OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing Variables from their Data via Deep Embeddings of Distributions
Venue: arXiv cs.LG 2019
Year: 2019

A key obstacle in automated analytics and meta-learning is the inability to recognize when different datasets contain measurements of the same variable. Because provided attribute labels are often uninformative in practice, this task may be more robustly addressed by leveraging the data values themselves rather than just relying on their arbitrarily selected variable names. Here, we present a computationally efficient method to identify high-confidence variable matches between a given set of data values and a large repository of previously encountered datasets. Our approach enjoys numerous advantages over distributional similarity based techniques because we leverage learned vector embeddings of datasets which adaptively account for natural forms of data variation encountered in practice. Based on the neural architecture of deep sets, our embeddings can be computed for both numeric and string data. In dataset search and schema matching tasks, our methods outperform standard statistical techniques and we find that the learned embeddings generalize well to new data sources.
RecSim: A Configurable Simulation Platform for Recommender Systems
Venue: arXiv cs.LG 2019
Year: 2019

We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.
Combinatorial Bandits for Sequential Learning in Colonel Blotto Games
Venue: arXiv cs.GT 2019
Year: 2019

The Colonel Blotto game is a renowned resource allocation problem with a long-standing literature in game theory (almost 100 years). However, its scope of application is still restricted by the lack of studies on the incomplete-information situations where a learning model is needed. In this work, we propose and study a regret-minimization model where a learner repeatedly plays the Colonel Blotto game against several adversaries. At each stage, the learner distributes her budget of resources on a fixed number of battlefields to maximize the aggregate value of battlefields she wins; each battlefield being won if there is no adversary that has higher allocation. We focus on the bandit feedback setting. We first show that it can be modeled as a path planning problem. It is then possible to use the classical COMBAND algorithm to guarantee a sub-linear regret in terms of time horizon, but this entails two fundamental challenges: (i) the computation is inefficient due to the huge size of the action set, and (ii) the standard exploration distribution leads to a loose guarantee in practice. To address the first, we construct a modified algorithm that can be efficiently implemented by applying a dynamic programming technique called weight pushing; for the second, we propose methods optimizing the exploration distribution to improve the regret bound. Finally, we implement our proposed algorithm and perform numerical experiments that show the regret improvement in practice.
Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an appropriate graph for the subsequent graph convolution. Existing methods manually construct or learn one fixed graph for all the layers of a GCNN. In order to adapt to the underlying structure of node features in different layers, we propose dynamic learning of graphs and node features jointly in GCNNs. In particular, we cast the graph optimization problem as distance metric learning to capture pairwise similarities of features in each layer. We deploy the Mahalanobis distance metric and further decompose the metric matrix into a low-dimensional matrix, which converts graph learning to the optimization of a low-dimensional matrix for efficient implementation. Extensive experiments on point clouds and citation network datasets demonstrate the superiority of the proposed method in terms of both accuracies and robustness.
The Nubo Virtual Services Marketplace
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we describe a virtual services marketplace, called Nubo, designed to connect buyers of virtual services (or tenants) with providers of those services on a cloud computing platform. The marketplace is implemented as a collection of distributed microservices along with a marketplace portal that runs as a Web application. The heart of Nubo is the Saranyu tenant and service management microservice. Saranyu is a decentralized application (dApp) built on top of the J.P. Morgan Quorum blockchain. Tenant and service accounts are represented as static (nonnegotiable) smart contracts written in the Solidity language. Quorum provides a tamper evident and tamper resistant distributed ledger, whereby multiple cloud and service providers can co-operate to provide service resources to tenants in a trustworthy fashion. Services offer resources consisting of a collection of attributes describing what the tenant can consume, and tenants subscribe to service resources through the Nubo Marketplace portal. The Service Manager microservice provides multitenant support for containerized services built for deployment and orchestration using Docker that were originally not designed to be managed through Saranyu. We discuss our design goals for Nubo, describe the overall architecture, discuss some details on how Saranyu uses the blockchain and smart contracts, and provide comprehensive performance and scalability data measured on the Saranyu REST API. The results indicate Saranyu is competitive with published results for comparable operations on the Havana release of OpenStack Keystone, but Saranyu provides a much richer collection of tenant and service management functionality than Keystone.
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila
Venue: arXiv cs.CV 2019
Year: 2019

Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists. Gene-edited Drosophilae have been used as a test platform for experimental investigation. For example, Parkinson's genes can be embedded into a group of newly bred Drosophilae for research purpose. However, human observation of numerous tiny Drosophilae for a long term is an arduous work, and the dependence on human's acute perception is highly unreliable. As a result, an automated system of social action detection using machine learning has been highly demanded. In this study, we propose to automate the detection and classification of two innate aggressive actions demonstrated by Drosophilae. Robust keypoint detection is achieved using selective spatio-temporal interest points (sSTIP) which are then described using the 3D Scale Invariant Feature Transform (3D-SIFT) descriptors. Dimensionality reduction is performed using Spectral Regression Kernel Discriminant Analysis (SR-KDA) and classification is done using the nearest centre rule. The classification accuracy shown demonstrates the feasibility of the proposed system.
Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
Venue: arXiv cs.CV 2019
Year: 2019

The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence). It requires certain models to simultaneously perform visual and linguistic understandings. Previous work predominantly ignores the precision of segment localization. Sliding window based methods use predefined search window sizes, which suffer from redundant computation, while existing anchor-based approaches fail to yield precise localization. We address this issue by proposing an end-to-end boundary-aware model, which uses a lightweight branch to predict semantic boundaries corresponding to the given linguistic information. To better detect semantic boundaries, we propose to aggregate contextual information by explicitly modeling the relationship between the current element and its neighbors. The most confident segments are subsequently selected based on both anchor and boundary predictions at the testing stage. The proposed model, dubbed Contextual Boundary-aware Prediction (CBP), outperforms its competitors with a clear margin on three public datasets. All codes are available on https://github.com/JaywongWang/CBP .
A Survey on Studying the Social Networks of Students
Venue: arXiv cs.SI 2019
Year: 2019

Do studies show that physical and online students' social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students' social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students' social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors.
Structural Robustness for Deep Learning Architectures
Venue: arXiv cs.LG 2019
Year: 2019

Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges. Unfortunately, they are susceptible to various types of noise, including adversarial attacks and corrupted inputs. In this work we introduce a formal definition of robustness which can be viewed as a localized Lipschitz constant of the network function, quantified in the domain of the data to be classified. We compare this notion of robustness to existing ones, and study its connections with methods in the literature. We evaluate this metric by performing experiments on various competitive vision datasets.
Should We Worry About Interference in Emerging Dense NGSO Satellite Constellations?
Venue: arXiv cs.NI 2019
Year: 2019

Many satellite operators are planning to deploy NGSO systems for broadband communication services in the Ku-, Ka-, and V-band, where some of them have already launched. Consequently, new challenges are expected for inter-system satellite coexistence due to the increased interference level and the complexity of the interactions resulting from the heterogeneity of the constellations. This is especially relevant for the Ku-band, where the NGSO systems are most diverse and existing GSO systems, which often support critical services, must be protected from interference. It is thus imperative to evaluate the impact of mutual inter-system interference, the efficiency of the basic interference mitigation techniques, and whether regulatory intervention is needed for the new systems. We conduct an extensive study of inter-satellite coexistence in the Ku-band, where we consider all recently proposed NGSO and some selected GSO systems. Our throughput degradation results suggest that existing spectrum regulation may be insufficient to ensure GSO protection from NGSO interference, especially due to the high transmit power of the LEO Kepler satellites. This also results in strong interference towards other NGSO systems, where traditional interference mitigation techniques like look-aside may perform poorly. Specifically, look-aside can be beneficial for large constellations, but detrimental for small constellations. Furthermore, we confirm that band-splitting among satellite operators significantly degrades throughput, also for the Ku-band. Our results overall show that the complexity of the inter-satellite interactions for new NGSO systems is too high to be managed via simple interference mitigation techniques. This means that more sophisticated engineering solutions, and potentially even more strict regulatory requirements, will be needed to ensure coexistence in emerging, dense NGSO deployments.
The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews
Venue: arXiv cs.CL 2019
Year: 2019

Review helpfulness serves as focal point in understanding customers' purchase decision-making process on online retailer platforms. An overwhelming majority of previous works find longer reviews to be more helpful than short reviews. In this paper, we propose that longer reviews should not be assumed to be uniformly more helpful; instead, we argue that the effect depends on the line of argumentation in the review text. To test this idea, we use a large dataset of customer reviews from Amazon in combination with a state-of-the-art approach from natural language processing that allows us to study argumentation lines at sentence level. Our empirical analysis suggests that the frequency of argumentation changes moderates the effect of review length on helpfulness. Altogether, we disprove the prevailing narrative that longer reviews are uniformly perceived as more helpful. Our findings allow retailer platforms to improve their customer feedback systems and to feature more useful product reviews.
Frustratingly Easy Natural Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa. Additionally, a lot of systems on the QA leaderboards do not have associated research documentation in order to successfully replicate their experiments. In this paper, we outline these algorithmic components such as Attention-over-Attention, coupled with data augmentation and ensembling strategies that have shown to yield state-of-the-art results on benchmark datasets like SQuAD, even achieving super-human performance. Contrary to these prior results, when we evaluate on the recently proposed Natural Questions benchmark dataset, we find that an incredibly simple approach of transfer learning from BERT outperforms the previous state-of-the-art system trained on 4 million more examples than ours by 1.9 F1 points. Adding ensembling strategies further improves that number by 2.3 F1 points.
Test Model Coverage Analysis under Uncertainty
Venue: arXiv cs.SE 2019
Year: 2019

