Abstract--Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of - explicit/implicit - graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution ofthese graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. I. INTRODUCTION In this paper, we consider the problem of graph-based classification: given a pattern (image, shape, handwritten character, documentetc.) Most of the early pattern classification methods were designed using numerical feature vectors resulting from statistical analysis [12], [29]. Other more successful extensions of these methods also integrate structural information (see for instance [27]). These extensions were built upon the assumption that parts, in patterns, do not appear independently and structural relationships among these parts are crucial in order to achieve effective description and classification [20].

Modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air Cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. For example, the camera of the TAIGA-IACT telescope has 560 pixels of hexagonal structure. Images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. The most powerful deep learning technique for image analysis, the so-called convolutional neural network (CNN), was implemented in this study. Two open source libraries for machine learning, PyTorch and TensorFlow, were tested as possible software platforms for particle identification in imaging air Cherenkov telescopes. Monte Carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. Further steps of implementation and improvement of this technique are discussed.

We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departure of such itineraries is to the user's search date the more likely they are to be cheaper than the tickets from one airline. We use these insights to formulate the problem as a trade-off between the recall of cheap itinerary constructions and the costs associated with building them. We propose a supervised learning solution with location embeddings which achieves an AUC=80.48, a substantial improvement over simpler baselines. We discuss various practical considerations for dealing with the staleness and the stability of the model and present the design of the machine learning pipeline. Finally, we present an analysis of the model's performance in production and its impact on Skyscanner's users.

Smart cooking based on Amazon Echo uses the internet of things and cloud computing to assist in cooking food. People may speak to Amazon Echo during the cooking in order to get the information and situation of the cooking. Amazon Echo recognizes what people say, then transfers the information to the cloud services, and speaks to people the results that cloud services make by querying the embedded cooking knowledge and achieving the information of intelligent kitchen devices online. An intelligent food thermometer and its mobile application are well-designed and implemented to monitor the temperature of cooking food.

Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. This work is supported by the MOE Academic fund AFD 05/15 SL and SUTD SRG ISTD 2017 129. Corresponding Author D. Herremans Singapore University of Technology and Design, Singapore & Institute for High Performance Computing, A*STAR, Singapore Email: dorien herremans@sutd.edu.sg 1 INTRODUCTION to compete with cutting-edge singing voice separation systems which use multichannel modeling,data augmentation, and model blending. Keywords Singing Voice Separation · Convolutional Neural Network · Ideal Binary Mask · Cross Entropy · Pixel-wise Image Classification 1 Introduction Humans have an exceptional ability to separate different sounds from a musical signal [3]. For instance, some musicians can distinguish the guitar part from a song and transcribe it; and most non-musician listeners are able to hear and sing along to lyrics of a song.

Some laughed at him, others told him that he wouldn't succeed. But with a strong will and positive attitude, Abdulrahman Abu Rawaa proved them wrong. With just one arm and a leg each, he can easily ride his bike along Gaza's sandy streets. He took off the pedal and chains to adjust the bike to his needs, allowing himself to easily balance on the bike and push himself forward. It's the easiest way for him to get around his neighbourhood in the "Bedouin Village" in the northern Gaza Strip.

Businesses will be ready for Web 3.0 and web 4.0 evolution which is connecting all devices in the real and virtual world in real-time.This will be more prominent in 2019. By 2020 we will be witnessed to the web 4.0 aka smart web when SEO will be less time consuming and easy. Defamation will be more ineffective and targeted bullying against brands, politicians, influencers and individuals will lose its power on social network. Since some advertising tools is equipped with Blockchain technology, there will be some improvements in digital marketing processes. Here are some insights on how artificial intelligence and Blockchain will shape up SEO and digital marketing in 2019.

KUCHING: Artificial intelligence (AI) is going to be the future of news replacing the print and social media, predicts Chief Minister Datuk Patinggi Abang Johari Tun Openg. He said now, there are books that claim that machines can think, which means there is already a technology that enables the technology to think. He noted that if a car can run without a driver, one day there will be a technology that can write stories through the mind of the technology. "I have a strong feeling that artificial intelligence will be the third era after the social media. "I foresee that this will happen and if it happens, it means the business profile in terms of news dissemination will also be changed," he said at the official launching of the Zamalah Wartawan Malaysia organised by the Malaysian Press Institute (MPI) here today. The Chief Minister said AI is already in use and therefore media practitioner, especially the young ones must be prepared for the next era, otherwise they will be left behind. He noted that even views or opinions can sometimes be expressed through the AI, be it positive or negative, depending on who makes the software and who makes the line. "This, I believe, will happen and it will certainly influence the social and political dimension as well as the community.

Spotting similarities, basking in patterns and drawing analogies are unique characteristics, that we as a species have imbibed into our biological formula. With biomimetics, we have set new standards for human intelligence. Deep learning algorithms like neural networks are one such byproduct of our innate desire to master this world and play God. A study aimed at AI safety by building realistic simulations of simple organisms like fruit flies and zebrafish has been published by Gopal Sarma and his team in collaboration with Vicarious AI . The roots of this approach are structured around neuropsychology.

No one is immune to products with shiny packaging, newfangled features, and high list prices--even when you're paid to be a skeptical reviewer of these things. At my last job, I welcomed a constant parade of lotions and socks and blow dryers into my apartment. The goods with fancy logos and trendy colors were the ones that made me thrill at my work. They felt nice to unwrap. They felt glamorous to take selfies with. Some nice things turn out to be duds. A $200 bright yellow hair dryer that I reviewed was slower and heavier than the ones you could find at a drugstore. After consulting several dentists, I learned that a luxury light-up teeth whitener works as well as Crest White Strips. There was a brand-name leather briefcase that my colleagues liked, but I found it was too skinny to tote around my personal essentials, like gym clothes and a bottle of wine. A pair of dog boots that cost as much as shoes for a small human being had trouble staying on my petite beagle's feet for more than 10 steps. More often, nice things can prove exactly as useful as their cheaper counterparts: the high-end treadmill made with the same parts as the version sold at Walmart, the sunscreen bottled and sold like a rare and fancy potion with identical active ingredients to much of the stuff available at the drugstore in bulk. There is simply an upper limit to how well a thing can work, which is why you're reading this, our list of holiday gift ideas that are good--and more importantly, Good Enough.