OpenRaiser/Intern-Atlas huggingface dataset:
Large-scale, real-time visual-inertial localization revisited
Venue: arXiv cs.CV 2019
Year: 2019

The overarching goals in image-based localization are scale, robustness and speed. In recent years, approaches based on local features and sparse 3D point-cloud models have both dominated the benchmarks and seen successful realworld deployment. They enable applications ranging from robot navigation, autonomous driving, virtual and augmented reality to device geo-localization. Recently end-to-end learned localization approaches have been proposed which show promising results on small scale datasets. However the positioning accuracy, scalability, latency and compute & storage requirements of these approaches remain open challenges. We aim to deploy localization at global-scale where one thus relies on methods using local features and sparse 3D models. Our approach spans from offline model building to real-time client-side pose fusion. The system compresses appearance and geometry of the scene for efficient model storage and lookup leading to scalability beyond what what has been previously demonstrated. It allows for low-latency localization queries and efficient fusion run in real-time on mobile platforms by combining server-side localization with real-time visual-inertial-based camera pose tracking. In order to further improve efficiency we leverage a combination of priors, nearest neighbor search, geometric match culling and a cascaded pose candidate refinement step. This combination outperforms previous approaches when working with large scale models and allows deployment at unprecedented scale. We demonstrate the effectiveness of our approach on a proof-of-concept system localizing 2.5 million images against models from four cities in different regions on the world achieving query latencies in the 200ms range.
Adversarially Trained Deep Neural Semantic Hashing Scheme for Subjective Search in Fashion Inventory
Venue: arXiv cs.CV 2019
Year: 2019

The simple approach of retrieving a closest match of a query image from one in the gallery, compares an image pair using sum of absolute difference in pixel or feature space. The process is computationally expensive, ill-posed to illumination, background composition, pose variation, as well as inefficient to be deployed on gallery sets with more than 1000 elements. Hashing is a faster alternative which involves representing images in reduced dimensional simple feature spaces. Encoding images into binary hash codes enables similarity comparison in an image-pair using the Hamming distance measure. The challenge, however, lies in encoding the images using a semantic hashing scheme that lets subjective neighbors lie within the tolerable Hamming radius. This work presents a solution employing adversarial learning of a deep neural semantic hashing network for fashion inventory retrieval. It consists of a feature extracting convolutional neural network (CNN) learned to (i) minimize error in classifying type of clothing, (ii) minimize hamming distance between semantic neighbors and maximize distance between semantically dissimilar images, (iii) maximally scramble a discriminator's ability to identify the corresponding hash code-image pair when processing a semantically similar query-gallery image pair. Experimental validation for fashion inventory search yields a mean average precision (mAP) of 90.65% in finding the closest match as compared to 53.26% obtained by the prior art of deep Cauchy hashing for hamming space retrieval.
Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce
Venue: arXiv cs.IR 2019
Year: 2019

Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce platforms seeking to implement real-time accurate NBA (next best action) policies. However, due to the low frequency of conversion events and the noisiness of browsing data, classifying user sessions is very challenging. In this paper, we address the clickstream classification problem in the fashion industry and present three major contributions to the burgeoning field of AI in fashion: first, we collected, normalized and prepared a novel dataset of live shopping sessions from a major European e-commerce fashion website; second, we use the dataset to test in a controlled environment strong baselines and SOTA models from the literature; finally, we propose a new discriminative neural model that outperforms neural architectures recently proposed at Rakuten labs.
Evaluating Language Model Finetuning Techniques for Low-resource Languages
Venue: arXiv cs.CL 2019
Year: 2019

Unlike mainstream languages (such as English and French), low-resource languages often suffer from a lack of expert-annotated corpora and benchmark resources that make it hard to apply state-of-the-art techniques directly. In this paper, we alleviate this scarcity problem for the low-resourced Filipino language in two ways. First, we introduce a new benchmark language modeling dataset in Filipino which we call WikiText-TL-39. Second, we show that language model finetuning techniques such as BERT and ULMFiT can be used to consistently train robust classifiers in low-resource settings, experiencing at most a 0.0782 increase in validation error when the number of training examples is decreased from 10K to 1K while finetuning using a privately-held sentiment dataset.
Requisite Variety in Ethical Utility Functions for AI Value Alignment
Venue: arXiv cs.AI 2019
Year: 2019

