OpenRaiser/Intern-Atlas huggingface dataset:
An unsupervised and customizable misspelling generator for mining noisy health-related text sources
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we present a customizable datacentric system that automatically generates common misspellings for complex health-related terms. The spelling variant generator relies on a dense vector model learned from large unlabeled text, which is used to find semantically close terms to the original/seed keyword, followed by the filtering of terms that are lexically dissimilar beyond a given threshold. The process is executed recursively, converging when no new terms similar (lexically and semantically) to the seed keyword are found. Weighting of intra-word character sequence similarities allows further problem-specific customization of the system. On a dataset prepared for this study, our system outperforms the current state-of-the-art for medication name variant generation with best F1-score of 0.69 and F1/4-score of 0.78. Extrinsic evaluation of the system on a set of cancer-related terms showed an increase of over 67% in retrieval rate from Twitter posts when the generated variants are included. Our proposed spelling variant generator has several advantages over the current state-of-the-art and other types of variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable. While the base system is fully unsupervised, we show how supervision maybe employed to adjust weights for task-specific customization. The performance and significant relative simplicity of our proposed approach makes it a much needed misspelling generation resource for health-related text mining from noisy sources. The source code for the system has been made publicly available for research purposes.
How Much Are You Willing to Share? A "Poker-Styled" Selective Privacy Preserving Framework for Recommender Systems
Venue: arXiv cs.IR 2018
Year: 2018

Most industrial recommender systems rely on the popular collaborative filtering (CF) technique for providing personalized recommendations to its users. However, the very nature of CF is adversarial to the idea of user privacy, because users need to share their preferences with others in order to be grouped with like-minded people and receive accurate recommendations. While previous privacy preserving approaches have been successful inasmuch as they concealed user preference information to some extent from a centralized recommender system, they have also, nevertheless, incurred significant trade-offs in terms of privacy, scalability, and accuracy. They are also vulnerable to privacy breaches by malicious actors. In light of these observations, we propose a novel selective privacy preserving (SP2) paradigm that allows users to custom define the scope and extent of their individual privacies, by marking their personal ratings as either public (which can be shared) or private (which are never shared and stored only on the user device). Our SP2 framework works in two steps: (i) First, it builds an initial recommendation model based on the sum of all public ratings that have been shared by users and (ii) then, this public model is fine-tuned on each user's device based on the user private ratings, thus eventually learning a more accurate model. Furthermore, in this work, we introduce three different algorithms for implementing an end-to-end SP2 framework that can scale effectively from thousands to hundreds of millions of items. Our user survey shows that an overwhelming fraction of users are likely to rate much more items to improve the overall recommendations when they can control what ratings will be publicly shared with others.
How Content Volume on Landing Pages Influences Consumer Behavior
Venue: arXiv cs.HC 2018
Year: 2018

Does more information elicit users compliance and engagement, or the other way around? This paper explores the relationship between content strategy and user experience (UX). Specifically, we examine how the amount of information provided on marketing web pages, often called landing pages,impact users willingness to provide their e-mail address (a behavior called conversion in marketing terms). We describe the results of two large-scale online experiments (n= 535 and n= 27,900) conducted in real-world commercial settings. The observed results indicate a negative correlation between the amount of information on a web page and users decision-making and engagement.
Segmentation, Incentives and Privacy
Venue: arXiv cs.GT 2018
Year: 2018

Data driven segmentation is the powerhouse behind the success of online advertising. Various underlying challenges for successful segmentation have been studied by the academic community, with one notable exception - consumers incentives have been typically ignored. This lacuna is troubling as consumers have much control over the data being collected. Missing or manipulated data could lead to inferior segmentation. The current work proposes a model of prior-free segmentation, inspired by models of facility location, and to the best of our knowledge provides the first segmentation mechanism that addresses incentive compatibility, efficient market segmentation and privacy in the absence of a common prior.
Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations
Venue: arXiv cs.NE 2018
Year: 2018

