OpenRaiser/Intern-Atlas huggingface dataset:
Interactive dimensionality reduction using similarity projections
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in machine learning allow us to analyze and describe the content of high-dimensional data like text, audio, images or other signals. In order to visualize that data in 2D or 3D, usually Dimensionality Reduction (DR) techniques are employed. Most of these techniques, e.g., PCA or t-SNE, produce static projections without taking into account corrections from humans or other data exploration scenarios. In this work, we propose the interactive Similarity Projection (iSP), a novel interactive DR framework based on similarity embeddings, where we form a differentiable objective based on the user interactions and perform learning using gradient descent, with an end-to-end trainable architecture. Two interaction scenarios are evaluated. First, a common methodology in multidimensional projection is to project a subset of data, arrange them in classes or clusters, and project the rest unseen dataset based on that manipulation, in a kind of semi-supervised interpolation. We report results that outperform competitive baselines in a wide range of metrics and datasets. Second, we explore the scenario of manipulating some classes, while enriching the optimization with high-dimensional neighbor information. Apart from improving classification precision and clustering on images and text documents, the new emerging structure of the projection unveils semantic manifolds. For example, on the Head Pose dataset, by just dragging the faces looking far left to the left and those looking far right to the right, all faces are re-arranged on a continuum even on the vertical axis (face up and down). This end-to-end framework can be used for fast, visual semi-supervised learning, manifold exploration, interactive domain adaptation of neural embeddings and transfer learning.
Robust Dynamic CPU Resource Provisioning in Virtualized Servers
Venue: arXiv cs.SY 2018
Year: 2018

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e. H-infinity filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time (mRT) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.
An Introductory Survey on Attention Mechanisms in NLP Problems
Venue: arXiv cs.CL 2018
Year: 2018

First derived from human intuition, later adapted to machine translation for automatic token alignment, attention mechanism, a simple method that can be used for encoding sequence data based on the importance score each element is assigned, has been widely applied to and attained significant improvement in various tasks in natural language processing, including sentiment classification, text summarization, question answering, dependency parsing, etc. In this paper, we survey through recent works and conduct an introductory summary of the attention mechanism in different NLP problems, aiming to provide our readers with basic knowledge on this widely used method, discuss its different variants for different tasks, explore its association with other techniques in machine learning, and examine methods for evaluating its performance.
Corpus Phonetics Tutorial
Venue: arXiv cs.CL 2018
Year: 2018

Corpus phonetics has become an increasingly popular method of research in linguistic analysis. With advances in speech technology and computational power, large scale processing of speech data has become a viable technique. This tutorial introduces the speech scientist and engineer to various automatic speech processing tools. These include acoustic model creation and forced alignment using the Kaldi Automatic Speech Recognition Toolkit (Povey et al., 2011), forced alignment using FAVE-align (Rosenfelder et al., 2014), the Montreal Forced Aligner (McAuliffe et al., 2017), and the Penn Phonetics Lab Forced Aligner (Yuan & Liberman, 2008), as well as stop consonant burst alignment using AutoVOT (Keshet et al., 2014). The tutorial provides a general overview of each program, step-by-step instructions for running the program, as well as several tips and tricks.
Cross-lingual Short-text Matching with Deep Learning
Venue: arXiv cs.CL 2018
Year: 2018

The problem of short text matching is formulated as follows: given a pair of sentences or questions, a matching model determines whether the input pair mean the same or not. Models that can automatically identify questions with the same meaning have a wide range of applications in question answering sites and modern chatbots. In this article, we describe the approach by team hahu to solve this problem in the context of the "CIKM AnalytiCup 2018 - Cross-lingual Short-text Matching of Question Pairs" that is sponsored by Alibaba. Our solution is an end-to-end system based on current advances in deep learning which avoids heavy feature-engineering and achieves improved performance over traditional machine-learning approaches. The log-loss scores for the first and second rounds of the contest are 0.35 and 0.39 respectively. The team was ranked 7th from 1027 teams in the overall ranking scheme by the organizers that consisted of the two contest scores as well as: innovation and system integrity, understanding data as well as practicality of the solution for business.
Consensus and Sectioning-based ADMM with Norm-1 Regularization for Imaging with a Compressive Reflector Antenna
Venue: arXiv cs.CE 2018
Year: 2018

