At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data securi...
At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data security risks and status quo of industrial Internet platforms. Among them, the security management strategy includes data classification and classification, data security assessment and data security threat emergency management. The security protection strategy deploys industrial data security protection from the technical aspects of collection, storage, transmission, processing, sharing, and deletion, providing a reference for further strengthening the industrial Internet data security.
In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process b...
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In this article authors proposed the fuzzy controller for the correct diagnosis of the breast pathological states. This mean can be used in medical practice by the cytologist as an additional way of diagnosis confirmi...
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We address the problem of attack detection and attack correction for multi-input multi-output discrete-time linear time-invariant systems under sensor attacks. More specifically, we consider the situation that a syste...
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Open Educational Resources (OERs) are freely accessible materials for teaching, learning, and research that have been made available such that they can be freely used, modified, and shared. Prompted by the potential p...
ISBN:
(数字)9781728126654
ISBN:
(纸本)9781728126661
Open Educational Resources (OERs) are freely accessible materials for teaching, learning, and research that have been made available such that they can be freely used, modified, and shared. Prompted by the potential positive impact that OERs can have, a growing community of OER enthusiasts has been advocating for their adoption in higher education, and elsewhere. The growing demand for computer science education and training (especially in areas related to artificial intelligence, machine learning, and big data) has led to speculation as to how OERs may be best leveraged to support such teaching and learning. The recent occasion of an international OER workshop at the 2019 IEEE Conference on Computers, Software and Applications (COMPSAC) allowed for six OER enthusiasts, all with extensive OER experience, to reflect on the status of OER adoption and understanding. These reflections, addressing misconceptions and challenges, form the basis for this paper.
Thanks to the automation of diagnostic processes based on the EEG signal and the creation of classifications of various attention disorders based on the analysis of the EEG signals (as diagnostic markers) - it may be ...
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Thanks to the automation of diagnostic processes based on the EEG signal and the creation of classifications of various attention disorders based on the analysis of the EEG signals (as diagnostic markers) - it may be possible to refer patients to appropriate specialists and provide them with appropriate neurofeedback-based treatment. The marker method proposed by the authors of the hereof paper is based on analysis of the EEG, which signal can be widely use in screening diagnostics. The proposed solution in this work may be used for neurofeedback or Brain Computer Interfaces (BCI). In this paper the authors presented implementation of markers for the purpose of analysis of some memory disorders and short-time attention disorder. The study is still in progress. Presented results have initial-study character.
A new approach to the integration of the results of several tools aimed at solving a single problem is considered. The approach is illustrated by the example of solving the authentication task for signature dynamics b...
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A new approach to the integration of the results of several tools aimed at solving a single problem is considered. The approach is illustrated by the example of solving the authentication task for signature dynamics based on the naive Bayesian classifier and the neural network. The approach guarantees results not worse than any of the classifiers separately from the point of view of a monotonous combination of the probabilities of errors of the first and second kind. The obtained results can be applied in the construction of a multifactor authentication system.
In this paper some preliminary results of network anomaly detection for resource-constrained systems have been presented. All described algorithms are embedded into a three-tier system application. The presented solut...
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In this paper some preliminary results of network anomaly detection for resource-constrained systems have been presented. All described algorithms are embedded into a three-tier system application. The presented solution is a response to nowadays trends in computer/network security domain that indicate gradual shifting from signature-based anomaly detection typically based on some selected statistical measures toward systems that are based on behavioural analysis of computer systems and/or networks. The presented system requirements, explain relevant terminology as well as we include some selected results and their analysis. The solution is potentially destined for very resource-constrained systems (IoT specifically), all tests were carried out in an example network to additionally analyse their effect of the presented approach on the computer resources.
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