Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the $K$-Nearest Neighboring (KNN) algorithm to defend against such attacks. However, such an approach can suffer from low to wrong detection accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on Silhouette clustering method, modified for mobile Android platforms. We proposed two Convolutional Neural Network (CNN)-type deep learning algorithms against this \emph{Silhouette Clustering-based Label Flipping Attack (SCLFA)}. We show the effectiveness of these two defense algorithms - \emph{Label-based Semi-supervised Defense (LSD)} and \emph{clustering-based Semi-supervised Defense (CSD)} - in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19\% accuracy when compared with the state-of-the-art method in the literature.

We are living in an age where almost any information is just a swipe away. There are innumerable blogs, videos, articles, papers and even podcasts on any topic that you want to consume information on. There are so many school thoughts in each and a lot of information on each in the internet. You are kinda getting the gist of where I am getting at. There is just so much information out there that it causes analysis paralysis and we become just consumers of information with nothing to act on.

With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT. In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. In doing so, we efficiently compute the transitivity between feature latent embeddings, that is if feature pairs have high interaction weights in the latent space, then feature embeddings with no observed interactions across the domains will be closely related as well. We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent. In addition, to better capture the non-linearity in user preferences across domains we extend the proposed CDT model by using a deep learning strategy, namely DeepCDT. Our experiments on six publicly available cross-domain tasks demonstrate the effectiveness of the proposed models, outperforming other state-of-the-art cross-domain strategies.

Acquiring new robot motor skills is cumbersome, as learning a skill from scratch and without prior knowledge requires the exploration of a large space of motor configurations. Accordingly, for learning a new task, time could be saved by restricting the parameter search space by initializing it with the solution of a similar task. We present a framework which is able of such knowledge transfer from already learned movement skills to a new learning task. The framework combines probabilistic movement primitives with descriptions of their effects for skill representation. New skills are first initialized with parameters inferred from related movement primitives and thereafter adapted to the new task through relative entropy policy search. We compare two different transfer approaches to initialize the search space distribution with data of known skills with a similar effect. We show the different benefits of the two knowledge transfer approaches on an object pushing task for a simulated 3-DOF robot. We can show that the quality of the learned skills improves and the required iterations to learn a new task can be reduced by more than 60% when past experiences are utilized.

Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform sear ches over encrypted cloud data outsourced by data owners. However, im plement-ing secure and precise query, efficient search and flexible dyn amic system maintenance at the same time in SSE remains a challenge. To ad dress this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on sea rching adversarial networks. We exploit searching adversarial netw orks to achieve optimal pseudo-keyword padding, and obtain the optimal gam e equilibrium for query precision and privacy protection strength. M aximum likelihood search balanced tree is generated by probabilistic l earning, which achieves efficient search and brings the computational compl exity close to O (log N). In addition, we enable flexible dynamic system maintenanc e with balanced index forest that makes full use of distribute d computing. Compared with previous works, our solution maintains query precision above 95% while ensuring adequate privacy protection, and i ntroduces low overhead on computation, communication and storage.

In this series I am going to provide you very brief introduction about time series analysis. Lets explore some basic terms used in time series. It is a increase or decrease of behavior of data over a period of time. It can be linear or non-leaner. If there is upward or increase behavior called as Up-Trend, same for decrease know as Down-Trend. When there is no trend that is known as stationary trend or horizontal trend.Trend appear for some time and disappear.

Abstract: Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Ear ly prediction of sepsis can improve situational awareness amongst clinicians and facilitate timely, protective interventions. While the application of predictive analytics in ICU patients has shown early promising results, much of the work has been encumbe red by high false - alarm rates. Efforts to improve specificity have been limited by several factors, most notably the difficulty of labeling sepsis onset time and the low prevalence of septic - events in the ICU. We show that by coupling a clinical criterion for defining sepsis onset time with a treatment policy (e.g., initiation of antibiotics within one hour of meeting the criterion), one may rank the relative utility of various criteria through offline policy evaluation. Given the optimal criterion, DeepAISE automatically learns predictive features related to higher - order interactions and temporal patterns among clinic al risk factors that maximize the data likelihood of observed time to septic events. DeepAISE has been incorporated into a clinical workflow, which provides real - time hourly sepsis risk scores. A comparative study of four baseline models indicates that Dee pAISE produces the most accurate predictions (AUC 0.90 and 0.87) and the lowest false alarm rates (FAR 0.20 and 0.26) in two separate cohorts (internal and external, respectively), while simultaneously producing interpretable representations of the clinica l time series and risk factors. Introduction Sepsis is a syndromic, life - threatening condition that arises when the body's response to infection injures its own internal organs (1) . Though the condition lacks the same public notoriety as other conditions like heart attacks, 6% of all hospitalized patients in the U nited S tates carry a primary diagnosis of sepsis as compared to 2.5% for the latter (2) . When all hospital deaths are ultimately considered, nearly 35% are attributable to sepsis (2) . This condition stands in stark contrast to heart attacks which have a mortality rate of 2.7 - 9.6% and only cost the US $12.1 billion ann ually, roughly half of the cost of sepsis (3) .

Transcriptomics response of SK-N-AS cells to methamidophos (an acetylcholine esterase inhibitor) exposure was measured at 10 time points between 0.5 and 48 h. The data was analyzed using a combination of traditional statistical methods and novel machine learning algorithms for detecting anomalous behavior and infer causal relations between time profiles. We identified several processes that appeared to be upregulated in cells treated with methamidophos including: unfolded protein response, response to cAMP, calcium ion response, and cell-cell signaling. The data confirmed the expected consequence of acetylcholine buildup. In addition, transcripts with potentially key roles were identified and causal networks relating these transcripts were inferred using two different computational methods: Siamese convolutional networks and time warp causal inference. Two types of anomaly detection algorithms, one based on Autoencoders and the other one based on Generative Adversarial Networks (GANs), were applied to narrow down the set of relevant transcripts.

We study the problem of rare event prediction for a class of slow-fast nonlinear dynamical systems. The state of the system of interest is described by a slow process, whereas a faster process drives its evolution. By taking advantage of recent advances in machine learning, we present a data-driven method to predict the future evolution of the state. We show that our method is capable of predicting a rare event at least several time steps in advance. We demonstrate our method using numerical experiments on two examples and discuss the mathematical and broader implications of our results.

Time series prediction with missing values is an important problem of time series analysis since complete data is usually hard to obtain in many real-world applications. To model the generation of time series, autoregressive (AR) model is a basic and widely used one, which assumes that each observation in the time series is a noisy linear combination of some previous observations along with a constant shift. To tackle the problem of prediction with missing values, a number of methods were proposed based on various data models. For real application scenarios, how do these methods perform over different types of time series with different levels of data missing remains to be investigated. In this paper, we focus on online methods for AR-model-based time series prediction with missing values. We adapted five mainstream methods to fit in such a scenario. We make detailed discussion on each of them by introducing their core ideas about how to estimate the AR coefficients and their different strategies to deal with missing values. We also present algorithmic implementations for better understanding. In order to comprehensively evaluate these methods and do the comparison, we conduct experiments with various configurations of relative parameters over both synthetic and real data. From the experimental results, we derived several noteworthy conclusions and shows that imputation is a simple but reliable strategy to handle missing values in online prediction tasks.