This paper presents three distributed techniques to find a sparse solution of the underdetermined linear problem $\textbf{g}=\textbf{Hu}$ with a norm-1 regularization, based on the Alternating Direction Method of Multipliers (ADMM). These techniques divide the matrix $\textbf{H}$ in submatrices by rows, columns, or both rows and columns, leading to the so-called consensus-based ADMM, sectioning-based ADMM, and consensus and sectioning-based ADMM, respectively. These techniques are applied particularly for millimeter-wave imaging through the use of a Compressive Reflector Antenna (CRA). The CRA is a hardware designed to increase the sensing capacity of an imaging system and reduce the mutual information among measurements, allowing an effective imaging of sparse targets with the use of Compressive Sensing (CS) techniques. Consensus-based ADMM has been proved to accelerate the imaging process and sectioning-based ADMM has shown to highly reduce the amount of information to be exchange among the computational nodes. In this paper, the mathematical formulation and graphical interpretation of these two techniques, together with the consensus and sectioning-based ADMM approach, are presented. The imaging quality, the imaging time, the convergence, and the communication efficiency among the nodes are analyzed and compared. The distributed capabitities of the ADMM-based approaches, together with the high sensing capacity of the CRA, allow the imaging of metallic targets in a 3D domain in quasi-real time with a reduced amount of information exchanged among the nodes.
TrolleyMod v1.0: An Open-Source Simulation and Data-Collection Platform for Ethical Decision Making in Autonomous Vehicles
Venue: arXiv cs.AI 2018
Year: 2018

This paper presents TrolleyMod v1.0, an open-source platform based on the CARLA simulator for the collection of ethical decision-making data for autonomous vehicles. This platform is designed to facilitate experiments aiming to observe and record human decisions and actions in high-fidelity simulations of ethical dilemmas that occur in the context of driving. Targeting experiments in the class of trolley problems, TrolleyMod provides a seamless approach to creating new experimental settings and environments with the realistic physics-engine and the high-quality graphical capabilities of CARLA and the Unreal Engine. Also, TrolleyMod provides a straightforward interface between the CARLA environment and Python to enable the implementation of custom controllers, such as deep reinforcement learning agents. The results of such experiments can be used for sociological analyses, as well as the training and tuning of value-aligned autonomous vehicles based on social values that are inferred from observations.
An Introduction to Fuzzy & Annotated Semantic Web Languages
Venue: arXiv cs.AI 2018
Year: 2018

We present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.
How to get meaningful and correct results from your finite element model
Venue: arXiv cs.NA 2018
Year: 2018

This document gives guidelines to set up, run, and postprocess correct simulations with the finite element method. It is not an introduction to the method itself, but rather a list of things to check and possible mistakes to watch out for when doing a finite element simulation.
Creatures great and SMAL: Recovering the shape and motion of animals from video
Venue: arXiv cs.CV 2018
Year: 2018

We present a system to recover the 3D shape and motion of a wide variety of quadrupeds from video. The system comprises a machine learning front-end which predicts candidate 2D joint positions, a discrete optimization which finds kinematically plausible joint correspondences, and an energy minimization stage which fits a detailed 3D model to the image. In order to overcome the limited availability of motion capture training data from animals, and the difficulty of generating realistic synthetic training images, the system is designed to work on silhouette data. The joint candidate predictor is trained on synthetically generated silhouette images, and at test time, deep learning methods or standard video segmentation tools are used to extract silhouettes from real data. The system is tested on animal videos from several species, and shows accurate reconstructions of 3D shape and pose.
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number of studies have shown that GAN-based image synthesis for data augmentation can aid in improving classification accuracy in a number of medical image analysis tasks, such as brain and liver image analysis. However, the efficacy of leveraging GANs for tackling prostate cancer analysis has not been previously explored. Motivated by this, in this study we introduce ProstateGAN, a GAN-based model for synthesizing realistic prostate diffusion imaging data. More specifically, in order to generate new diffusion imaging data corresponding to a particular cancer grade (Gleason score), we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process. Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores.
Predicting the time-evolution of multi-physics systems with sequence-to-sequence models
Venue: arXiv cs.LG 2018
Year: 2018

