In this paper, we demonstrate a new way to perform continuous authentication using Mouse Dynamics as the behavioural biometric modality. In the proposed scheme, the user will be authenticated per mouse event performed...
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ISBN:
(纸本)9783885796060
In this paper, we demonstrate a new way to perform continuous authentication using Mouse Dynamics as the behavioural biometric modality. In the proposed scheme, the user will be authenticated per mouse event performed on his/her system. We have used a publicly available mouse dynamics dataset and extracted per event features suitable for the proposed scheme. In this research, we have used the mouse dynamics data of 49 users and evaluated the system performance with 6 machine learning algorithms. In this approach, the genuine user has never been classified as an impostor throughout a full session whereas the average number of mouse actions an impostor could perform before detection is 94 from the best classification algorithm with a person based threshold.
High-frequency models for radar backscatter can include components with different structures that correspond to different physical scattering mechanisms on an object. We consider the problem of structure selection for...
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ISBN:
(纸本)0780362934
High-frequency models for radar backscatter can include components with different structures that correspond to different physical scattering mechanisms on an object. We consider the problem of structure selection for an attributed scattering center model that includes both localized and distributed scattering terms. We propose three structure classification algorithms, and compare their performance. We show that a threshold test on the estimated length parameter performs as well as a GLRT test, but is computationally more efficient. A computationally fast image-based test is shown to perform as well as the GLRT and length-based tests for scattering center lengths greater than twice the crossrange resolution of the measurements.
Classification is a popular problem within machine learning, and increasing the effectiveness of classification algorithms has many significant applications within industry and academia. In particular, focus will be g...
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ISBN:
(纸本)9781479917372
Classification is a popular problem within machine learning, and increasing the effectiveness of classification algorithms has many significant applications within industry and academia. In particular, focus will be given to Higher-Order Naive Bayes (HONB), a relational variant of the famed Naive Bayes (NB) statistical classification algorithm that has been shown to outperform Naive Bayes in many cases [1,10]. Specifically, HONB has outperformed NB on character n-gram based feature spaces when the available training data is small [2]. In this paper, a correlation is hypothesized between the performance of HONB on character n-gram feature spaces and how closely the feature space distribution follows Zipf's Law. This hypothesis stems from the overarching goal of ultimately understanding HONB and knowing when it will outperform NB. Textual datasets ranging from several thousand instances to nearly 20,000 instances, some containing microtext, were used to generate character n-gram feature spaces. HONB and NB were both used to model these datasets, using varying character n-gram sizes (2-7) and dictionary sizes up to 5000 features. The performances of HONB and NB were then compared, and the results show potential support for our hypothesis: namely, the results support the hypothesized correlation for the Accuracy and Precision metrics. Additionally, a solution is provided for an open problem which was presented in [1], giving an explicit formula for the number of SDRs from k given sets, which has connections to counting higher-order paths of arbitrary length, which are important in the learning stage of HONB.
This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a ...
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ISBN:
(纸本)9781728155463
This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a novel mechanism referred to as SortAlgo-Metrics analysis to identify cloud-based servers. We deployed four cloud-based servers to run four sorting algorithms to allow features extraction that are used for analysis. Consequently, the model has been validated by comparing training data and the testing data with 96% probability. Therefore, with more complex properties pulled out from the cloud-based servers and advanced statistical model, SortAlgo-Metrics mechanism could generate a higher degree of basis numbers for ICMetrics technology entropy key generation for cloud-based server authentication, and other complex systems.
Sleep is essential for a healthy and productive life, yet its importance is largely overlooked, allowing populations to sleep less and to develop sleep disturbances. This trend results into an epidemic of poor quality...
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ISBN:
(纸本)9781538645550;9781538666302
Sleep is essential for a healthy and productive life, yet its importance is largely overlooked, allowing populations to sleep less and to develop sleep disturbances. This trend results into an epidemic of poor quality and insufficient sleep that is turn jeopardizes health, performance, mood, memory, social relationships and productivity. A first step to overcome this epidemic relies on uncovering it at the individual and societal levels. The availability of different wearable devices that can track physiological signals represents a great opportunity to define and quantify the problem. Many such devices incorporate a photoplethysmography (PPG) sensor. This triggers interest in studies aiming to get insight into human sleep structure based on information obtained from PPG sensors. This study aimed to validate a new automated sleep analysis which is simply based on the inter-beat-interval series obtained from PPG, and that uses features of heart rate variability. The candidate algorithm was tested against gold standard scoring of whole night state-of-the-art sleep studies. The PPG-based sleep scoring performs very well in differentiating sleep stages, however, the sleep/wake separation is not sufficient and requires improvement. This last task is facilitated by the fact that the majority of devices with PPG capabilities, are equipped with accelerometers providing additional information for better separation. Combining accelerometers and PPG signals from wearable devices in a sleep analyser is likely to provide a reliable and accurate automated detection of sleep and wakefulness, including sleep macro- and micro-architecture.
