OpenRaiser/Intern-Atlas huggingface dataset:
Learning in situ: a randomized experiment in video streaming
Venue: arXiv cs.NI 2019
Year: 2019

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.
Proximal Reliability Optimization for Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Despite the numerous advances, reinforcement learning remains away from widespread acceptance for autonomous controller design as compared to classical methods due to lack of ability to effectively tackle the reality gap. The reliance on absolute or deterministic reward as a metric for optimization process renders reinforcement learning highly susceptible to changes in problem dynamics. We introduce a novel framework that effectively quantizes the uncertainty of the design space and induces robustness in controllers by switching to a reliability-based optimization routine. The data efficiency of the method is maintained to match reward based optimization methods by employing a model-based approach. We prove the stability of learned neuro-controllers in both static and dynamic environments on classical reinforcement learning tasks such as Cart Pole balancing and Inverted Pendulum.
Improving Long Distance Slot Carryover in Spoken Dialogue Systems
Venue: arXiv cs.CL 2019
Year: 2019

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show that this approach results in poor performance over longer context dialogues. In this paper, we propose to jointly model the slots. We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies. Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance.
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Venue: arXiv cs.CL 2019
Year: 2019

It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.
Learning Transferable Cooperative Behavior in Multi-Agent Teams
Venue: arXiv cs.LG 2019
Year: 2019

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized framework and further show that the learned policies quickly transfer to scenarios with different team sizes along with strong zero-shot generalization performance. This is an important step towards developing multi-agent teams which can be realistically deployed in the real world without assuming complete prior knowledge or instantaneous communication at unbounded distances.
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem
Venue: arXiv cs.LG 2019
Year: 2019

This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs. We use deep Graph Convolutional Networks to build efficient TSP graph representations and output tours in a non-autoregressive manner via highly parallelized beam search. Our approach outperforms all recently proposed autoregressive deep learning techniques in terms of solution quality, inference speed and sample efficiency for problem instances of fixed graph sizes. In particular, we reduce the average optimality gap from 0.52% to 0.01% for 50 nodes, and from 2.26% to 1.39% for 100 nodes. Finally, despite improving upon other learning-based approaches for TSP, our approach falls short of standard Operations Research solvers.
Learning to Explain: Answering Why-Questions via Rephrasing
Venue: arXiv cs.CL 2019
Year: 2019

Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction. Explanations are challenging in that they require many different forms of abstract knowledge and reasoning. Previous work has either relied on human-curated structured knowledge bases or detailed domain representation to generate satisfactory explanations. They are also often limited to ranking pre-existing explanation choices. In our work, we contribute to the under-explored area of generating natural language explanations for general phenomena. We automatically collect large datasets of explanation-phenomenon pairs which allow us to train sequence-to-sequence models to generate natural language explanations. We compare different training strategies and evaluate their performance using both automatic scores and human ratings. We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.
A Novel Hyperparameter-free Approach to Decision Tree Construction that Avoids Overfitting by Design
Venue: arXiv cs.LG 2019
Year: 2019

Decision trees are an extremely popular machine learning technique. Unfortunately, overfitting in decision trees still remains an open issue that sometimes prevents achieving good performance. In this work, we present a novel approach for the construction of decision trees that avoids the overfitting by design, without losing accuracy. A distinctive feature of our algorithm is that it requires neither the optimization of any hyperparameters, nor the use of regularization techniques, thus significantly reducing the decision tree training time. Moreover, our algorithm produces much smaller and shallower trees than traditional algorithms, facilitating the interpretability of the resulting models.
Graduated Optimization of Black-Box Functions
Venue: arXiv cs.LG 2019
Year: 2019

Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our approach for tuning high dimensional hyperparameters in machine learning.
Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems
Venue: arXiv cs.RO 2019
Year: 2019

SLAM based techniques are often adopted for solving the navigation problem for the drones in GPS denied environment. Despite the widespread success of these approaches, they have not yet been fully exploited for automation in a warehouse system due to expensive sensors and setup requirements. This paper focuses on the use of low-cost monocular camera-equipped drones for performing warehouse management tasks like inventory scanning and position update. The methods introduced are at par with the existing state of warehouse environment present today, that is, the existence of a grid network for the ground vehicles, hence eliminating any additional infrastructure requirement for drone deployment. As we lack scale information, that in itself forbids us to use any 3D techniques, we focus more towards optimizing standard image processing algorithms like the thick line detection and further developing it into a fast and robust grid localization framework. In this paper, we show different line detection algorithms, their significance in grid localization and their limitations. We further extend our proposed implementation towards a real-time navigation stack for an actual warehouse inspection case scenario. Our line detection method using skeletonization and centroid strategy works considerably even with varying light conditions, line thicknesses, colors, orientations, and partial occlusions. A simple yet effective Kalman Filter has been used for smoothening the ρ and θ outputs of the two different line detection methods for better drone control while grid following. A generic strategy that handles the navigation of the drone on a grid for completion of the allotted task is also developed. Based on the simulation and real-life experiments, the final developments on the drone localization and navigation in a structured environment are discussed.
SPECCFI: Mitigating Spectre Attacks using CFI Informed Speculation
Venue: arXiv cs.CR 2019
Year: 2019

