Reviewerly/exHarmony huggingface dataset:
Joint modeling of users, questions and answers for answer selection in CQA

In this paper, we propose solutions to advance answer selection in Community Question Answering (CQA). Automatically selecting correct answers can significantly improve intelligence for CQA, as users are not required to browse the large quantity of texts and select the right answers manually. Also, automatic answers selection can minimize the time for satisfying users seeking the correct answers and maximize user engagement with the site. Unlike previous works, we propose a hybrid attention mechanism to model question-answer pairs. Specifically, for each word, we calculate the intra-sentence attention indicating its local importance and the inter-sentence attention implying its importance to the counterpart sentence. The inter-sentence attention is based on the interactions between question-answer pairs, and the combination of these two attention mechanisms enables us to align the most informative parts in question-answer pairs for sentence matching. Additionally, we exploit user information for answer selection due to the fact that users are more likely to provide correct answers in their areas of expertise. We model users from their written answers to alleviate data sparsity problem, and then learn user representations according to the informative parts in sentences that are useful for question-answer matching task. This mean of modelling users can bridge the semantic gap between different users, as similar users may have the same way of wording their answers. The representations of users, questions and answers are learnt in an end-to-end neural network in a mean that best explains the interrelation between question-answer pairs. We validate the proposed model on a public dataset, and demonstrate its advantages over the baselines with thorough experiments.
Search, sense making and learning: closing gaps

Purpose This paper aims to discuss how search, sense making and learning have become more closely integrated, as search services have leveraged new technologies and large and media-diverse data streams. Design/methodology/approach The paper reviews progress in search over the past 60 years, summarizes different theories of sense making and learning and proposes a framework for integrating these activities. Findings The arguments are supported with examples from search in 2018 and suggest that even as search becomes an automated process during learning, search strategies must continue to evolve to insure that complex information needs can be met. Research limitations/implications The work is limited to search that uses electronic search systems. Implications include the need to understand that multiple levels of system inferences/estimates are used to present search results and that different kinds of learning processes are affected by search systems. Social implications The importance of information literacy is implied. Originality/value This paper will provide readers with an understanding of how search services and systems have evolved and their implications for human learning.
Augmented Reality Graph Visualizations

Three-dimensional node-link diagrams are an important class of visualization for immersive analysis. Yet, there is little knowledge on how to visualize edges to support efficient analysis. We present an exploration of the design space for edge styles and discuss the results of a user study comparing six different edge variants.
CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining

Recommender system suggests a personalized recommendation by filtering the information based on users interest. Nowadays, users like to purchase the best possible items and services to spend the shortest span of time. The cross-domain recommendation system is a method of recommendation wherein knowledge is gathered from multiple domains. With respect to the user’s search term from the source domain, most similar items are recommended from the target domain. Semantic similarity between two different items can be achieved through Wpath method using Ontology. PrefixSpan is used for generating sequential patterns and Topseq rule mining algorithm is used for finding the frequent sequential rule. So, this work tries to extend cross domain recommendation by 1) finding the semantic similarity of items using Ontology; 2) applying Collaborative Filtering for finding similar items and users; 3) generating frequent item sequences using PrefixSpan sequential pattern mining algorithm and 4) recommending user preferred items using Topseq rule mining algorithm. The recommender system is evaluated considering precision, recall and F1 Score measures. It finds CD-SPM which yields better F1 Score. The proposed approach also alleviates the new user problem and sparsity problem to some extent.
SIGCHI Lifetime Research Award Talk

Today's mainstream Human-Computer Interaction (HCI) research primarily addresses functional concerns - the needs of users, practical applications, and usability evaluation. Tangible Bits and Radical Atoms are driven by vision and carried out with an artistic approach. While today's technologies will become obsolete in one year, and today's applications will be replaced in 10 years, true visions - we believe - can last longer than 100 years. Tangible Bits (3, 4) seeks to realize seamless interfaces between humans, digital information, and the physical environment by giving physical form to digital information and computation, making bits directly manipulatable and perceptible both in the foreground and background of our consciousness (peripheral awareness). Our goal is to invent new design media for artistic expression as well as for scientific analysis, taking advantage of the richness of human senses and skills we develop throughout our lifetime interacting with the physical world, as well as the computational reflection enabled by real-time sensing and digital feedback. Radical Atoms (5) leaps beyond Tangible Bits by assuming a hypothetical generation of materials that can change form and properties dynamically, becoming as reconfigurable as pixels on a screen. Radical Atoms is the future material that can transform its shape, conform to constraints, and inform the users of their affordances. Radical Atoms is a vision for the future of Human-Material Interaction, in which all digital information has a physical manifestation, thus enabling us to interact directly with it.
Cryptographic Accumulator-Based Scheme for Critical Data Integrity Verification in Cloud Storage