Echo State Network (ESN) presents a distinguished kind of recurrent neural networks. It is built upon a sparse, random and large hidden infrastructure called reservoir. ESNs have succeeded in dealing with several non-linear problems such as prediction, classification, etc. Thanks to its rich dynamics, ESN is used as an Autoencoder (AE) to extract features from original data representations. ESN is not only used with its basic single layer form but also with the recently proposed Multi-Layer (ML) architecture. The well setting of ESN (basic and ML) architectures and training parameters is a crucial and hard labor task. Generally, a number of parameters (hidden neurons, sparsity rates, input scaling) is manually altered to achieve minimum learning error. However, this randomly hand crafted task, on one hand, may not guarantee best training results and on the other hand, it can raise the network's complexity. In this paper, a hierarchical bi-level evolutionary optimization is proposed to deal with these issues. The first level includes a multi-objective architecture optimization providing maximum learning accuracy while sustaining the complexity at a reduced standard. Multi-objective Particle Swarm Optimization (MOPSO) is used to optimize ESN structure in a way to provide a trade-off between the network complexity decreasing and the accuracy increasing. A pareto-front of optimal solutions is generated by the end of the MOPSO process. These solutions present the set of candidates that succeeded in providing a compromise between different objectives (learning error and network complexity). At the second level, each of the solutions already found undergo a mono-objective weights optimization to enhance the obtained pareto-front. Empirical results ensure the effectiveness of the evolved ESN recurrent AEs (basic and ML) for noisy and noise free data.
Delay Performance of Multi-Antenna Multicasting in Wireless Networks
Venue: arXiv cs.IT 2018
Year: 2018

Low-latency communication is currently attracting significant attention due to the emergence of mission-critical Internet of Things (IoT) applications and content-centric services. A deep understanding of the delay performance is essential for efficient wireless system design and end-to-end latency guarantees. In this paper, we investigate the network-layer performance of physical layer multi-antenna multicasting, i.e., when the same data is simultaneously conveyed to multiple users. We provide a statistical characterization of the service process in terms of its Mellin transform and derive probabilistic delay bounds using tools from stochastic network calculus. Furthermore, using extreme value theory, we characterize the service process for very large number of users and derive scaling laws as the number of antennas and/or users is taken to infinity. Our results can be used for system dimensioning to guarantee the delay requirements in wireless multicast networks.
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Indoor semantic segmentation has always been a difficult task in computer vision. In this paper, we propose an RGB-D residual encoder-decoder architecture, named RedNet, for indoor RGB-D semantic segmentation. In RedNet, the residual module is applied to both the encoder and decoder as the basic building block, and the skip-connection is used to bypass the spatial feature between the encoder and decoder. In order to incorporate the depth information of the scene, a fusion structure is constructed, which makes inference on RGB image and depth image separately, and fuses their features over several layers. In order to efficiently optimize the network's parameters, we propose a `pyramid supervision' training scheme, which applies supervised learning over different layers in the decoder, to cope with the problem of gradients vanishing. Experiment results show that the proposed RedNet(ResNet-50) achieves a state-of-the-art mIoU accuracy of 47.8% on the SUN RGB-D benchmark dataset.
Deep Multi-Structural Shape Analysis: Application to Neuroanatomy
Venue: arXiv cs.CV 2018
Year: 2018

We propose a deep neural network for supervised learning on neuroanatomical shapes. The network directly operates on raw point clouds without the need for mesh processing or the identification of point correspondences, as spatial transformer networks map the data to a canonical space. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. The proposed network consists of multiple branches, so that features for multiple structures are learned simultaneously. We demonstrate the performance of our method on two applications: (i) the prediction of Alzheimer's disease and mild cognitive impairment and (ii) the regression of the brain age. Finally, we visualize the important parts of the anatomy for the prediction by adapting the occlusion method to point clouds.
A rational decentralized generalized Nash equilibrium seeking for energy markets
Venue: arXiv cs.CE 2018
Year: 2018

We propose a method to design a decentralized energy market which guarantees individual rationality (IR) in expectation, in the presence of system-level grid constraints. We formulate the market as a welfare maximization problem subject to IR constraints, and we make use of Lagrangian duality to model the problem as a n-person non-cooperative game with a unique generalized Nash equilibrium (GNE). We provide a distributed algorithm which converges to the GNE. The convergence and properties of the algorithm are investigated by means of numerical simulations.
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Venue: arXiv cs.DC 2018
Year: 2018