Spectre attacks and their many subsequent variants are a new vulnerability class affecting modern CPUs. The attacks rely on the ability to misguide speculative execution, generally by exploiting the branch prediction structures, to execute a vulnerable code sequence speculatively. In this paper, we propose to use Control-Flow Integrity (CFI), a security technique used to stop control-flow hijacking attacks, on the committed path, to prevent speculative control-flow from being hijacked to launch the most dangerous variants of the Spectre attacks (Spectre-BTB and Spectre-RSB). Specifically, CFI attempts to constrain the possible targets of an indirect branch to a set of legal targets defined by a pre-calculated control-flow graph (CFG). As CFI is being adopted by commodity software (e.g., Windows and Android) and commodity hardware (e.g., Intel's CET and ARM's BTI), the CFI information becomes readily available through the hardware CFI extensions. With the CFI information, we apply CFI principles to also constrain illegal control-flow during speculative execution. Specifically, our proposed defense, SPECCFI, ensures that control flow instructions target legal destinations to constrain dangerous speculation on forward control-flow paths (indirect calls and branches). We augment this protection with a precise speculation-aware hardware stack to constrain speculation on backward control-flow edges (returns). We combine this solution with existing solutions against branch target predictor attacks (Spectre-PHT) to close all known non-vendor-specific Spectre vulnerabilities. We show that SPECCFI results in small overheads both in terms of performance and additional hardware complexity.
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
Venue: arXiv cs.CV 2019
Year: 2019

The 2D virtual try-on task has recently attracted a lot of interest from the research community, for its direct potential applications in online shopping as well as for its inherent and non-addressed scientific challenges. This task requires to fit an in-shop cloth image on the image of a person. It is highly challenging because it requires to warp the cloth on the target person while preserving its patterns and characteristics, and to compose the item with the person in a realistic manner. Current state-of-the-art models generate images with visible artifacts, due either to a pixel-level composition step or to the geometric transformation. In this paper, we propose WUTON: a Warping U-net for a Virtual Try-On system. It is a siamese U-net generator whose skip connections are geometrically transformed by a convolutional geometric matcher. The whole architecture is trained end-to-end with a multi-task loss including an adversarial one. This enables our network to generate and use realistic spatial transformations of the cloth to synthesize images of high visual quality. The proposed architecture can be trained end-to-end and allows us to advance towards a detail-preserving and photo-realistic 2D virtual try-on system. Our method outperforms the current state-of-the-art with visual results as well as with the Learned Perceptual Image Similarity (LPIPS) metric.
Bifurcation analysis of a TaO memristor model
Venue: arXiv cs.ET 2019
Year: 2019

This paper presents a study of bifurcation in the time-averaged dynamics of TaO memristors driven by narrow pulses of alternating polarities. The analysis, based on a physics-inspired model, focuses on the stable fixed points and on how these are affected by the pulse parameters. Our main finding is the identification of a driving regime when two stable fixed points exist simultaneously. To the best of our knowledge, such bistability is identified in a single memristor for the first time. This result can be readily tested experimentally, and is expected to be useful in future memristor circuit designs.
Semi- and Weakly-supervised Human Pose Estimation
Venue: arXiv cs.CV 2019
Year: 2019

For human pose estimation in still images, this paper proposes three semi- and weakly-supervised learning schemes. While recent advances of convolutional neural networks improve human pose estimation using supervised training data, our focus is to explore the semi- and weakly-supervised schemes. Our proposed schemes initially learn conventional model(s) for pose estimation from a small amount of standard training images with human pose annotations. For the first semi-supervised learning scheme, this conventional pose model detects candidate poses in training images with no human annotation. From these candidate poses, only true-positives are selected by a classifier using a pose feature representing the configuration of all body parts. The accuracies of these candidate pose estimation and true-positive pose selection are improved by action labels provided to these images in our second and third learning schemes, which are semi- and weakly-supervised learning. While the first and second learning schemes select only poses that are similar to those in the supervised training data, the third scheme selects more true-positive poses that are significantly different from any supervised poses. This pose selection is achieved by pose clustering using outlier pose detection with Dirichlet process mixtures and the Bayes factor. The proposed schemes are validated with large-scale human pose datasets.
Towards A Broader Acceptance Of Formal Verification Tools: The Role Of Education
Venue: arXiv cs.SE 2019
Year: 2019