In this work, sequence-to-sequence (seq2seq) models, originally developed for language translation, are used to predict the temporal evolution of complex, multi-physics computer simulations. The predictive performance of seq2seq models is compared to state transition models for datasets generated with multi-physics codes with varying levels of complexity - from simple 1D diffusion calculations to simulations of inertial confinement fusion implosions. Seq2seq models demonstrate the ability to accurately emulate complex systems, enabling the rapid estimation of the evolution of quantities of interest in computationally expensive simulations.
Design of Spectrally Shaped Binary Sequences via Randomized Convex Relaxation
Venue: arXiv cs.IT 2018
Year: 2018

Wideband communication receivers often deal with the problems of detecting weak signals from distant sources received together with strong nearby interferers. When the techniques of random modulation are used in communication system receivers, one can design a spectrally shaped sequence that mitigates interferer bands while preserving message bands. Common implementation constraints require sequence quantization, which turns the design problem formulation to an integer optimization problem solved using a semidefinite program on a matrix that is restricted to have rank one. Common approximation schemes for this problem are not amenable due to the distortion to the spectrum caused by the required quantization. We propose a method that leverages a randomized projection and quantization of the solution of a semidefinite program, an approach that has been previously used for related integer programs. We provide a theoretical and numerical analysis on the feasibility and quality of the approximation provided by the proposed approach. Furthermore, numerical simulations show that our proposed approach returns the same sequence as an exhaustive search (when feasible), showcasing its accuracy and efficiency. Furthermore, our proposed method succeeds in finding suitable spectrally shaped sequences for cases where exhaustive search is not feasible, achieving better performance than existing alternatives.
Blockchain-based Firmware Update Scheme Tailored for Autonomous Vehicles
Venue: arXiv cs.CY 2018
Year: 2018

Recently, Autonomous Vehicles (AVs) have gained extensive attention from both academia and industry. AVs are a complex system composed of many subsystems, making them a typical target for attackers. Therefore, the firmware of the different subsystems needs to be updated to the latest version by the manufacturer to fix bugs and introduce new features, e.g., using security patches. In this paper, we propose a distributed firmware update scheme for the AVs' subsystems, leveraging blockchain and smart contract technology. A consortium blockchain made of different AVs manufacturers is used to ensure the authenticity and integrity of firmware updates. Instead of depending on centralized third parties to distribute the new updates, we enable AVs, namely distributors, to participate in the distribution process and we take advantage of their mobility to guarantee high availability and fast delivery of the updates. To incentivize AVs to distribute the updates, a reward system is established that maintains a credit reputation for each distributor account in the blockchain. A zero-knowledge proof protocol is used to exchange the update in return for a proof of distribution in a trust-less environment. Moreover, we use attribute-based encryption (ABE) scheme to ensure that only authorized AVs will be able to download and use a new update. Our analysis indicates that the additional cryptography primitives and exchanged transactions do not affect the operation of the AVs network. Also, our security analysis demonstrates that our scheme is efficient and secure against different attacks.
A Scalable Framework For Real-Time Multi-Robot, Multi-Human Collision Avoidance
Venue: arXiv cs.RO 2018
Year: 2018

Robust motion planning is a well-studied problem in the robotics literature, yet current algorithms struggle to operate scalably and safely in the presence of other moving agents, such as humans. This paper introduces a novel framework for robot navigation that accounts for high-order system dynamics and maintains safety in the presence of external disturbances, other robots, and non-deterministic intentional agents. Our approach precomputes a tracking error margin for each robot, generates confidence-aware human motion predictions, and coordinates multiple robots with a sequential priority ordering, effectively enabling scalable safe trajectory planning and execution. We demonstrate our approach in hardware with two robots and two humans. We also showcase our work's scalability in a larger simulation.
EdgeBench: Benchmarking Edge Computing Platforms
Venue: arXiv cs.NI 2018
Year: 2018