Generative Adversarial Networks (GANs) are one of the most recent deep learning models that generate synthetic data from limited genuine datasets. GANs are on the frontier as further extension of deep learning into many domains (e.g., medicine, robotics, content synthesis) requires massive sets of labeled data that is generally either unavailable or prohibitively costly to collect. Although GANs are gaining prominence in various fields, there are no accelerators for these new models. In fact, GANs leverage a new operator, called transposed convolution, that exposes unique challenges for hardware acceleration. This operator first inserts zeros within the multidimensional input, then convolves a kernel over this expanded array to add information to the embedded zeros. Even though there is a convolution stage in this operator, the inserted zeros lead to underutilization of the compute resources when a conventional convolution accelerator is employed. We propose the GANAX architecture to alleviate the sources of inefficiency associated with the acceleration of GANs using conventional convolution accelerators, making the first GAN accelerator design possible. We propose a reorganization of the output computations to allocate compute rows with similar patterns of zeros to adjacent processing engines, which also avoids inconsequential multiply-adds on the zeros. This compulsory adjacency reclaims data reuse across these neighboring processing engines, which had otherwise diminished due to the inserted zeros. The reordering breaks the full SIMD execution model, which is prominent in convolution accelerators. Therefore, we propose a unified MIMD-SIMD design for GANAX that leverages repeated patterns in the computation to create distinct microprograms that execute concurrently in SIMD mode.
Hardware Transactional Persistent Memory
Venue: arXiv cs.DC 2018
Year: 2018

Emerging Persistent Memory technologies (also PM, Non-Volatile DIMMs, Storage Class Memory or SCM) hold tremendous promise for accelerating popular data-management applications like in-memory databases. However, programmers now need to deal with ensuring the atomicity of transactions on Persistent Memory resident data and maintaining consistency between the order in which processors perform stores and that in which the updated values become durable. The problem is specially challenging when high-performance isolation mechanisms like Hardware Transactional Memory (HTM) are used for concurrency control. This work shows how HTM transactions can be ordered correctly and atomically into PM by the use of a novel software protocol combined with a Persistent Memory Controller, without requiring changes to processor cache hardware or HTM protocols. In contrast, previous approaches require significant changes to existing processor microarchitectures. Our approach, evaluated using both micro-benchmarks and the STAMP suite compares well with standard (volatile) HTM transactions. It also yields significant gains in throughput and latency in comparison with persistent transactional locking.
Ring Migration Topology Helps Bypassing Local Optima
Venue: arXiv cs.NE 2018
Year: 2018

Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different islands leads to diversity, thus possibly exploring the search space better. Many theoretical analyses so far have found a complete (or sufficiently quickly expanding) topology as underlying migration graph most efficient for optimization, even though a quick dissemination of individuals leads to a loss of diversity. We suggest a simple fitness function FORK with two local optima parametrized by $r \geq 2$ and a scheme for composite fitness functions. We show that, while the (1+1) EA gets stuck in a bad local optimum and incurs a run time of $Θ(n^{2r})$ fitness evaluations on FORK, island models with a complete topology can achieve a run time of $Θ(n^{1.5r})$ by making use of rare migrations in order to explore the search space more effectively. Finally, the ring topology, making use of rare migrations and a large diameter, can achieve a run time of $\tildeΘ(n^r)$, the black box complexity of FORK. This shows that the ring topology can be preferable over the complete topology in order to maintain diversity.
Securify: Practical Security Analysis of Smart Contracts
Venue: arXiv cs.CR 2018
Year: 2018

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.
Minimizing Event-Handling Latencies in Secure Virtual Machines
Venue: arXiv cs.OS 2018
Year: 2018