Being a complex subject of major importance in AI Safety research, value alignment has been studied from various perspectives in the last years. However, no final consensus on the design of ethical utility functions facilitating AI value alignment has been achieved yet. Given the urgency to identify systematic solutions, we postulate that it might be useful to start with the simple fact that for the utility function of an AI not to violate human ethical intuitions, it trivially has to be a model of these intuitions and reflect their variety $ - $ whereby the most accurate models pertaining to human entities being biological organisms equipped with a brain constructing concepts like moral judgements, are scientific models. Thus, in order to better assess the variety of human morality, we perform a transdisciplinary analysis applying a security mindset to the issue and summarizing variety-relevant background knowledge from neuroscience and psychology. We complement this information by linking it to augmented utilitarianism as a suitable ethical framework. Based on that, we propose first practical guidelines for the design of approximate ethical goal functions that might better capture the variety of human moral judgements. Finally, we conclude and address future possible challenges.
Network-accelerated Distributed Machine Learning Using MLFabric
Venue: arXiv cs.DC 2019
Year: 2019

Existing distributed machine learning (DML) systems focus on improving the computational efficiency of distributed learning, whereas communication aspects have received less attention. Many DML systems treat the network as a blackbox. Thus, DML algorithms' performance is impeded by network bottlenecks, and DML systems end up sacrificing important algorithmic and system-level benefits. We present MLfabric, a communication library that manages all network transfers in a DML system, and holistically determines the communication pattern of a DML algorithm at any point in time. This allows MLfabric to carefully order transfers (i.e., gradient updates) to improve convergence, opportunistically aggregate updates in-network to improve efficiency, and proactively replicate some of them to support new notions of fault tolerance. We empirically find that MLfabric achieves up to 3X speed-up in training large deep learning models in realistic dynamic cluster settings.
YouTube Chatter: Understanding Online Comments Discourse on Misinformative and Political YouTube Videos
Venue: arXiv cs.CY 2019
Year: 2019

We conduct a preliminary analysis of comments on political YouTube content containing misinformation in comparison to comments on trustworthy or apolitical videos, labelling the bias and factual ratings of our channels according to Media Bias Fact Check where applicable. One of our most interesting discoveries is that especially-polarized or misinformative political channels (Left-Bias, Right-Bias, PragerU, Conspiracy-Pseudoscience, and Questionable Source) generate 7.5x more comments per view and 10.42x more replies per view than apolitical or Pro-Science channels; in particular, Conspiracy-Pseudoscience and Questionable Sources generate 8.3x more comments per view and 11.0x more replies per view than apolitical and Pro-Science channels. We also compared average thread lengths, average comment lengths, and profanity rates across channels, and present simple machine learning classifiers for predicting the bias category of a video based on these statistics.
Multiplicative Models for Recurrent Language Modeling
Venue: arXiv cs.LG 2019
Year: 2019

Recently, there has been interest in multiplicative recurrent neural networks for language modeling. Indeed, simple Recurrent Neural Networks (RNNs) encounter difficulties recovering from past mistakes when generating sequences due to high correlation between hidden states. These challenges can be mitigated by integrating second-order terms in the hidden-state update. One such model, multiplicative Long Short-Term Memory (mLSTM) is particularly interesting in its original formulation because of the sharing of its second-order term, referred to as the intermediate state. We explore these architectural improvements by introducing new models and testing them on character-level language modeling tasks. This allows us to establish the relevance of shared parametrization in recurrent language modeling.
A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation
Venue: arXiv cs.NI 2019
Year: 2019

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation.
Genus 2 Supersingular Isogeny Oblivious Transfer
Venue: arXiv cs.CR 2019
Year: 2019

We present an oblivious transfer scheme that extends the proposal made by Barreto, Oliveira and Benits, based in isogenies supersingular elliptic curves, to the setting of principally polarized supersingular abelian surfaces.
Bayesian Generalized Network Design
Venue: arXiv cs.GT 2019
Year: 2019