Public cloud storage is a fundamental cloud computing service.Currently, most owners of large data outsource their data to cloud storage services-even high-profile owners such as governments.However, public cloud storage services are not optimal for ensuring the possession and integrity of the outsourced data, a situation that has given rise to many proposed provable data possession check schemes (PDP).A PDP scheme allows data owners to efficiently, periodically, and securely verify that a cloud storage provider possesses the outsourced data.Most of the currently available provable data possession check schemes make selective (i.e., probabilistic) checks using random data blocks to verify data integrity rather than checking the entire dataset.Therefore, these schemes are considered inadequate by critical infrastructure sectors that involve highly sensitive data (critical data).In this paper, a new and efficient deterministic data integrity check scheme called cryptographic-accumulator provable data possession (CAPDP) is proposed.The CAPDP surpasses the common limitations exhibited by other currently proposed schemes.The underlying scheme of the CAPDP is based on a modified RSA-based cryptographic accumulator that has the following advantages: 1) it allows the data owner to perform an unlimited number of data integrity checks; 2) it supports data dynamics; 3) it is efficient in terms of communication, computation and storage costs for both the data owner and the cloud storage provider; 4) the verification operation in the proposed scheme is independent of the number of blocks being verified; 5) it minimizes the burden and cost of the verification process on the data owner's side, enabling verification to be performed even on low-power devices; and 6) it prevents tag forgery, data deletion, replacement, and data leakage attacks and detects replay attacks.Moreover, the prototype implementation and experimental results show that the scheme is applicable in real-life applications.
Geospatial Object Detection via Deconvolutional Region Proposal Network

In the problem of geospatial object detection, the quality and amount of reference boxes significantly impact the detection performance and prediction speed of object detection networks. Nowadays, most of the popular detection methods adopt the anchor mechanism to generate reference boxes. This paper proposed an anchor-free and sliding-window-free deconvolutional region proposal network and constructed a two-stage deconvolutional object detection network. Instead of using an anchor mechanism, we proposed to use a deconvolutional neural network followed by a connected region generation module to generate reference boxes. The comparison experiments and quantitative analysis with NWPU VHR-10 dataset demonstrate that DeRPN can vastly reduce the number of reference boxes and improve the precision of the reference box coordinates. The experiments also suggest that our proposed two-stage object detection network can not only obtain the nearly state-of-the-art detection results but also achieve the prediction speed close to that of the one-stage detection network.
A Fourier Perspective on Model Robustness in Computer Vision

Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision. Data augmentation is a commonly used approach for improving robustness, however robustness gains are typically not uniform across corruption types. Indeed increasing performance in the presence of random noise is often met with reduced performance on other corruptions such as contrast change. Understanding when and why these sorts of trade-offs occur is a crucial step towards mitigating them. Towards this end, we investigate recently observed trade-offs caused by Gaussian data augmentation and adversarial training. We find that both methods improve robustness to corruptions that are concentrated in the high frequency domain while reducing robustness to corruptions that are concentrated in the low frequency domain. This suggests that one way to mitigate these trade-offs via data augmentation is to use a more diverse set of augmentations. Towards this end we observe that AutoAugment, a recently proposed data augmentation policy optimized for clean accuracy, achieves state-of-the-art robustness on the CIFAR-10-C benchmark.
Class-Agnostic Counting

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.
Width Provably Matters in Optimization for Deep Linear Neural Networks.

We prove that for an $L$-layer fully-connected linear neural network, if the width of every hidden layer is $\tilde\Omega (L \cdot r \cdot d_{\mathrm{out}} \cdot \kappa^3 )$, where $r$ and $\kappa$ are the rank and the condition number of the input data, and $d_{\mathrm{out}}$ is the output dimension, then gradient descent with Gaussian random initialization converges to a global minimum at a linear rate. The number of iterations to find an $\epsilon$-suboptimal solution is $O(\kappa \log(\frac{1}{\epsilon}))$. Our polynomial upper bound on the total running time for wide deep linear networks and the $\exp\left(\Omega\left(L\right)\right)$ lower bound for narrow deep linear neural networks [Shamir, 2018] together demonstrate that wide layers are necessary for optimizing deep models.
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize. We demonstrate that a trained agent fails completely when facing small visual changes, and that fine-tuning---the common transfer learning paradigm---fails to adapt to these changes, to the extent that it is faster to re-train the model from scratch. We show that by separating the visual transfer task from the control policy we achieve substantially better sample efficiency and transfer behavior, allowing an agent trained on the source task to transfer well to the target tasks. The visual mapping from the target to the source domain is performed using unaligned GANs, resulting in a control policy that can be further improved using imitation learning from imperfect demonstrations. We demonstrate the approach on synthetic visual variants of the Breakout game, as well as on transfer between subsequent levels of Road Fighter, a Nintendo car-driving game. A visualization of our approach can be seen in this https URL and this https URL .
What’s in a Name?

Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Optimizing for Recall in Automatic Requirements Classification: An Empirical Study

Using Machine Learning to solve requirements engineering problems can be a tricky task. Even though certain algorithms have exceptional performance, their recall is usually below 100%. One key aspect in the implementation of machine learning tools is the balance between recall and precision. Tools that do not find all correct answers may be considered useless. However, some tasks are very complicated and even requirements engineers struggle to solve them perfectly. If a tool achieves performance comparable to a trained engineer while reducing her workload considerably, it is considered to be useful. One such task is the classification of specification content elements into requirements and non-requirements. In this paper, we analyze this specific requirements classification problem and assess the importance of recall by performing an empirical study. We compared two groups of students who performed this task with and without tool support, respectively. We use the results to compute an estimate of β for the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">β</sub> score, allowing us to choose the optimal balance between precision and recall. Furthermore, we use the results to assess the practical time savings realized by the approach. By using the tool, users may not be able to find all defects in a document, however, they will be able to find close to all of them in a fraction of the time necessary. This demonstrates the practical usefulness of our approach and machine learning tools in general.
Emergence of Object Segmentation in Perturbed Generative Models

We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories.
Accurate, reliable and fast robustness evaluation

Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. Despite much attention, however, progress towards more robust models is significantly impaired by the difficulty of evaluating the robustness of neural network models. Today's methods are either fast but brittle (gradient-based attacks), or they are fairly reliable but slow (score- and decision-based attacks). We here develop a new set of gradient-based adversarial attacks which (a) are more reliable in the face of gradient-masking than other gradient-based attacks, (b) perform better and are more query efficient than current state-of-the-art gradient-based attacks, (c) can be flexibly adapted to a wide range of adversarial criteria and (d) require virtually no hyperparameter tuning. These findings are carefully validated across a diverse set of six different models and hold for L0, L1, L2 and Linf in both targeted as well as untargeted scenarios. Implementations will soon be available in all major toolboxes (Foolbox, CleverHans and ART). We hope that this class of attacks will make robustness evaluations easier and more reliable, thus contributing to more signal in the search for more robust machine learning models.
List-decodable Linear Regression

We give the first polynomial-time algorithm for robust regression in the list-decodable setting where an adversary can corrupt a greater than 1/2 fraction of examples. For any \alpha < 1, our algorithm takes as input a sample {(x_i,y_i)}_{i \leq n} of n linear equations where \alpha n of the equations satisfy y_i = \langle x_i,\ell^*\rangle +\zeta for some small noise \zeta and (1-\alpha) n of the equations are {\em arbitrarily} chosen. It outputs a list L of size O(1/\alpha) - a fixed constant - that contains an \ell that is close to \ell^*. Our algorithm succeeds whenever the inliers are chosen from a certifiably anti-concentrated distribution D. In particular, this gives a (d/\alpha)^{O(1/\alpha^8)} time algorithm to find a O(1/\alpha) size list when the inlier distribution is a standard Gaussian. For discrete product distributions that are anti-concentrated only in regular directions, we give an algorithm that achieves similar guarantee under the promise that \ell^* has all coordinates of the same magnitude. To complement our result, we prove that the anti-concentration assumption on the inliers is information-theoretically necessary. To solve the problem we introduce a new framework for list-decodable learning that strengthens the ``identifiability to algorithms'' paradigm based on the sum-of-squares method.
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models that are not easily explainable for humans. We propose DRUM, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs that resolves these problems. We motivate our method by making a connection between learning confidence scores for each rule and low-rank tensor approximation. DRUM uses bidirectional RNNs to share useful information across the tasks of learning rules for different relations. We also empirically demonstrate the efficiency of DRUM over existing rule mining methods for inductive link prediction on a variety of benchmark datasets.
Efficiently escaping saddle points on manifolds

Smooth, non-convex optimization problems on Riemannian manifolds occur in machine learning as a result of orthonormality, rank or positivity constraints. First- and second-order necessary optimality conditions state that the Riemannian gradient must be zero, and the Riemannian Hessian must be positive semidefinite. Generalizing Jin et al.'s recent work on perturbed gradient descent (PGD) for optimization on linear spaces [How to Escape Saddle Points Efficiently (2017), Stochastic Gradient Descent Escapes Saddle Points Efficiently (2019)], we study a version of perturbed Riemannian gradient descent (PRGD) to show that necessary optimality conditions can be met approximately with high probability, without evaluating the Hessian. Specifically, for an arbitrary Riemannian manifold $\mathcal{M}$ of dimension $d$, a sufficiently smooth (possibly non-convex) objective function $f$, and under weak conditions on the retraction chosen to move on the manifold, with high probability, our version of PRGD produces a point with gradient smaller than $\epsilon$ and Hessian within $\sqrt{\epsilon}$ of being positive semidefinite in $O((\log{d})^4 / \epsilon^{2})$ gradient queries. This matches the complexity of PGD in the Euclidean case. Crucially, the dependence on dimension is low, which matters for large-scale applications including PCA and low-rank matrix completion, which both admit natural formulations on manifolds. The key technical idea is to generalize PRGD with a distinction between two types of gradient steps: ``steps on the manifold'' and ``perturbed steps in a tangent space of the manifold.'' Ultimately, this distinction makes it possible to extend Jin et al.'s analysis seamlessly.