The emerging trend of edge computing has led several cloud providers to release their own platforms for performing computation at the 'edge' of the network. We compare two such platforms, Amazon AWS Greengrass and Microsoft Azure IoT Edge, using a new benchmark comprising a suite of performance metrics. We also compare the performance of the edge frameworks to cloud-only implementations available in their respective cloud ecosystems. Amazon AWS Greengrass and Azure IoT Edge use different underlying technologies, edge Lambda functions vs. containers, and so we also elaborate on platform features available to developers. Our study shows that both of these edge platforms provide comparable performance, which nevertheless differs in important ways for key types of workloads used in edge applications. Finally, we discuss several current issues and challenges we faced in deploying these platforms.
Age of Information Scaling in Large Networks
Venue: arXiv cs.IT 2018
Year: 2018

We study age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks. There are $n$ nodes that are randomly paired with each other on a fixed area to form $n$ source-destination (S-D) pairs. We propose a three-phase transmission scheme which utilizes local cooperation between the nodes by forming what we call mega update packets to serve multiple S-D pairs at once. We show that under the proposed scheme average age of an S-D pair scales as $O(n^{\frac{1}{4}})$ as the number of users, $n$, in the network grows. To the best of our knowledge, this is the best age scaling result for a multiple source-multiple destination setting.
Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness
Venue: arXiv cs.CV 2018
Year: 2018

Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this paper, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We present an extensive naturalistic drive dataset of drivers in a conditionally autonomous vehicle running on Californian freeways. We collect subjective ratings for the driver's take-over readiness from multiple human observers viewing the sensor feed. Analysis of the ratings in terms of intra-class correlation coefficients (ICCs) shows a high degree of consistency in the ratings across raters. We define a metric for the driver's take-over readiness termed the 'Observable Readiness Index (ORI)' based on the ratings. Finally, we propose an LSTM model for continuous estimation of the driver's ORI based on a holistic representation of the driver's state, capturing gaze, hand, pose and foot activity. Our model estimates the ORI with a mean absolute error of 0.449 on a 5 point scale.
Phishing in an Academic Community: A Study of User Susceptibility and Behavior
Venue: arXiv cs.CR 2018
Year: 2018

We present an observational study on the relationship between demographic factors and phishing susceptibility at the University of Maryland, Baltimore County (UMBC). In spring 2018, we delivered phishing attacks to 450 randomly-selected students on three different days (1,350 students total) to examine user click rates and demographics among UMBC's undergraduates. Participants were initially unaware of the study. Experiment 1 claimed to bill students; Experiment 2 enticed users with monetary rewards; and Experiment 3 threatened users with account cancellation. We found correlations resulting in lowered susceptibility based on college affiliation, academic year progression, cyber training, involvement in cyber clubs or cyber scholarship programs, time spent on the computer, and age demographics. We found no significant correlation between gender and susceptibility. Contrary to our expectations, we observed greater user susceptibility with greater phishing knowledge and awareness. Students who identified themselves as understanding the definition of phishing had a higher susceptibility than did their peers who were merely aware of phishing attacks, with both groups having a higher susceptibility than those with no knowledge of phishing. Approximately 59% of subjects who opened the phishing email clicked on its phishing link, and approximately 70% of those subjects who additionally answered a demographic survey clicked.
Constraint-based Sequential Pattern Mining with Decision Diagrams
Venue: arXiv cs.LG 2018
Year: 2018

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.
Automatic Grammar Augmentation for Robust Voice Command Recognition
Venue: arXiv cs.CL 2018
Year: 2018

This paper proposes a novel pipeline for automatic grammar augmentation that provides a significant improvement in the voice command recognition accuracy for systems with small footprint acoustic model (AM). The improvement is achieved by augmenting the user-defined voice command set, also called grammar set, with alternate grammar expressions. For a given grammar set, a set of potential grammar expressions (candidate set) for augmentation is constructed from an AM-specific statistical pronunciation dictionary that captures the consistent patterns and errors in the decoding of AM induced by variations in pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions. Using this candidate set, greedy optimization based and cross-entropy-method (CEM) based algorithms are considered to search for an augmented grammar set with improved recognition accuracy utilizing a command-specific dataset. Our experiments show that the proposed pipeline along with algorithms considered in this paper significantly reduce the mis-detection and mis-classification rate without increasing the false-alarm rate. Experiments also demonstrate the consistent superior performance of CEM method over greedy-based algorithms.