OpenRaiser/Intern-Atlas huggingface dataset:
Making targeted black-box evasion attacks effective and efficient
Venue: arXiv cs.LG 2019
Year: 2019

We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem setting and systematically evaluate what benefits the adversary can gain by using substitute models. We show that there is an exploration-exploitation tradeoff in that query efficiency comes at the cost of effectiveness. We present two new attack strategies for using substitute models and show that they are as effective as previous query-only techniques but require significantly fewer queries, by up to three orders of magnitude. We also show that an agile adversary capable of switching through different attack techniques can achieve pareto-optimal efficiency. We demonstrate our attack against Google Cloud Vision showing that the difficulty of black-box attacks against real-world prediction APIs is significantly easier than previously thought (requiring approximately 500 queries instead of approximately 20,000 as in previous works).
Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis
Venue: arXiv cs.CL 2019
Year: 2019

Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods. In this paper, we propose embedding capacity (the amount of information the embedding contains about the data) as a unified method of analyzing the behavior of latent variable models of speech, comparing existing heuristic (non-variational) methods to variational methods that are able to explicitly constrain capacity using an upper bound on representational mutual information. In our proposed model (Capacitron), we show that by adding conditional dependencies to the variational posterior such that it matches the form of the true posterior, the same model can be used for high-precision prosody transfer, text-agnostic style transfer, and generation of natural-sounding prior samples. For multi-speaker models, Capacitron is able to preserve target speaker identity during inter-speaker prosody transfer and when drawing samples from the latent prior. Lastly, we introduce a method for decomposing embedding capacity hierarchically across two sets of latents, allowing a portion of the latent variability to be specified and the remaining variability sampled from a learned prior. Audio examples are available on the web.
A Component-Based Approach to Traffic Data Wrangling
Venue: arXiv cs.DB 2019
Year: 2019

We produce an increasing amount of data. This is positive as it allows us to make better informed decisions if we can base them on a lot of data. However, in many domains the `raw' data that is produced, is not usable for analysis due to unreadable format, errors, noise, inconsistencies or other factors. An example of such domain is traffic -- traffic data can be used for impactful decision-making from short-term problems to large-scale infrastructure projects. We call the process of preparing data for consumption Data Wrangling. Several data wrangling tools exist that are easy to use and provide general functionality. However, no one tool is capable of performing complex domain-specific data wrangling operations. The author of this project has chosen two popular programming languages for data science -- R and Python -- for implementing traffic data wrangling operators as web services. These web services expose HTTP (Hypertext Transfer Protocol) REST (Representational State Transfer) APIs (Application Programming Interfaces), which can be used for integrating the services into another system. As traffic data analysts often lack the necessary programming skills required for working with complex services, an abstraction layer was designed by the author. In the abstraction layer, the author wrapped the data wrangling services inside Taverna components -- this made the services usable via an easy-to-use GUI (Graphical User Interface) provided by Taverna Workbench, which is a program suitable for carrying out data wrangling tasks. This also enables reuse of the components in other workflows. The data wrangling components were tested and validated by using them for two common traffic data wrangling requests. Datasets from Transport for Greater Manchester (TfGM) and the Met Office were used to carry out the experiments.
Algebra of Concurrent Games
Venue: arXiv cs.LO 2019
Year: 2019

We introduce parallelism into the basic algebra of games to model concurrent game algebraically. Parallelism is treated as a new kind of game operation. The resulted algebra of concurrent games can be used widely to reason the parallel systems.
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
Venue: arXiv cs.LG 2019
Year: 2019

In-memory computing is an emerging computing paradigm that could enable deeplearning inference at significantly higher energy efficiency and reduced latency. The essential idea is to map the synaptic weights corresponding to each layer to one or more computational memory (CM) cores. During inference, these cores perform the associated matrix-vector multiply operations in place with O(1) time complexity, thus obviating the need to move the synaptic weights to an additional processing unit. Moreover, this architecture could enable the execution of these networks in a highly pipelined fashion. However, a key challenge is to design an efficient communication fabric for the CM cores. Here, we present one such communication fabric based on a graph topology that is well suited for the widely successful convolutional neural networks (CNNs). We show that this communication fabric facilitates the pipelined execution of all state of-the-art CNNs by proving the existence of a homomorphism between one graph representation of these networks and the proposed graph topology. We then present a quantitative comparison with established communication topologies and show that our proposed topology achieves the lowest bandwidth requirements per communication channel. Finally, we present a concrete example of mapping ResNet-32 onto an array of CM cores.
Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy
Venue: arXiv cs.LG 2019
Year: 2019

