Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed. The raw data generated by distributed network terminals are collected and processed by edge servers. The edge servers split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of limited edge computation ability, part of the raw data in huge data sources may be directly transmitted to the cloud. To manage limited resources online, we propose an algorithm based on Lyapunov optimization to jointly optimize the policy of edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay and saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.

We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistically Discriminative Sub-trajectory Mining (SDSM) for this problem. An advantage of the SDSM method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a false positive sub-trajectory is smaller than a specified significance threshold alpha (e.g., 0.05), which is indispensable when the method is used in scientific or social studies under noisy environment. Finding such statistically discriminative sub-trajectories from massive trajectory dataset is both computationally and statistically challenging. In the SDSM method, we resolve the difficulties by introducing a tree representation among sub-trajectories and running an efficient permutation-based statistical inference method on the tree. To the best of our knowledge, SDSM is the first method that can efficiently extract statistically discriminative sub-trajectories from massive trajectory dataset. We illustrate the effectiveness and scalability of the SDSM method by applying it to a real-world dataset with 1,000,000 trajectories which contains 16,723,602,505 sub-trajectories.

For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency. Towards achieving this goal, we develop a differentiable NAS solution, where the search space includes arbitrary feed-forward network consisting of the predefined number of connections. Benefiting from a proposed ensemble Gumbel-Softmax estimator, our method optimizes both the architecture of a deep network and its parameters in the same round of backward propagation, yielding an end-to-end mechanism of searching network architectures. Extensive experiments on a variety of popular datasets strongly evidence that our method is capable of discovering high-performance architectures, while guaranteeing the requisite efficiency during searching.

A large body of recent work has investigated the phenomenon of evasion attacks using adversarial examples for deep learning systems, where the addition of norm-bounded perturbations to the test inputs leads to incorrect output classification. Previous work has investigated this phenomenon in closed-world systems where training and test inputs follow a pre-specified distribution. However, real-world implementations of deep learning applications, such as autonomous driving and content classification are likely to operate in the open-world environment. In this paper, we demonstrate the success of open-world evasion attacks, where adversarial examples are generated from out-of-distribution inputs (OOD adversarial examples). In our study, we use 11 state-of-the-art neural network models trained on 3 image datasets of varying complexity. We first demonstrate that state-of-the-art detectors for out-of-distribution data are not robust against OOD adversarial examples. We then consider 5 known defenses for adversarial examples, including state-of-the-art robust training methods, and show that against these defenses, OOD adversarial examples can achieve up to 4$\times$ higher target success rates compared to adversarial examples generated from in-distribution data. We also take a quantitative look at how open-world evasion attacks may affect real-world systems. Finally, we present the first steps towards a robust open-world machine learning system.

We describe a method for unmixing mixtures of freely independent random variables in a manner analogous to the independent component analysis (ICA) based method for unmixing independent random variables from their additive mixtures. Random matrices play the role of free random variables in this context so the method we develop, which we call Free component analysis (FCA), unmixes matrices from additive mixtures of matrices. We describe the theory, the various algorithms, and compare FCA to ICA. We show that FCA performs comparably to, and often better than, ICA in every application, such as image and speech unmixing, where ICA has been known to succeed. Our computational experiments suggest that not-so-random matrices, such as images and spectrograms of waveforms are (closer to being) freer "in the wild" than we might have theoretically expected.

We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide whether the fields should be sprayed. Our solution includes two groups of prediction models. The first group contains decision trees that predict migration time of Sunn Pest from winter quarters to wheat fields. The second group contains random forest models that predict the nymphal stage percentages of Sunn Pest which is a criterion for pesticide application. We trained our models on four years of climate data which was collected from Kir\c{s}ehir and Aksaray. The experiments show that our promised solution make correct predictions with high accuracies.

Veg out this weekend and still get all the best deals online on robot vacuums, sheets, and more. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. Finally it's your time to sleep in, relax a bit, and just generally do the things that you enjoy most, for no other reason besides the fact that you finally can. It's also the perfect time to indulge in a little retail therapy, but if you're not too keen on dealing with big crowds in stores, don't worry.

Soon, you won't have to struggle to decide what to eat from the McDonald's menu. The fast food giant is rolling out high-tech menus at 700 restaurants across the country that use artificial intelligence to suggest items. It comes after McDonald's acquired Israeli digital startup Dynamic Yield in March. McDonald's is launching high-tech kiosks at 700 restaurants across the country that use AI to suggest items. McDonald's purchased Dynamic Yield, an Israeli company which employs data and analytics to increase sales.

Extraterrestrial life, that familiar science-fiction trope, that kitschy fantasy, that CGI nightmare, has become a matter of serious discussion, a'risk factor', a'scenario'. How has ET gone from sci-fi fairytale to a serious scientific endeavour modelled by macroeconomists, funded by fiscal conservatives and discussed by theologians? Because, following a string of remarkable discoveries over the past two decades, the idea of alien life is not as far-fetched as it used to seem. Discovery now seems inevitable and possibly imminent. Extraterrestrial life, that familiar science-fiction trope, that kitschy fantasy, that CGI nightmare, has become a matter of serious discussion, a'risk factor', a'scenario'.

In a previously undocumented use of facial recognition software, police in Washington state are using Amazon's'Rekognition' to track down criminals with as little as an artist's sketch. According to a report from The Washington Post, police in Washington County are able to compare pictures of suspects harvested from security cameras and eye-witness' cell phone pictures against databases containing 300,000 mugshots of known criminals. In just Washington County Police Department alone, the report states more than 1,000 facial scans were logged last year which have helped identify subjects, sometimes leading officers to home arrests. Amazon's facial recognition software is being used to process criminal sketches in an unprecedented deployment of the technology in law enforcement. While law enforcement say the software has been a critical tool in expediting investigations and tracking down otherwise elusive criminals, skeptics say the use of facial recognition opens up a proverbial Pandora's Box of mass surveillance that could lead to more false identifications.