivity of consumers' personal data requested at the time of service registration can contribute to the risk. Second, lack of privacy control transparency can be a risk factor if the cloud storage provider inadequately show the necessary privacy principles that are practiced. The proposed methodology can assist the consumers when determining the risk levels of privacy loss of different cloud storage services. We present the application of the methodology to a case of an organization selecting a cloud storage service to host its corporate data.
This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of m...
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This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of medical investigation in the decision-making process in clinical bioethics, particularly in this work, in cases of euthanasia. The data used in the approach were collected from 75 students of the nursing undergraduate course using an eye tracker. The available data were processed through feature selection methods, and were later used to create models capable of predicting the euthanasia decision through ML algorithms. Statistical experiments showed that the predictive model resultant from the multilayer perceptron (MLP) algorithm led to the best performance compared with the other tested algorithms, presenting an accuracy of 90.7% and a mean area under the ROC curve of 0.90. Interesting knowledge (patterns and rules) for the studied bioethical decision-making was extracted using simulations with MLP models and inspecting the obtained decision-tree rules. The good performance shown by the obtained MLP predictive model demonstrates that the proposed investigation approach may be used to test scientific hypotheses related to visual attention and decision-making.
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brai...
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We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainN
In this paper will be described the way of using data understanding techniques for semantic information extraction and data meaning evaluation. Extracted information may be then used for performing some management tas...
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In this paper will be described the way of using data understanding techniques for semantic information extraction and data meaning evaluation. Extracted information may be then used for performing some management tasks and security actions. It may play important role in advanced secure management applications and intelligent access control to secure data. Such techniques allow extending security procedures and have a great influence for creation new areas of security like personalized cryptography.
Including millimeter-wave (mm-wave) data in multi-wavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We dem...
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Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are vari-ous methods that exist in other domains to...
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Plasmonic metasurfaces have been employed for tuning and controlling light enabling various novel applications. Their appeal is enhanced with the incorporation of an active element with the metasurfaces paving the way...
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Activities with smart devices have been more complicated and diversified, and font legibility influences these activities both directly and indirectly. This study investigated the difference of Korean font legibility ...
Activities with smart devices have been more complicated and diversified, and font legibility influences these activities both directly and indirectly. This study investigated the difference of Korean font legibility between printed paper and VDT. Four Korean fonts, Dodum and Gulim - sans serif types, and Batang and Gungseo - serif types, were compared in experiments. The result showed that there was a significant difference in the font legibility for printed paper, but not for VDT. On printed paper, serif types were more legible than sans serif types. However, a significant difference between serif types and sans serif types were not found in VDT. Dodum, one of the sans serif font types achieved the highest score on preference in the survey questionnaire among the four Korean fonts. The paper suggests that serif types did not always provide the optimal legibility in VDT.
Calibration is an important process in traffic microsimulation modelling since it enables to represent reality more accurately. In this research we present a general methodology for the calibration process and develop...
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ISBN:
(纸本)9781509018901
Calibration is an important process in traffic microsimulation modelling since it enables to represent reality more accurately. In this research we present a general methodology for the calibration process and develop a calibration software for Transmodcler traffic microsimulation. This software follows this methodology and has specific methods for its main steps. We propose an experiment design model to select parameters. In addition we used two genetic algorithms to search the optimal values for the parameters. Besides, this software presents an innovative module to explore and visualize the output of the calibration process. In this paper we present the general design of the software which we tested, alongside our methodology, in a case of study in the city of Pereira, Colombia. The results show that it can find optimal values for the parameters and give the user a global view of the process. It indicates that this software has the capability to calibrate traffic microsimulation models.
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