OpenRaiser/Intern-Atlas huggingface dataset:
IoT for Green Building Management
Venue: arXiv cs.OH 2018
Year: 2018

Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small to medium-sized buildings. As such, the Internet of Things (IoT), which can monitor and collect a large amount of data on different contexts of a building and feed the data to the processor of the BMS, provides a new opportunity to integrate intelligence into the BMS to monitor and manage the energy consumption of the building in a cost-effective manner. Although an extensive literature is available on IoT based BMS and applications of signal processing techniques for some aspects of building energy management separately, detailed study on their integration to address the overall BMS is quite limited. As such, the proposed paper will address this gap by providing an overview of an IoT based BMS leveraging signal processing and machine learning techniques. It is demonstrated how to extract high-level building occupancy information through simple and low-cost IoT sensors and studied the impact of human activities on energy usage of a building, which can be exploited to design energy conservation measures to reduce the building's energy consumption.
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
Venue: arXiv cs.CV 2018
Year: 2018

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream approaches for enhanced scan efficiency are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. In reconstruction, performance decreases towards higher acceleration factors with diminished sampling density particularly at high-spatial-frequencies. In synthesis, the absence of data samples from the target contrast can lead to artefactual sensitivity or insensitivity to image features. Here we propose a new approach for synergistic reconstruction-synthesis of multi-contrast MRI based on conditional generative adversarial networks. The proposed method preserves high-frequency details of the target contrast by relying on the shared high-frequency information available from the source contrast, and prevents feature leakage or loss by relying on the undersampled acquisitions of the target contrast. Demonstrations on brain MRI datasets from healthy subjects and patients indicate the superior performance of the proposed method compared to previous state-of-the-art. The proposed method can help improve the quality and scan efficiency of multi-contrast MRI exams.
A Formal Model of the Safety-Critical Java Level 2 Paradigm
Venue: arXiv cs.SE 2018
Year: 2018

Safety-Critical Java (SCJ) introduces a new programming paradigm for applications that must be certified. The SCJ specification (JSR 302) is an Open Group Standard, but it does not include verification techniques. Previous work has addressed verification for SCJ Level~1 programs. We support the much more complex SCJ Level~2 programs, which allows the programming of highly concurrent multi-processor applications with Java threads, and wait and notify mechanisms. We present a formal model of SCJ Level~2 that captures the state and behaviour of both SCJ programs and the SCJ API. This is the first formal semantics of the SCJ Level~2 paradigm and is an essential ingredient in the development of refinement-based reasoning techniques for SCJ Level~2 programs. We show how our models can be used to prove properties of the SCJ API and applications.
A Neurobiological Evaluation Metric for Neural Network Model Search
Venue: arXiv cs.CV 2018
Year: 2018

Neuroscience theory posits that the brain's visual system coarsely identifies broad object categories via neural activation patterns, with similar objects producing similar neural responses. Artificial neural networks also have internal activation behavior in response to stimuli. We hypothesize that networks exhibiting brain-like activation behavior will demonstrate brain-like characteristics, e.g., stronger generalization capabilities. In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior. To calculate HMS, representational dissimilarity matrices (RDMs) are created as abstractions of activation behavior, measured by the correlations of activations to stimulus pairs. HMS is then the correlation between the fMRI RDM and the neural network RDM across all stimulus pairs. We test the metric on unsupervised predictive coding networks, which specifically model visual perception, and assess the metric for statistical significance over a large range of hyperparameters. Our experiments show that networks with increased human-model similarity are correlated with better performance on two computer vision tasks: next frame prediction and object matching accuracy. Further, HMS identifies networks with high performance on both tasks. An unexpected secondary finding is that the metric can be employed during training as an early-stopping mechanism.
Software-Defined Multi-Cloud Computing: A Vision, Architectural Elements, and Future Directions
Venue: arXiv cs.DC 2018
Year: 2018