We study network coordination problems, as captured by the setting of generalized network design (Emek et al., STOC 2018), in the face of uncertainty resulting from partial information that the network users hold regarding the actions of their peers. This uncertainty is formalized using Alon et al.'s Bayesian ignorance framework (TCS 2012). While the approach of Alon et al. is purely combinatorial, the current paper takes into account computational considerations: Our main technical contribution is the development of (strongly) polynomial time algorithms for local decision making in the face of Bayesian uncertainty.
Cross-view Relation Networks for Mammogram Mass Detection
Venue: arXiv cs.CV 2019
Year: 2019

Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, and this is crucial for doctors' decisions in clinical practice. However, existing mass detection methods do not consider jointly learning effective features from the two relational views. To address this issue, this paper proposes a novel mammogram mass detection framework, termed Cross-View Relation Region-based Convolutional Neural Networks (CVR-RCNN). The proposed CVR-RCNN is expected to capture the latent relation information between the corresponding mass region of interests (ROIs) from the two paired views. Evaluations on a new large-scale private dataset and a public mammogram dataset show that the proposed CVR-RCNN outperforms existing state-of-the-art mass detection methods. Meanwhile, our experimental results suggest that incorporating the relation information across two views helps to train a superior detection model, which is a promising avenue for mammogram mass detection.
Mismatched Guesswork
Venue: arXiv cs.IT 2019
Year: 2019

We study the problem of mismatched guesswork, where we evaluate the number of symbols $y \in \mathcal{Y}$ which have higher likelihood than $X \sim μ$ according to a mismatched distribution $ν$. We discuss the role of the tilted/exponential families of the source distribution $μ$ and of the mismatched distribution $ν$. We show that the value of guesswork can be characterized using the tilted family of the mismatched distribution $ν$, while the probability of guessing is characterized by an exponential family which passes through $μ$. Using this characterization, we demonstrate that the mismatched guesswork follows a large deviation principle (LDP), where the rate function is described implicitly using information theoretic quantities. We apply these results to one-to-one source coding (without prefix free constraint) to obtain the cost of mismatch in terms of average codeword length. We show that the cost of mismatch in one-to-one codes is no larger than that of the prefix-free codes, i.e., $D(μ\| ν)$. Further, the cost of mismatch vanishes if and only if $ν$ lies on the tilted family of the true distribution $μ$, which is in stark contrast to the prefix-free codes. These results imply that one-to-one codes are inherently more robust to mismatch.
On Symmetry and Initialization for Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically verify this and show that this does not hold when the initial conditions are chosen at random. The proof of convergence investigates the interaction between the two layers of the network. Our results highlight the importance of using symmetry in the design of neural networks.
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
Venue: arXiv cs.CL 2019
Year: 2019

Learning algorithms become more powerful, often at the cost of increased complexity. In response, the demand for algorithms to be transparent is growing. In NLP tasks, attention distributions learned by attention-based deep learning models are used to gain insights in the models' behavior. To which extent is this perspective valid for all NLP tasks? We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization. To this end, we present both a qualitative and quantitative analysis to investigate the behavior of the attention heads. We show that some attention heads indeed specialize towards syntactically and semantically distinct input. We propose an approach to evaluate to which extent the Transformer model relies on specifically learned attention distributions. We also discuss what this implies for using attention distributions as a means of transparency.
EQuANt (Enhanced Question Answer Network)
Venue: arXiv cs.CL 2019
Year: 2019

Machine Reading Comprehension (MRC) is an important topic in the domain of automated question answering and in natural language processing more generally. Since the release of the SQuAD 1.1 and SQuAD 2 datasets, progress in the field has been particularly significant, with current state-of-the-art models now exhibiting near-human performance at both answering well-posed questions and detecting questions which are unanswerable given a corresponding context. In this work, we present Enhanced Question Answer Network (EQuANt), an MRC model which extends the successful QANet architecture of Yu et al. to cope with unanswerable questions. By training and evaluating EQuANt on SQuAD 2, we show that it is indeed possible to extend QANet to the unanswerable domain. We achieve results which are close to 2 times better than our chosen baseline obtained by evaluating a lightweight version of the original QANet architecture on SQuAD 2. In addition, we report that the performance of EQuANt on SQuAD 1.1 after being trained on SQuAD2 exceeds that of our lightweight QANet architecture trained and evaluated on SQuAD 1.1, demonstrating the utility of multi-task learning in the MRC context.
Creek: Low-latency, Mixed-Consistency Transactional Replication Scheme
Venue: arXiv cs.DC 2019
Year: 2019