According to a recent investigation, an estimated 33-50% of the world's coral reefs have undergone degradation, believed to be as a result of climate change. A strong driver of climate change and the subsequent environmental impact are greenhouse gases such as methane. However, the exact relation climate change has to the environmental condition cannot be easily established. Remote sensing methods are increasingly being used to quantify and draw connections between rapidly changing climatic conditions and environmental impact. A crucial part of this analysis is processing spectroscopy data using radiative transfer models (RTMs) which is a computationally expensive process and limits their use with high volume imaging spectrometers. This work presents an algorithm that can efficiently emulate RTMs using neural networks leading to a multifold speedup in processing time, and yielding multiple downstream benefits.
Forward and Backward Knowledge Transfer for Sentiment Classification
Venue: arXiv cs.AI 2019
Year: 2019

This paper studies the problem of learning a sequence of sentiment classification tasks. The learned knowledge from each task is retained and used to help future or subsequent task learning. This learning paradigm is called Lifelong Learning (LL). However, existing LL methods either only transfer knowledge forward to help future learning and do not go back to improve the model of a previous task or require the training data of the previous task to retrain its model to exploit backward/reverse knowledge transfer. This paper studies reverse knowledge transfer of LL in the context of naive Bayesian (NB) classification. It aims to improve the model of a previous task by leveraging future knowledge without retraining using its training data. This is done by exploiting a key characteristic of the generative model of NB. That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks. Experimental results show that the proposed method markedly outperforms existing LL baselines.
PizzaBox: Studying Internet Connected Physical Object Manipulation based Food Ordering
Venue: arXiv cs.HC 2019
Year: 2019

This paper presents the designing and testing of PizzaBox, a 3D printed, interactive food ordering system that aims to differ from conventional food ordering systems and provide an entertaining and unique experience when ordering a pizza by incorporating underlying technologies that support ubiquitous computing. The PizzaBox has gone through both low and medium fidelity testing while working collaboratively with participants to co-design and refine a product that is approachable to all age groups while maintaining a simple process for ordering food from start to finish. Final testing was conducted at an independent pizzeria where interviews with participants lead us to develop four discussion themes 1) usability and end user engagement, 2) towards connected real-time products and services, 3) healthy eating, 4) evolution of food ordering systems. Our interviews show that in general, PizzaBox would have a greater appeal to a younger audience by providing a fantasy of helping in the creation and baking of the pizza but also has a novelty value that all ages would enjoy. We investigate the effect that the PizzaBox has in encouraging new healthy habits or promoting a healthier lifestyle as well as how we can improve PizzaBox to better encourage these lifestyle changes.
Structure from Motion for Panorama-Style Videos
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel Structure from Motion pipeline that is capable of reconstructing accurate camera poses for panorama-style video capture without prior camera intrinsic calibration. While panorama-style capture is common and convenient, previous reconstruction methods fail to obtain accurate reconstructions due to the rotation-dominant motion and small baseline between views. Our method is built on the assumption that the camera motion approximately corresponds to motion on a sphere, and we introduce three novel relative pose methods to estimate the fundamental matrix and camera distortion for spherical motion. These solvers are efficient and robust, and provide an excellent initialization for bundle adjustment. A soft prior on the camera poses is used to discourage large deviations from the spherical motion assumption when performing bundle adjustment, which allows cameras to remain properly constrained for optimization in the absence of well-triangulated 3D points. To validate the effectiveness of the proposed method we evaluate our approach on both synthetic and real-world data, and demonstrate that camera poses are accurate enough for multiview stereo.
A Survey on Neural Network Language Models
Venue: arXiv cs.CL 2019
Year: 2019

As the core component of Natural Language Processing (NLP) system, Language Model (LM) can provide word representation and probability indication of word sequences. Neural Network Language Models (NNLMs) overcome the curse of dimensionality and improve the performance of traditional LMs. A survey on NNLMs is performed in this paper. The structure of classic NNLMs is described firstly, and then some major improvements are introduced and analyzed. We summarize and compare corpora and toolkits of NNLMs. Further, some research directions of NNLMs are discussed.
Analysis of a Poisson-picking symmetric winners-take-all game with randomized payoffs
Venue: arXiv cs.GT 2019
Year: 2019