Cloud computing has been emerged in the last decade to enable utility-based computing resource management without purchasing hardware equipment. Cloud providers run multiple data centers in various locations to manage and provision the Cloud resources to their customers. More recently, the introduction of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) opens more opportunities in Clouds which enables dynamic and autonomic configuration and provisioning of the resources in Cloud data centers. This paper proposes architectural framework and principles for Programmable Network Clouds hosting SDNs and NFVs for geographically distributed Multi-Cloud computing environments. Cost and SLA-aware resource provisioning and scheduling that minimizes the operating cost without violating the negotiated SLAs are investigated and discussed in regards of techniques for autonomic and timely VNF composition, deployment and management across multiple Clouds. We also discuss open challenges and directions for creating auto-scaling solutions for performance optimization of VNFs using analytics and monitoring techniques, algorithms for SDN controller for scalable traffic and deployment management. The simulation platform and the proof-of-concept prototype are presented with initial evaluation results.
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance. Besides of pose, a capsule should be attached with a probability (often denoted as activation) for its presence. The dynamic routing helps capsules achieve more generalization capacity with many fewer model parameters. However, the bottleneck that prevents widespread applications of capsule is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, and propose two fast routing methods with different optimization strategies. Our methods prompt the time efficiency of routing by nearly 40\% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of $64\times{64}$ pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.
NETRA: Enhancing IoT Security using NFV-based Edge Traffic Analysis
Venue: arXiv cs.CR 2018
Year: 2018

This is the era of smart devices or things which are fueling the growth of Internet of Things (IoT). It is impacting every sphere around us, making our life dependent on this technological feat. It is of high concern that these smart things are being targeted by cyber criminals taking advantage of heterogeneity, minuscule security features and vulnerabilities within these devices. Conventional centralized IT security measures have limitations in terms of scalability and cost. Therefore, these smart devices are required to be monitored closer to their location ideally at the edge of IoT networks. In this paper, we explore how some security features can be implemented at the network edge to secure these smart devices. We explain the importance of Network Function Virtualization (NFV) in order to deploy security functions at the network edge. To achieve this goal, we introduce NETRA - a novel lightweight Docker-based architecture for virtualizing network functions to provide IoT security. Also, we highlight the advantages of the proposed architecture over the standardized NFV architecture in terms of storage, memory usage, latency, throughput, load average, scalability and explain why the standardized architecture is not suitable for IoT. We study the performance of proposed NFV based edge analysis for IoT security and show that attacks can be detected with more than 95% accuracy in less than a second.
Parallel Louvain Community Detection Optimized for GPUs
Venue: arXiv cs.DC 2018
Year: 2018

Community detection now is an important operation in numerous graph based applications. It is used to reveal groups that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential, the support for parallel computers is rather limited. The cause is largely the irregularity of the algorithm and the underlying heuristics imply a sequential nature. In this paper a GPU based parallelized version of the Louvain method is presented. The Louvain method is a multi-phase, iterative heuristic for modularity optimization. It was originally developed by Blondel et al. (2008), the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. The parallel heuristics used, were first introduced by Hao Lu et al. (2015). As the Louvain method is inherently sequential, it limits the possibility of scalable usage. Thanks to the proposed parallel heuristics, I observe how this method can behave on GPUs. For evaluation I implemented the heuristics using CUDA on a GeForce GTX 980 GPU and for testing Ive used organization landscapes from the CERN developed Collaboration Spotting project that involves patents and publications to visualize the connections in technologies among its collaborators. Compared to the parallel Louvain implementation running on 8 threads on the same machine that has the used GPU, the CUDA implementation is able to produce community outputs comparable to the CPU generated results, while providing absolute speedups of up to 30 using the GeForce GTX 980 consumer grade GPU.
Particle-based simulations of reaction-diffusion processes with Aboria
Venue: arXiv cs.CE 2018
Year: 2018

