OpenRaiser/Intern-Atlas huggingface dataset:
Averaging Stochastic Gradient Descent on Riemannian Manifolds
Venue: arXiv cs.LG 2018
Year: 2018

We consider the minimization of a function defined on a Riemannian manifold $\mathcal{M}$ accessible only through unbiased estimates of its gradients. We develop a geometric framework to transform a sequence of slowly converging iterates generated from stochastic gradient descent (SGD) on $\mathcal{M}$ to an averaged iterate sequence with a robust and fast $O(1/n)$ convergence rate. We then present an application of our framework to geodesically-strongly-convex (and possibly Euclidean non-convex) problems. Finally, we demonstrate how these ideas apply to the case of streaming $k$-PCA, where we show how to accelerate the slow rate of the randomized power method (without requiring knowledge of the eigengap) into a robust algorithm achieving the optimal rate of convergence.
Antifragility for Intelligent Autonomous Systems
Venue: arXiv cs.AI 2018
Year: 2018

Antifragile systems grow measurably better in the presence of hazards. This is in contrast to fragile systems which break down in the presence of hazards, robust systems that tolerate hazards up to a certain degree, and resilient systems that -- like self-healing systems -- revert to their earlier expected behavior after a period of convalescence. The notion of antifragility was introduced by Taleb for economics systems, but its applicability has been illustrated in biological and engineering domains as well. In this paper, we propose an architecture that imparts antifragility to intelligent autonomous systems, specifically those that are goal-driven and based on AI-planning. We argue that this architecture allows the system to self-improve by uncovering new capabilities obtained either through the hazards themselves (opportunistic) or through deliberation (strategic). An AI planning-based case study of an autonomous wheeled robot is presented. We show that with the proposed architecture, the robot develops antifragile behaviour with respect to an oil spill hazard.
Gender Aware Spoken Language Translation Applied to English-Arabic
Venue: arXiv cs.CL 2018
Year: 2018

Spoken Language Translation (SLT) is becoming more widely used and becoming a communication tool that helps in crossing language barriers. One of the challenges of SLT is the translation from a language without gender agreement to a language with gender agreement such as English to Arabic. In this paper, we introduce an approach to tackle such limitation by enabling a Neural Machine Translation system to produce gender-aware translation. We show that NMT system can model the speaker/listener gender information to produce gender-aware translation. We propose a method to generate data used in adapting a NMT system to produce gender-aware. The proposed approach can achieve significant improvement of the translation quality by 2 BLEU points.
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
Venue: arXiv cs.AI 2018
Year: 2018

The task of answering natural language questions over RDF data has received wide interest in recent years, in particular in the context of the series of QALD benchmarks. The task consists of mapping a natural language question to an executable form, e.g. SPARQL, so that answers from a given KB can be extracted. So far, most systems proposed are i) monolingual and ii) rely on a set of hard-coded rules to interpret questions and map them into a SPARQL query. We present the first multilingual QALD pipeline that induces a model from training data for mapping a natural language question into logical form as probabilistic inference. In particular, our approach learns to map universal syntactic dependency representations to a language-independent logical form based on DUDES (Dependency-based Underspecified Discourse Representation Structures) that are then mapped to a SPARQL query as a deterministic second step. Our model builds on factor graphs that rely on features extracted from the dependency graph and corresponding semantic representations. We rely on approximate inference techniques, Markov Chain Monte Carlo methods in particular, as well as Sample Rank to update parameters using a ranking objective. Our focus lies on developing methods that overcome the lexical gap and present a novel combination of machine translation and word embedding approaches for this purpose. As a proof of concept for our approach, we evaluate our approach on the QALD-6 datasets for English, German & Spanish.
Digital Scientific Notations as a Human-Computer Interface in Computer-Aided Research
Venue: arXiv cs.HC 2018
Year: 2018

Most of today's scientific research relies on computers and software not only for administrational tasks, but also for processing scientific information. Examples of such computer-aided research are the analysis of experimental data or the simulation of phenomena based on theoretical models. With the rapid increase of computational power, scientific software has integrated more and more complex scientific knowledge in a black-box fashion. As a consequence, its users do not know, and don't even have a chance of finding out, which models or assumptions their computations are based on. The black-box nature of scientific software has thereby become a major cause of mistakes. The present work starts with an analysis of this situation from the point of view of human-computer interaction in scientific research. It identifies the key role of digital scientific notations at the human-computer interface, and describes a proof-of-concept implementation of such a digital scientific notation for scientific models formulated as mathematical equations.
An Energy Balance Based Method for Parameter Identification of a Free-Flying Robot Grasping An Unknown Object
Venue: arXiv cs.RO 2018
Year: 2018

The estimation of inertial parameters of a robotic system is crucial for better trajectory tracking performance, specially when model-based controllers are used for carrying out precise tasks. In this paper, we consider the scenario of grasping an object of unknown properties by a free-flyer space robot with limited actuation. The problem is to find the inertial parameters of the complete system after grasping has been performed. Excitation is provided in inertial space, and the excitation trajectories are found by optimization. Truncated Fourier series are used to represent the reference as well as tracked trajectory. An approach based on the energy balance between the actuation work and the rate of change of kinetic energy is introduced to calculate the number of harmonics in the Fourier series used to represent the executed trajectory, while trying to find a balance between accounting for saturation effects and keeping out noise. The effect of input saturation on parameter estimation is also studied. Simulation results using the Space CoBot free-flyer robot are presented to show the feasibility of the approach.
Screening for cancer using a learning Internet advertising system
Venue: arXiv cs.CY 2018
Year: 2018

