Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Matrix Factorization Techniques for Recommender Systems

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

/pdf/edge-intelligence-paving-the-last-mile-of-artificial-19sytmrpiq.pdf

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

There are two kinds of tutorial articles: those that provide a primer on an established topic and those that let us in on the ground floor of something of emerging importance. The first type of tutorial can have a noted expert who has been gracious (and brave) enough to write a field guide about a particular topic. The other sort of tutorial typically involves researchers who have each been laboring on a topic for some years. Both sorts of tutorial articles are very much desired. But we, as an editorial board for both Systems and Transactions, know that there has been no logical place for them in the AESS until this series was started several years ago. With these tutorials, we hope to continue to give them a home, a welcome, and provide a service to our membership. We do not intend to publish tutorials on a regular basis, but we hope to deliver them once or twice per year. We need and welcome good, useful tutorial articles (both kinds) in relevant AESS areas. If you, the reader, can offer a topic of interest and an author to write about it, please contact us. Self-nominations are welcome, and even more ideal is a suggestion of an article that the editor(s) can solicit. All articles will be reviewed in detail. Criteria on which they will be judged include their clarity of presentation, relevance, and likely audience, and, of course, their correctness and scientific merit. As to the mathematical level, the articles in this issue are a good guide: in each case the author has striven to explain complicated topics in simple-well, tutorial-terms. There should be no (or very little) novel material: the home for archival science is the Transactions Magazine, and submissions that need to be properly peer reviewed would be rerouted there. Likewise, articles that are interesting and descriptive, but lack significant tutorial content, ought more properly be submitted to the Systems Magazine.

What is a tutorial

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean Fl score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.

Understanding and improving recurrent networks for human activity recognition by continuous attention

We present FootprintID, an indoor pedestrian identification system that utilizes footstep-induced structural vibration to infer pedestrian identities for enabling various smart building applications. Previous studies have explored other sensing methods, including vision-, RF-, mobile-, and acoustic-based methods. They often require specific sensing conditions, including line-of-sight, high sensor density, and carrying wearable devices. Vibration-based methods, on the other hand, provide easy-to-install sparse sensing and utilize gait to distinguish different individuals. However, the challenge for these methods is that the signals are sensitive to the gait variations caused by different walking speeds and the floor variations caused by structural heterogeneity. We present FootprintID, a vibration-based approach that achieves robust pedestrian identification. The system uses vibration sensors to detect footstep-induced vibrations. It then selects vibration signals and classifiers to accommodate sensing variations, taking step location and frequency into account. We utilize the physical insight on how individual step signal changes with walking speeds and introduce an iterative transductive learning algorithm (ITSVM) to achieve robust classification with limited labeled training data. When trained only on the average walking speed and tested on different walking speeds, FootprintID achieves up to 96% accuracy and a 3X improvement in extreme speeds compared to the Support Vector Machine. Furthermore, it achieves up to 90% accuracy (1.5X improvement) in uncontrolled experiments.

/pdf/footprintid-indoor-pedestrian-identification-through-ambient-3kghy25uat.pdf

FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing

Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consumption for analyzing sensor data must be low, since most wearables and portable devices are power-strapped. Second, the velocity of analyzing big data on these devices must be high, otherwise the limited local storage may overflow.This paper presents our hardware-software co-design of a classifier for wearables to detect a person's transportation mode (i.e., still, walking, running, biking, and on a vehicle). We particularly focus on addressing the big-data small-footprint requirement by designing a classifier that is low in both computational complexity and memory requirement. Together with a sensor-hub configuration, we are able to drastically reduce power consumption by 99%, while maintaining competitive mode-detection accuracy. The data used in the paper is made publicly available for conducting research.

/pdf/big-data-small-footprint-the-design-of-a-low-power-3y243ipjo6.pdf

Big data small footprint: the design of a low-power classifier for detecting transportation modes

Traditional recommender systems rely on user feedback such as ratings or clicks to the items, to analyze the user interest and provide personalized recommendations. However, rating or click feedback are limited in that they do not exactly tell why users like or dislike an item. If a user does not like the recommendations and can not effectively express the reasons via rating and clicking, the feedback from the user may be very sparse. These limitations lead to inefficient model learning of the recommender system. To address these limitations, more effective user feedback to the recommendations should be designed, so that the system can effectively understand a user's preference and improve the recommendations over time. In this paper, we propose a novel dialog-based recommender system to interactively recommend a list of items with visual appearance. At each time, the user receives a list of recommended items with visual appearance. The user can point to some items and describe their feedback, such as the desired features in the items they want in natural language. With this natural language based feedback, the recommender system updates and provides another list of items. To model the user behaviors of viewing, commenting and clicking on a list of items, we propose a visual dialog augmented cascade model. To efficiently understand the user preference and learn the model, exploration should be encouraged to provide more diverse recommendations to quickly collect user feedback on more attributes of the items. We propose a variant of the cascading bandits, where the neural representations of the item images and user feedback in natural language are utilized. In a task of recommending a list of footwear, we show that our visual dialog augmented interactive recommender needs around 41.03% rounds of recommendations, compared to the traditional interactive recommender only relying on the user click behavior.

A Visual Dialog Augmented Interactive Recommender System

Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in real-world use cases. Semi-supervised learning augments labeled examples with unlabeled examples, often resulting in improved performance. However, the semi-supervised methods studied in the activity recognition literatures assume that feature engineering is already done. In this paper, we lift this assumption and present two semi-supervised methods based on convolutional neural networks (CNNs) to learn discriminative hidden features. Our semi-supervised CNNs learn from both labeled and unlabeled data while also performing feature learning on raw sensor data. In experiments on three real world datasets, we show that our CNNs outperform supervised methods and traditional semi-supervised learning methods by up to 18% in mean F1-score (F m ).

/pdf/semi-supervised-convolutional-neural-networks-for-human-3b0ddwmsak.pdf

Tong Yu

Papers

Understanding and improving recurrent networks for human activity recognition by continuous attention

FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing

Big data small footprint: the design of a low-power classifier for detecting transportation modes

A Visual Dialog Augmented Interactive Recommender System

Semi-supervised convolutional neural networks for human activity recognition