In this paper we introduce Creek, a low-latency, eventually consistent replication scheme that also enables execution of strongly consistent operations (akin to ACID transactions). Operations can have arbitrary complex (but deterministic) semantics. Similarly to state machine replication (SMR), Creek totally-orders all operations, but does so using two different broadcast mechanisms: a timestamp-based one and our novel conditional atomic broadcast (CAB). The former is used to establish a tentative order of all operations for speculative execution, and it can tolerate network partitions. On the other hand, CAB is only used to ensure linearizable execution of the strongly consistent operations, whenever distributed consensus can be solved. The execution of strongly consistent operations also stabilizes the execution order of the causally related weakly consistent operations. Creek uses multiversion concurrency control to efficiently handle operations' rollbacks and reexecutions resulting from the mismatch between the tentative and the final execution orders. In the TPC-C benchmark, Creek offers up to 2.5 times lower latency in returning client responses compared to the state-of-the-art speculative SMR scheme, while maintaining high accuracy of the speculative execution (92-100%).
Trimming Graphs Using Clausal Proof Optimization
Venue: arXiv cs.LO 2019
Year: 2019

We present a method to gradually compute a smaller and smaller unsatisfiable core of a propositional formula by minimizing proofs of unsatisfiability. The goal is to compute a minimal unsatisfiable core that is relatively small compared to other minimal unsatisfiable cores of the same formula. We try to achieve this goal by postponing deletion of arbitrary clauses from the formula as long as possible---in contrast to existing minimal unsatisfiable core algorithms. We applied this method to reduce the smallest known unit-distance graph with chromatic number 5 from 553 vertices and 2720 edges to 529 vertices and 2670 edges.
ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and Recognition -- RRC-MLT-2019
Venue: arXiv cs.CV 2019
Year: 2019

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.
Universal audio synthesizer control with normalizing flows
Venue: arXiv cs.LG 2019
Year: 2019

The ubiquity of sound synthesizers has reshaped music production and even entirely defined new music genres. However, the increasing complexity and number of parameters in modern synthesizers make them harder to master. Hence, the development of methods allowing to easily create and explore with synthesizers is a crucial need. Here, we introduce a novel formulation of audio synthesizer control. We formalize it as finding an organized latent audio space that represents the capabilities of a synthesizer, while constructing an invertible mapping to the space of its parameters. By using this formulation, we show that we can address simultaneously automatic parameter inference, macro-control learning and audio-based preset exploration within a single model. To solve this new formulation, we rely on Variational Auto-Encoders (VAE) and Normalizing Flows (NF) to organize and map the respective auditory and parameter spaces. We introduce the disentangling flows, which allow to perform the invertible mapping between separate latent spaces, while steering the organization of some latent dimensions to match target variation factors by splitting the objective as partial density evaluation. We evaluate our proposal against a large set of baseline models and show its superiority in both parameter inference and audio reconstruction. We also show that the model disentangles the major factors of audio variations as latent dimensions, that can be directly used as macro-parameters. We also show that our model is able to learn semantic controls of a synthesizer by smoothly mapping to its parameters. Finally, we discuss the use of our model in creative applications and its real-time implementation in Ableton Live
Geographical Security Questions for Fallback Authentication
Venue: arXiv cs.HC 2019
Year: 2019

Fallback authentication is the backup authentication method used when the primary authentication method (e.g., passwords, fingerprints, etc.) fails. Currently, widely-deployed fallback authentication methods (e.g., security questions, email resets, and SMS resets) suffer from documented security and usability flaws that threaten the security of accounts. These flaws motivate us to design and study Geographical Security Questions (GeoSQ), a system for fallback authentication. GeoSQ is an Android application that utilizes autobiographical location data for fallback authentication. We performed security and usability analyses of GeoSQ through an in-person two-session lab study (n=36,18 pairs). Our results indicate that GeoSQ exceeds the security of its counterparts, while its usability (specifically login time) has room for improvement.