This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.

Machine and deep learning applications bring new workflows and challenges to enterprise data center architectures. One of the key challenges revolves around data and the storage solutions needed to store, manage, and deliver up to AI's demands. Today's intelligent applications require infrastructure that is very different from traditional analytics workloads, and an organization's data architecture decisions will have a big impact on the success of its AI projects. These are among the key takeaways from a new white paper by the research firm Moor Insights & Strategy. "While discussions of machine learning and deep learning naturally gravitate towards compute, it's clear that these solutions force new ways of thinking about data," the firm notes in its "Enterprise Machine & Deep Learning with Intelligent Storage" paper.

Deep Learning in 11 Lines of MATLAB Code See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings. This demo uses AlexNet, a pretrained deep convolutional neural network that has been trained on over a million images.

The motivation for attacking a vehicle is vast -- it can be hacked to gain personal information (as previously IoT devices were hacked), leak contacts or use its connectivity for DDoS attacks and similar traditional attacks, Newly crafted exotic attacks may be introduced, such as ransomware (and pay to unlock), GPU attack to mine cryptocurrencies (and pay for the electricity), or steering an autonomous truck carrying goods to a desired location, combining Spoofed traffic signs and GPS jamming (without breaking a single line of code). Attacking vehicles' Software 2.0, does not require traditional software hacking skills, nor does it even requires specific automotive technologies knowledge. With a plethora of published research papers (albeit, the vast majority of it is simulative), listed in Nicholas Carlini's homepage (a Google Brain researcher, specializing in adversarial networks), every single researcher is a published neural-nets hacker. Not everyone agrees, though, that adversarial-image-based attacks are a real threat. Chris Valasek & Charlie Miller, the 2015 hacked Jeep Cherokee hackers duo, have addressed in a 2018 paper that autonomous vehicles may be "less hackable than you think".

With convolutional neural networks and state-of-the-art image recognition techniques it is possible to make old movie classics shine again. Neural networks polish the image, reduce the noise and apply colors to the aged images. The first movies were created in the late nineteenth century with celluloid photographic film used in conjunction with motion picture cameras. Skip ahead to 2018, when the global movie industry was worth $41.7 billion globally. Serving entertainment, cultural and social purposes, films are a hugely important heritage to protect.

Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limited to image classification models. To overcome this limitation, we extend the existing approaches to generate grid saliencies, which provide spatially coherent visual explanations for (pixel-level) dense prediction networks. As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering which context most influences the class predictions inside a target object area. We investigate the effectiveness of grid saliency on a synthetic dataset with an artificially induced bias between objects and their context as well as on the real-world Cityscapes dataset using state-of-the-art segmentation networks. Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in the data.

Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet- 3D and transferred 2D ImageNet weights achieves a human comparable accuracy.

The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.

--Action recognition greatly benefits motion understanding in video analysis. Recurrent networks such as long short-term memory (LSTM) networks are a popular choice for motion-aware sequence learning tasks. Recently, a convolutional extension of LSTM was proposed, in which input-to-hidden and hidden-to-hidden transitions are modeled through convolution with a single kernel. This implies an unavoidable tradeoff between effectiveness and efficiency. Herein, we propose a new enhancement to convolutional LSTM networks that supports accommodation of multiple convolutional kernels and layers. This resembles a Network-in-LSTM approach, which improves upon the aforementioned concern. In addition, we propose an attention-based mechanism that is specifically designed for our multi-kernel extension. We evaluated our proposed extensions in a supervised classification setting on the UCF-101 and Sports-1M datasets, with the findings showing that our enhancements improve accuracy. We also undertook qualitative analysis to reveal the characteristics of our system and the convolutional LSTM baseline. CTION recognition is a challenging-yet-essential task in modern computer vision that is typically performed on video clips. Videos are now frequently encountered in our everyday lives on social media platforms such as Instagram, Facebook, and Y ouTube. Many applications can benefit from action recognition; for example, autonomous driving, security and surveillance, and sports analysis. Unlike static images, videos have an inherently spatiotemporal nature. The motion of subjects, such as persons, animals, or objects, carries significant information on the current action.

Despite the importance of handwritten numeral classification, a robust and effective method for a widely used language like Arabic is still due. This study focuses to overcome two major limitations of existing works: data diversity and effective learning method. Hence, the existing Arabic numeral datasets have been merged into a single dataset and augmented to introduce data diversity. Moreover, a novel deep model has been proposed to exploit diverse data samples of unified dataset. The proposed deep model utilizes the low-level edge features by propagating them through residual connection. To make a fair comparison with the proposed model, the existing works have been studied under the unified dataset. The comparison experiments illustrate that the unified dataset accelerates the performance of the existing works. Moreover, the proposed model outperforms the existing state-of-the-art Arabic handwritten numeral classification methods and obtain an accuracy of 99.59% in the validation phase. Apart from that, different state-of-the-art classification models have studied with the same dataset to reveal their feasibility for the Arabic numeral classification. Code available at http://github.com/sharif-apu/EdgeNet.