For constrained end devices in Internet of Things, such as smart meters (SMs), data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby SMs and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient, and practical, it requires only one round of data exchange among a SM, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node, and the trusted third party.

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's $\rho$ to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's $\rho$, which measures correlation between a set of ranked lists. Multivariate Spearman's $\rho$ is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's $\rho$. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008.

MOTIVATION: Many ChIP-Seq experiments are aimed at developing gold standards for determining the locations of various genomic features such as transcription start or transcription factor binding sites on the whole genome. Many such pioneering experiments lack rigorous testing methods and adequate 'gold standard' annotations to compare against as they themselves are the most reliable source of empirical data available. To overcome this problem, we propose a self-consistency test whereby a dataset is tested against itself. It relies on a supervised machine learning style protocol for in silico annotation of a genome and accuracy estimation to guarantee, at least, self-consistency. RESULTS: The main results use a novel performance metric (a calibrated precision) in order to assess and compare the robustness of the proposed supervised learning method across different test sets. As a proof of principle, we applied the whole protocol to two recent ChIP-Seq ENCODE datasets of STAT1 and Pol-II binding sites. STAT1 is benchmarked against in silicodetection of binding sites using available position weight matrices. Pol-II, the main focus of this paper, is benchmarked against 17 algorithms for the closely related and well-studied problem of in silico transcription start site (TSS) prediction. Our results also demonstrate the feasibility of in silico genome annotation extension with encouraging results from a small portion of annotated genome to the remainder. AVAILABILITY: Available fromhttp://www.genomics.csse.unimelb.edu.au/gat.

We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets. 1

BACKGROUND: When analysing microarray and other small sample size biological datasets, care is needed to avoid various biases. We analyse a form of bias, stratification bias, that can substantially affect analyses using sample-reuse validation techniques and lead to inaccurate results. This bias is due to imperfect stratification of samples in the training and test sets and the dependency between these stratification errors, i.e. the variations in class proportions in the training and test sets are negatively correlated. RESULTS: We show that when estimating the performance of classifiers on low signal datasets (i.e. those which are difficult to classify), which are typical of many prognostic microarray studies, commonly used performance measures can suffer from a substantial negative bias. For error rate this bias is only severe in quite restricted situations, but can be much larger and more frequent when using ranking measures such as the receiver operating characteristic (ROC) curve and area under the ROC (AUC). Substantial biases are shown in simulations and on the van 't Veer breast cancer dataset. The classification error rate can have large negative biases for balanced datasets, whereas the AUC shows substantial pessimistic biases even for imbalanced datasets. In simulation studies using 10-fold cross-validation, AUC values of less than 0.3 can be observed on random datasets rather than the expected 0.5. Further experiments on the van 't Veer breast cancer dataset show these biases exist in practice. CONCLUSION: Stratification bias can substantially affect several performance measures. In computing the AUC, the strategy of pooling the test samples from the various folds of cross-validation can lead to large biases; computing it as the average of per-fold estimates avoids this bias and is thus the recommended approach. As a more general solution applicable to other performance measures, we show that stratified repeated holdout and a modified version of k-fold cross-validation, balanced, stratified cross-validation and balanced leave-one-out cross-validation, avoids the bias. Therefore for model selection and evaluation of microarray and other small biological datasets, these methods should be used and unstratified versions avoided. In particular, the commonly used (unbalanced) leave-one-out cross-validation should not be used to estimate AUC for small datasets.