Virtualization, after having found widespread adoption in the server and desktop arena, is poised to change the architecture of embedded systems as well. The benefits afforded by virtualization - enhanced isolation, manageability, flexibility, and security - could be instrumental for developers of embedded systems as an answer to the rampant increase in complexity. While mature desktop and server solutions exist, they cannot be easily reused on embedded systems because of markedly different requirements. Unfortunately, optimizations aimed at throughput, important for servers, often compromise on aspects like predictable real-time behavior, which are crucial to many embedded systems. In a similar vein, the requirements for small trusted computing bases, lightweight inter-VM communication, and small footprints are often not accommodated. This observation suggests that virtual machines for embedded systems should be constructed from scratch with particular attention paid to the specific requirements. In this paper, we set out with a virtual machine designed for security-conscious workloads and describe the steps necessary to achieve good event-handling latencies. That evolution is possible because the underlying microkernel is well suited to satisfy real-time requirements. As the guest system we chose Linux with the PREEMPT_RT configuration, which itself was developed in an effort to bring down event-handling latencies in a general purpose system. Our results indicate that the increase of event-handling latencies of a guest running in a virtual machine does not, compared to native execution, exceed a factor of two.
Secure and Efficient Skyline Queries on Encrypted Data
Venue: arXiv cs.DB 2018
Year: 2018

Outsourcing data and computation to cloud server provides a cost-effective way to support large scale data storage and query processing. However, due to security and privacy concerns, sensitive data (e.g., medical records) need to be protected from the cloud server and other unauthorized users. One approach is to outsource encrypted data to the cloud server and have the cloud server perform query processing on the encrypted data only. It remains a challenging task to support various queries over encrypted data in a secure and efficient way such that the cloud server does not gain any knowledge about the data, query, and query result. In this paper, we study the problem of secure skyline queries over encrypted data. The skyline query is particularly important for multi-criteria decision making but also presents significant challenges due to its complex computations. We propose a fully secure skyline query protocol on data encrypted using semantically-secure encryption. As a key subroutine, we present a new secure dominance protocol, which can be also used as a building block for other queries. Furthermore, we demonstrate two optimizations, data partitioning and lazy merging, to further reduce the computation load. Finally, we provide both serial and parallelized implementations and empirically study the protocols in terms of efficiency and scalability under different parameter settings, verifying the feasibility of our proposed solutions.
Stability Analysis for Fast Settling Switched DPLL
Venue: arXiv cs.SY 2018
Year: 2018

In current generation digital phase locked loop (DPLL) architectures, techniques like adaptive loop bandwidth with loop order switching and switched phase-detection are employed to achieve better lock time and jitter performance. This work derives stability conditions for such DPLL architectures using Multiple Lyapunov Functions (MLFs) for switched systems. The loop-parameters chosen on the basis of these stability conditions ensure that chattering phenomenon does not occur during switching between different subsystems. A 5GHz fractional-N DPLL designed with these loop-parameter values is fabricated in CMOS65nm-LL technology. The measured settling time of the implemented DPLL is within 1us. The efficiency of switching rule and stability conditions used for this DPLL is validated with the fast settling response, which is the best lock time reported until now for fractional-N DPLLs.
Implementing Mediators with Asynchronous Cheap Talk
Venue: arXiv cs.DC 2018
Year: 2018

A mediator can help non-cooperative agents obtain an equilibrium that may otherwise not be possible. We study the ability of players to obtain the same equilibrium without a mediator, using only cheap talk, that is, nonbinding pre-play communication. Previous work has considered this problem in a synchronous setting. Here we consider the effect of asynchrony on the problem, and provide upper bounds for implementing mediators. Considering asynchronous environments introduces new subtleties, including exactly what solution concept is most appropriate and determining what move is played if the cheap talk goes on forever. Different results are obtained depending on whether the move after such "infinite play" is under the control of the players or part of the description of the game.
Challenges in High-dimensional Reinforcement Learning with Evolution Strategies
Venue: arXiv cs.NE 2018
Year: 2018

Evolution Strategies (ESs) have recently become popular for training deep neural networks, in particular on reinforcement learning tasks, a special form of controller design. Compared to classic problems in continuous direct search, deep networks pose extremely high-dimensional optimization problems, with many thousands or even millions of variables. In addition, many control problems give rise to a stochastic fitness function. Considering the relevance of the application, we study the suitability of evolution strategies for high-dimensional, stochastic problems. Our results give insights into which algorithmic mechanisms of modern ES are of value for the class of problems at hand, and they reveal principled limitations of the approach. They are in line with our theoretical understanding of ESs. We show that combining ESs that offer reduced internal algorithm cost with uncertainty handling techniques yields promising methods for this class of problems.