Winners-take-all situations introduce an incentive for agents to diversify their behavior, since doing so will result in splitting an eventual price with fewer people. At the same time, when the payoff of a process depends on a parameter choice that is symmetric with respect to agents, all agents have the incentive to choose the values of the parameter that lead to higher payoffs. We explore the trade-off between these two considerations, with a focus on a particular example. This example can be seen as a simple model for the situation where a group of friends bet against each other about the top-scoring team in a sports league. We obtain analytic characterizations of the symmetric equilibria in the case of only 2 agents and in the case where there are only two possible top-scorers. We also conduct some simulations beyond these cases, and observe how does the pressure to diversify behavior evolve as the parameters of the model change.
Deep Music Analogy Via Latent Representation Disentanglement
Venue: arXiv cs.SD 2019
Year: 2019

Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level representations and their relationships, from one piece to another; however, this procedure requires disentangling music representations, which usually takes little effort for musicians but is non-trivial for computers. Three sub-problems arise: extracting latent representations from the observation, disentangling the representations so that each part has a unique semantic interpretation, and mapping the latent representations back to actual music. In this paper, we contribute an explicitly-constrained variational autoencoder (EC$^2$-VAE) as a unified solution to all three sub-problems. We focus on disentangling the pitch and rhythm representations of 8-beat music clips conditioned on chords. In producing music analogies, this model helps us to realize the imaginary situation of "what if" a piece is composed using a different pitch contour, rhythm pattern, or chord progression by borrowing the representations from other pieces. Finally, we validate the proposed disentanglement method using objective measurements and evaluate the analogy examples by a subjective study.
Accuracy Requirements for Early Estimation of Crop Production in Senegal
Venue: arXiv cs.CY 2019
Year: 2019

Early warning systems for food security rely on timely and accurate estimations of crop production. Several approaches have been developed to get early estimations of area and yield, the two components of crop production. The most common methods, based on Earth observation data, are image classification for crop area and correlation with vegetation index for crop yield. Regardless of the approach used, early estimators of cropland area, crop area or crop yield should have an accuracy providing lower production error than existing historical crop statistics. The objective of this study is to develop a methodological framework to define the accuracy requirements for early estimators of cropland area, crop area and crop yield in Senegal. These requirements are made according to (i) the inter-annual variability and the trend of historical data, (ii) the calendar of official statistics data collection, and (iii) the time at which early estimations of cropland area, crop area and crop yield can theoretically be available. This framework is applied to the seven main crops in Senegal using 20 years of crop production data. Results show that the inter-annual variability of crop yield is the main factor limiting the accuracy of pre-harvest production forecast. Estimators of cropland area can be used to improve production prediction of groundnuts, millet and rice, the three main crops in Senegal stressing the value of cropland mapping for food security. While applied to Senegal, this study could easily be reproduced in any country where reliable agricultural statistics are available.
Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
Venue: arXiv cs.RO 2019
Year: 2019

Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions for humanoid robots. However, including uncertainties and stochasticity in the TO problem to generate robust motions can easily lead to an interactable problem. Furthermore, since the models used in the TO have always some level of abstraction, it is hard to find a realistic set of uncertainty in the space of abstract model. In this paper we aim at leveraging a sample-efficient learning technique (Bayesian optimization) to robustify trajectory optimization for humanoid locomotion. The main idea is to use Bayesian optimization to find the optimal set of cost weights which compromises performance with respect to robustness with a few realistic simulation/experiment. The results show that the proposed approach is able to generate robust motions for different set of disturbances and uncertainties.
Gendered Pronoun Resolution using BERT and an extractive question answering formulation
Venue: arXiv cs.CL 2019
Year: 2019