In model-based testing (MBT) we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution paths, depending on some internal decisions made by the software. Consequently, performing precise test analyses, e.g. to calculate the test coverage, are not possible. This can be mitigated if developers can annotate the model with estimated probabilities for taking each transition. A probabilistic model checking algorithm can subsequently be used to do simple probabilistic coverage analysis. However, in practice developers often want to know what the achieved aggregate coverage, which unfortunately cannot be re-expressed as a standard model checking problem. This paper presents an extension to allow efficient calculation of probabilistic aggregate coverage, and moreover also in combination with k-wise coverage.
Measures in Visualization Space
Venue: arXiv cs.GR 2019
Year: 2019

Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of individual visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.
GMLS-Nets: A framework for learning from unstructured data
Venue: arXiv cs.LG 2019
Year: 2019

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.
Minimizing Margin of Victory for Fair Political and Educational Districting
Venue: arXiv cs.SI 2019
Year: 2019

In many practical scenarios, a population is divided into disjoint groups for better administration, e.g., electorates into political districts, employees into departments, students into school districts, and so on. However, grouping people arbitrarily may lead to biased partitions, raising concerns of gerrymandering in political districting, racial segregation in schools, etc. To counter such issues, in this paper, we conceptualize such problems in a voting scenario, and propose FAIR DISTRICTING problem to divide a given set of people having preference over candidates into k groups such that the maximum margin of victory of any group is minimized. We also propose the FAIR CONNECTED DISTRICTING problem which additionally requires each group to be connected. We show that the FAIR DISTRICTING problem is NP-complete for plurality voting even if we have only 3 candidates but admits polynomial time algorithms if we assume k to be some constant or everyone can be moved to any group. In contrast, we show that the FAIR CONNECTED DISTRICTING problem is NP-complete for plurality voting even if we have only 2 candidates and k = 2. Finally, we propose heuristic algorithms for both the problems and show their effectiveness in UK political districting and in lowering racial segregation in public schools in the US.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets
Venue: arXiv cs.CV 2019
Year: 2019

Autonomous driving requires various computer vision algorithms, such as object detection and tracking.Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that corrects loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox,we can increase the number of object edges that are labeled automatically (within 1\% error) by 50% to reduce manual annotation time.
Reusing Convolutional Activations from Frame to Frame to Speed up Training and Inference
Venue: arXiv cs.CV 2019
Year: 2019

When processing similar frames in succession, we can take advantage of the locality of the convolution operation to reevaluate only portions of the image that changed from the previous frame. By saving the output of a layer of convolutions and calculating the change from frame to frame, we can reuse previous activations and save computational resources that would otherwise be wasted recalculating convolutions whose outputs we have already observed. This technique can be applied to many domains, such as processing videos from stationary video cameras, studying the effects of occluding or distorting sections of images, applying convolution to multiple frames of audio or time series data, or playing Atari games. Furthermore, this technique can be applied to speed up both training and inference.
Illuminated Decision Trees with Lucid
Venue: arXiv cs.CV 2019
Year: 2019

The Lucid methods described by Olah et al. (2018) provide a way to inspect the inner workings of neural networks trained on image classification tasks using feature visualization. Such methods have generally been applied to networks trained on visually rich, large-scale image datasets like ImageNet, which enables them to produce enticing feature visualizations. To investigate these methods further, we applied them to classifiers trained to perform the much simpler (in terms of dataset size and visual richness), yet challenging task of distinguishing between different kinds of white blood cell from microscope images. Such a task makes generating useful feature visualizations difficult, as the discriminative features are inherently hard to identify and interpret. We address this by presenting the "Illuminated Decision Tree" approach, in which we use a neural network trained on the task as a feature extractor, then learn a decision tree based on these features, and provide Lucid visualizations for each node in the tree. We demonstrate our approach with several examples, showing how this approach could be useful both in model development and debugging, and when explaining model outputs to non-experts.
Requirements Intelligence with OpenReq Analytics
Venue: arXiv cs.SE 2019
Year: 2019

With the rise of social media like Twitter and distribution platforms like app stores, users have various ways to express their opinions about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams. However, a manual analysis of user feedback is cumbersome and hard to manage. We present OpenReq Analytics, a software requirements intelligence service, that collects, processes, analyzes, and visualizes user feedback.