Breast cancer is the most commonly diagnosed form of cancer in women. Pattern classification approaches often have difficulties with breast cancer related datasets as the available training data are typically imbalanc...
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ISBN:
(纸本)9781467364003;9781467363976
Breast cancer is the most commonly diagnosed form of cancer in women. Pattern classification approaches often have difficulties with breast cancer related datasets as the available training data are typically imbalanced with many more benign cases recorded than malignant ones, leading to a bias in the classification and insufficient sensitivity. In this paper, we present an ensemble classification algorithm that addresses this problem by employing cost-sensitive decision trees as base classifiers which are trained on random feature subspaces to ensure diversity, and an evolutionary algorithm for simultaneous classifier selection and fusion. Experimental results on two different breast cancer datasets confirm our approach to work well and to provide boosted sensitivity compared to various other state-of-the-art ensembles.
Modular Multilevel has been widely applied in Flexible HVDC, electric locomotives and PET(Power Electronics Transformers). The modulation method is important for the converter performance. This paper presents an impro...
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ISBN:
(纸本)9783038350491
Modular Multilevel has been widely applied in Flexible HVDC, electric locomotives and PET(Power Electronics Transformers). The modulation method is important for the converter performance. This paper presents an improved modulation method for Modular Multilevel Converter.
Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of ...
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ISBN:
(纸本)9781424479290
Cardiovascular disease (CVD) is the major cause of death in the world. Clinical guidelines recommend the use of risk assessment tools (scores) to identify the CVD risk of each patient as the correct stratification of patients may significantly contribute to the optimization of the health care strategies. This work further explores the personalization of CVD risk assessment, supported on the evidence that a specific CVD risk assessment tool may have good performance within a given group of patients and might perform poorly within other groups. Two main personalization methods based on the proper creation of groups of patients are presented: i) clustering patients approach;ii) similarity measures approach. These two methodologies were validated in a Portuguese population (460 Acute Coronary Syndrome with non-ST segment elevation (ACS-NSTEMI) patients). The similarity measures approach had the best performance, achieving maximum values of sensitivity, specificity and geometric mean of, respectively, 77.7%, 63.2%, 69.7%. These values represent an enhancement in relation to the best performance obtained with current CVD risk assessment tools applied in clinical practice (78.5%, 53.2%, 64.4%).
Cognitive radio (CR) is a promising solution to improve the spectrum utilization. Spectrum handoff is an indispensable component in cognitive radio networks. There are two kinds of handoff scheme: proactive handoff an...
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ISBN:
(纸本)9788132225805;9788132225799
Cognitive radio (CR) is a promising solution to improve the spectrum utilization. Spectrum handoff is an indispensable component in cognitive radio networks. There are two kinds of handoff scheme: proactive handoff and reactive handoff. In reactive handoff, the predefined target spectrum list (PTSL) model can effectively improve the accuracy of handoff and reduce handoff latency. In this paper, we studied the sorting algorithm of PTSL model and put forward an improved algorithm. The traditional sorting algorithm just considered two elements, the channel bandwidth and quality. We add the predicted channel available time into consideration. When spectrum handoff occurs, the secondary user can choose the channel that not only the bandwidth and quality can satisfy the request, but also the available time can suit the business. Simulation results show that the improved algorithm can avoid the secondary handoff effectively and improve the performance of the whole system.
A recursive, odd-even transposition sorter based vector quantizer which is used in mismatch shaping algorithms is presented. Although recursive parallel sorting algorithms require less area than fully parallel sorting...
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ISBN:
(纸本)9781509043866
A recursive, odd-even transposition sorter based vector quantizer which is used in mismatch shaping algorithms is presented. Although recursive parallel sorting algorithms require less area than fully parallel sorting algorithms, they are slower than fully parallel algorithms. A widely used recursive parallel sorting algorithm is the perfect shuffle which requires multiple clock cycles to shuffle and sort the data. The proposed recursive algorithm uses fewer clock cycles than the perfect shuffle to sort less than 80 inputs. An area efficient version is also proposed to sort less than 16 inputs faster than perfect shuffle algorithm. To compare the performance of various sorting algorithms suitable for vector quantizer, they are realized and synthesized in TSMC 40nm low-power technology. Speed and area results indicate that the proposed algorithm sorts 32 inputs at a 42% faster rate by using 14% fewer components than the perfect shuffle sorter and a 80% slower rate by using 27% fewer components than the Bitonic sorter. The area efficient version sorts 32 inputs at a 21% slower rate by using 32% fewer components than the perfect shuffle sorter.
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