The resolution of ambiguous pronouns is a longstanding challenge in Natural Language Understanding. Recent studies have suggested gender bias among state-of-the-art coreference resolution systems. As an example, Google AI Language team recently released a gender-balanced dataset and showed that performance of these coreference resolvers is significantly limited on the dataset. In this paper, we propose an extractive question answering (QA) formulation of pronoun resolution task that overcomes this limitation and shows much lower gender bias (0.99) on their dataset. This system uses fine-tuned representations from the pre-trained BERT model and outperforms the existing baseline by a significant margin (22.2% absolute improvement in F1 score) without using any hand-engineered features. This QA framework is equally performant even without the knowledge of the candidate antecedents of the pronoun. An ensemble of QA and BERT-based multiple choice and sequence classification models further improves the F1 (23.3% absolute improvement upon the baseline). This ensemble model was submitted to the shared task for the 1st ACL workshop on Gender Bias for Natural Language Processing. It ranked 9th on the final official leaderboard. Source code is available at https://github.com/rakeshchada/corefqa
Neural Heterogeneous Scheduler
Venue: arXiv cs.LG 2019
Year: 2019

Access to parallel and distributed computation has enabled researchers and developers to improve algorithms and performance in many applications. Recent research has focused on next generation special purpose systems with multiple kinds of coprocessors, known as heterogeneous system-on-chips (SoC). In this paper, we introduce a method to intelligently schedule--and learn to schedule--a stream of tasks to available processing elements in such a system. We use deep reinforcement learning enabling complex sequential decision making and empirically show that our reinforcement learning system provides for a viable, better alternative to conventional scheduling heuristics with respect to minimizing execution time.
A Regression Approach to Certain Information Transmission Problems
Venue: arXiv cs.IT 2019
Year: 2019

A general information transmission model, under independent and identically distributed Gaussian codebook and nearest neighbor decoding rule with processed channel output, is investigated using the performance metric of generalized mutual information. When the encoder and the decoder know the statistical channel model, it is found that the optimal channel output processing function is the conditional expectation operator, thus hinting a potential role of regression, a classical topic in machine learning, for this model. Without utilizing the statistical channel model, a problem formulation inspired by machine learning principles is established, with suitable performance metrics introduced. A data-driven inference algorithm is proposed to solve the problem, and the effectiveness of the algorithm is validated via numerical experiments. Extensions to more general information transmission models are also discussed.
Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators
Venue: arXiv cs.RO 2019
Year: 2019

This paper describes the control, and evaluation of a new human-scaled biped robot with liquid cooled viscoelastic actuators (VLCA). Based on the lessons learned from previous work from our team on VLCA [1], we present a new system design embodying a Reaction Force Sensing Series Elastic Actuator (RFSEA) and a Force Sensing Series Elastic Actuator (FSEA). These designs are aimed at reducing the size and weight of the robot's actuation system while inheriting the advantages of our designs such as energy efficiency, torque density, impact resistance and position/force controllability. The system design takes into consideration human-inspired kinematics and range-of-motion (ROM), while relying on foot placement to balance. In terms of actuator control, we perform a stability analysis on a Disturbance Observer (DOB) designed for force control. We then evaluate various position control algorithms both in the time and frequency domains for our VLCA actuators. Having the low level baseline established, we first perform a controller evaluation on the legs using Operational Space Control (OSC) [2]. Finally, we move on to evaluating the full bipedal robot by accomplishing unsupported dynamic walking by means of the algorithms to appear in [3].
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Venue: arXiv cs.LG 2019
Year: 2019

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML pipelines is sub-optimal, since it cannot jointly optimize multiple components. To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation. Our experiments show that fine-tuning of the translated pipelines is a promising technique able to increase the final accuracy.
SAR: Learning Cross-Language API Mappings with Little Knowledge
Venue: arXiv cs.LG 2019
Year: 2019

To save manual effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, all these approaches still require large amount of manual effort in preparing parallel program corpora, ranging from pairs of APIs, to manually identified code in different languages that are considered as functionally equivalent. To minimize the manual effort in identifying parallel program corpora and API mappings, this paper aims at an automated approach to map APIs across languages with much less knowledge a priori needed than other existing approaches. The approach is based on an realization of the notion of domain adaption combined with code embedding, which can better align two vector spaces: taking as input large sets of programs, our approach first generates numeric vector representations of the programs, especially the APIs used in each language, and it adapts generative adversarial networks (GAN) to align the vectors from the spaces of two languages. For a better alignment, we initialize the GAN with parameters derived from optional API mapping seeds that can be identified accurately with a simple automatic signature-based matching heuristic. Then the cross-language API mappings can be identified via nearest-neighbors queries in the aligned vector spaces.