Fingerprinting attacks have emerged as a serious threat against privacy mechanisms, such as SSL, Tor, and encrypting tunnels. Researchers have proposed numerous attacks and defenses, and the Tor project now includes both network-and browser-level defenses against these attacks, but published defenses have high overhead, poor security, or both. This paper (1) systematically analyzes existing attacks and defenses to understand which traffic features convey the most information (and therefore are most important for defenses to hide), (2) proves lower bounds on the bandwidth costs of any defense that achieves a given level of security, (3) presents a mathematical framework for evaluating performance of fingerprinting attacks and defenses in the open-world, given their closed-world performance, and (4) presents a new defense, Tamaraw, that achieves a better security/bandwidth trade-off than any previously proposed defense.Our feature-based analysis provides clear directions to defense designers on which features need to be hidden. Our lower bounds on bandwidth costs help us understand the limits of fingerprinting defenses and to determine how close we are to "success". Our open-world/close-world connection enables researchers to perform simpler closed-world experiments and predict open-world performance. Tamaraw provides an "existence proof" for efficient, secure defenses.

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.

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A major disturbance in nature, drought, has a significant impact on the vulnerability and resilience of semi-arid ecosystems by shifting phenology and productivity. However, due to the various disturbance mechanisms, phenology and primary productivity have remained largely ambiguous until now. This paper evaluated the spatio-temporal changes of phenology and productivity based on GIMMS NDVI3g time series data, and demonstrated the responses of vegetation phenology and productivity to drought disturbances with the standardized precipitation evapotranspiration index (SPEI) in semi-arid ecosystems of northern China. The results showed that (1): vegetation phenology exhibited dramatic spatial heterogeneity with different rates, mostly presented in the regions with high chances of land cover type variation. The delayed onset of growing season (SOS) and advanced end of growing season (EOS) occurred in Horqin Sandy Land and the eastern Ordos Plateau with a one to three days/decade (p < 0.05) rate and in the middle and east of Inner Mongolia with a two days/decade rate, respectively. Vegetation productivity presented a clear pattern: south increased and north decreased. (2) Spring drought delayed SOS in grassland, barren/sparsely vegetated land, and cropland, while autumn drought significantly advanced EOS in grassland and barren/sparsely vegetated lands. Annual drought reduced vegetation productivity and the sensitivity of productivity regarding drought disturbance was higher than that of phenology.

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

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