Formal methods yet advantageous, face challenges towards wide acceptance and adoption in software development practices. The major reason being presumed complexity. The issue can be addressed by academia with a thoughtful plan of teaching and practise. The user study detailed in this paper is examining AutoProof tool with the motivation to identify complexities attributed to formal methods. Participants' (students of Masters program in Computer Science) performance and feedback on the experience with formal methods assisted us in extracting specific problem areas that effect tool usability. The study results infer, along with improvements in verification tool functionalities, teaching program must be modified to include pre-requisite courses to make formal methods easily adapted by students and promoting their usage in software development process.
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series
Venue: arXiv cs.LG 2019
Year: 2019

Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals. We define the notion of a sub-interval relationship (SIR) to capture such interactions that are prominent only in certain sub-intervals of time. To that end, we propose a fast-optimal guaranteed algorithm to find most interesting SIR relationship in a pair of time series. Lastly, we demonstrate the utility of our method in climate science domain based on a real-world dataset along with its scalability scope and obtain useful domain insights.
Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission
Venue: arXiv cs.CL 2019
Year: 2019

Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL) enables models to extract interpretable features from unstructured data, they often require a large amount of training data. However, many tasks in medical domains inherently consist of small sample data with lengthy documents; for a kidney transplant as an example, data from only a few thousand of patients are available and each patient's document consists of a couple of millions of words in major hospitals. Thus, complex DL methods cannot be applied to these kinds of domains. In this paper, we present a comprehensive ensemble model using vector space modeling and topic modeling. Our proposed model is evaluated on the readmission task of kidney transplant patients and improves 0.0211 in terms of c-statistics from the previous state-of-the-art approach using structured data, while typical DL methods fail to beat this approach. The proposed architecture provides the interpretable score for each feature from both modalities, structured and unstructured data, which is shown to be meaningful through a physician's evaluation.
TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
Venue: arXiv cs.CL 2019
Year: 2019

The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/advice or is just socializing. Our primary submission was ranked as the second one among all participants in the official evaluation phase. The article presents our primary solution: Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings. This is followed by a description of two contrastive solutions based on ensemble methods.
Localization in Aerial Imagery with Grid Maps using LocGAN
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps. Currently, most self-localization approaches relate the current sensor observations to a map generated from previously acquired data. Unfortunately, this data is not always available and the generated maps are usually sensor setup specific. Global Navigation Satellite Systems (GNSS) can overcome this problem. However, they are not always reliable especially in urban areas due to multi-path and shadowing effects. Since aerial imagery is usually available, we can use it as prior information. To match aerial images with grid maps, we use conditional Generative Adversarial Networks (cGANs) which transform aerial images to the grid map domain. The transformation between the predicted and measured grid map is estimated using a localization network (LocNet). Given the geo-referenced aerial image transformation the vehicle pose can be estimated. Evaluations performed on the data recorded in region Karlsruhe, Germany show that our LocGAN approach provides reliable global localization results.
Gamification of Enterprise Systems: A Synthesis of Mechanics, Dynamics, and Risks
Venue: arXiv cs.SE 2019
Year: 2019

Organizations highly depend on enterprise systems (ES), which are unlikely to develop their full potential if end-users neglect system usage. Accordingly, organizations attempt to overcome barriers to end-user acceptance in the ES context, which can be attributed to several factors on ES, organizational, and end-user level. Trying to take advantage of the growing passion for games, Gamification is a phenomenon proposed to motivate people by applying elements common to games in other contexts that have the potential to increase end-user acceptance. While first applications of Gamification exist in areas such as finance, health, and education, utility of gamifying ES has not been explored in-depth. Aiming to understand how Gamification can be applied to ES to increase user motivation, we analyze literature concerning game elements (i.e., mechanics and dynamics) used in Gamification and related risks. Our study yields a synthesis of mechanics in clusters of system design, challenges, rewards, and user specifics as well as related dynamics. We discuss the extent to which the game elements can be used to address ES acceptance barriers. While our study reveals that Gamification has potential for motivating ES users, future research should analyze concrete implementations of Gamification in ES contexts to investigate long-term effects.