Mathematical models of transport and reactions in biological systems have been traditionally written in terms of partial differential equations (PDEs) that describe the time evolution of population-level variables. In recent years, the use of stochastic particle-based models, which keep track of the evolution of each organism in the system, has become widespread. These models provide a lot more detail than the population-based PDE models, for example by explicitly modelling particle-particle interactions, but bring with them many computational challenges. In this paper we overview Aboria, a powerful and flexible C++ library for the implementation of numerical methods for particle-based models. We demonstrate the use of Aboria with a commonly used model in mathematical biology, namely cell chemotaxis. Cells interact with each other and diffuse, biased by extracellular chemicals, that can be altered by the cells themselves. We use a hybrid approach where particle-based models of cells are coupled with a PDE for the concentration of the extracellular chemical.
Online Influence Maximization with Local Observations
Venue: arXiv cs.LG 2018
Year: 2018

We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node. The node transmits the information to some others that are in the same connected component in a random graph. The goal of the decision maker is to reach as many nodes as possible, with the added complication that feedback is only available about the degree of the selected node. Our main result shows that such local observations can be sufficient for maximizing global influence in two broadly studied families of random graph models: stochastic block models and Chung--Lu models. With this insight, we propose a bandit algorithm that aims at maximizing local (and thus global) influence, and provide its theoretical analysis in both the subcritical and supercritical regimes of both considered models. Notably, our performance guarantees show no explicit dependence on the total number of nodes in the network, making our approach well-suited for large-scale applications.
Reward Constrained Policy Optimization
Venue: arXiv cs.LG 2018
Year: 2018

Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general constraints. In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one. We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Venue: arXiv cs.CV 2018
Year: 2018

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge.
A visual approach for age and gender identification on Twitter
Venue: arXiv cs.CL 2018
Year: 2018

The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task.
Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment.
FairGAN: Fairness-aware Generative Adversarial Networks
Venue: arXiv cs.LG 2018
Year: 2018

Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative adversarial networks (GAN), we present fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility. Compared with the naive fair data generation models, FairGAN further ensures the classifiers which are trained on generated data can achieve fair classification on real data. Experiments on a real dataset show the effectiveness of FairGAN.
Surface Light Field Compression using a Point Cloud Codec
Venue: arXiv cs.MM 2018
Year: 2018

Light field (LF) representations aim to provide photo-realistic, free-viewpoint viewing experiences. However, the most popular LF representations are images from multiple views. Multi-view image-based representations generally need to restrict the range or degrees of freedom of the viewing experience to what can be interpolated in the image domain, essentially because they lack explicit geometry information. We present a new surface light field (SLF) representation based on explicit geometry, and a method for SLF compression. First, we map the multi-view images of a scene onto a 3D geometric point cloud. The color of each point in the point cloud is a function of viewing direction known as a view map. We represent each view map efficiently in a B-Spline wavelet basis. This representation is capable of modeling diverse surface materials and complex lighting conditions in a highly scalable and adaptive manner. The coefficients of the B-Spline wavelet representation are then compressed spatially. To increase the spatial correlation and thus improve compression efficiency, we introduce a smoothing term to make the coefficients more similar across the 3D space. We compress the coefficients spatially using existing point cloud compression (PCC) methods. On the decoder side, the scene is rendered efficiently from any viewing direction by reconstructing the view map at each point. In contrast to multi-view image-based LF approaches, our method supports photo-realistic rendering of real-world scenes from arbitrary viewpoints, i.e., with an unlimited six degrees of freedom (6DOF). In terms of rate and distortion, experimental results show that our method achieves superior performance with lighter decoder complexity compared with a reference image-plus-geometry compression (IGC) scheme, indicating its potential in practical virtual and augmented reality applications.
Bi-Directional Neural Machine Translation with Synthetic Parallel Data
Venue: arXiv cs.CL 2018
Year: 2018

Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.
Getting to Know Low-light Images with The Exclusively Dark Dataset
Venue: arXiv cs.CV 2018
Year: 2018

Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light has seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark dataset such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing visualizations of both hand-crafted and learned features. Most importantly, we found that the effects of low-light reaches far deeper into the features than can be solved by simple "illumination invariance'". It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The Exclusively Dark dataset with its annotation is available at https://github.com/cs-chan/Exclusively-Dark-Image-Dataset