Studies have shown that the traces people leave when browsing the internet may indicate the onset of diseases such as cancer. Here we show that the adaptive engines of advertising systems in conjunction with clinically verified questionnaires can be used to identify people who are suspected of having one of three types of solid tumor cancers. In the first study, 308 people were recruited through ads shown on the Bing search engine to complete a clinically verified risk questionnaire. A classifier trained to predict questionnaire response using only past queries on Bing reached an Area Under the Curve of 0.64 for all three cancer types, verifying that past searches could be used to identify people with suspected cancer. The second study was conducted using the Google ads system in the same configuration as in the first study. However, in this study the ads system was set to automatically learn to identify people with suspected cancer. A total of 70,586 people were shown the ads, and 6,484 clicked and were referred to complete the clinical questionnaires. People from countries with higher Internet access and lower life expectancy tended to click more on the ads. Over time the advertisement system learned to identify people who were likely to have symptoms consistent with suspected cancer, such that the percentage of people completing the questionnaires and found to have suspected cancer reached approximately 11\% at the end of the experiment. These results demonstrate the utility of using search engine queries to screen for possible cancer and the application of modern advertising systems to help identify people who are likely suffering from serious medical conditions. This is especially true in countries where medical services are less developed.
A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research
Venue: arXiv cs.CL 2018
Year: 2018

Having a quality annotated corpus is essential especially for applied research. Despite the recent focus of Web science community on researching about cyberbullying, the community dose not still have standard benchmarks. In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment.We crawled data from Twitter using our offensive lexicon. Then relied on the human judge to annotate the collected tweets w.r.t. the contextual types because using offensive words is not sufficient to reliably detect harassment. Our corpus consists of 25,000 annotated tweets in five contextual types. We are pleased to share this novel annotated corpus and the lexicon with the research community. The instruction to acquire the corpus has been published on the Git repository.
Tone Biased MMR Text Summarization
Venue: arXiv cs.IR 2018
Year: 2018

Text summarization is an interesting area for researchers to develop new techniques to provide human like summaries for vast amounts of information. Summarization techniques tend to focus on providing accurate representation of content, and often the tone of the content is ignored. Tone of the content sets a baseline for how a reader perceives the content. As such being able to generate summary with tone that is appropriate for the reader is important. In our work we implement Maximal Marginal Relevance [MMR] based multi-document text summarization and propose a naive model to change tone of the summarization by setting a bias to specific set of words and restricting other words in the summarization output. This bias towards a specified set of words produces a summary whose tone is same as tone of specified words.
Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model
Venue: arXiv cs.AI 2018
Year: 2018

Sociotechnical systems are complex systems, where nonlinear interaction among different players can obscure causal relationships. The absence of mechanisms to help us understand how to create a change in the system makes it hard to manage these systems. Influencing and shaping are social operators acting on sociotechnical systems to design a change. However, the two operators are usually discussed in an ad-hoc manner, without proper guiding models and metrics which assist in adopting these models successfully. Moreover, both social operators rely on accurate understanding of the concept of trust. Without such understanding, neither of these operators can create the required level to create a change in a desirable direction. In this paper, we define these concepts in a concise manner suitable for modelling the concepts and understanding their dynamics. We then introduce a model for influencing and shaping and use Computational Red Teaming principles to design and demonstrate how this model operates. We validate the results computationally through a simulation environment to show social influencing and shaping in an artificial society.
Enabling Multiple Access for Non-Line-of-Sight Light-to-Camera Communications
Venue: arXiv cs.ET 2018
Year: 2018

Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.
Robust Actor-Critic Contextual Bandit for Mobile Health (mHealth) Interventions
Venue: arXiv cs.LG 2018
Year: 2018

We consider the actor-critic contextual bandit for the mobile health (mHealth) intervention. State-of-the-art decision-making algorithms generally ignore the outliers in the dataset. In this paper, we propose a novel robust contextual bandit method for the mHealth. It can achieve the conflicting goal of reducing the influence of outliers while seeking for a similar solution compared with the state-of-the-art contextual bandit methods on the datasets without outliers. Such performance relies on two technologies: (1) the capped-$\ell_{2}$ norm; (2) a reliable method to set the thresholding hyper-parameter, which is inspired by one of the most fundamental techniques in the statistics. Although the model is non-convex and non-differentiable, we propose an effective reweighted algorithm and provide solid theoretical analyses. We prove that the proposed algorithm can find sufficiently decreasing points after each iteration and finally converges after a finite number of iterations. Extensive experiment results on two datasets demonstrate that our method can achieve almost identical results compared with state-of-the-art contextual bandit methods on the dataset without outliers, and significantly outperform those state-of-the-art methods on the badly noised dataset with outliers in a variety of parameter settings.
Network-Clustered Multi-Modal Bug Localization
Venue: arXiv cs.IR 2018
Year: 2018

Developers often spend much effort and resources to debug a program. To help the developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been devised. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). While both techniques ultimately generate a ranked list of program elements that likely contain a bug, they only consider one source of information--either bug reports or program spectra--which is not optimal. In light of this deficiency, this paper presents a new approach dubbed Network-clustered Multi-modal Bug Localization (NetML), which utilizes multi-modal information from both bug reports and program spectra to localize bugs. NetML facilitates an effective bug localization by carrying out a joint optimization of bug localization error and clustering of both bug reports and program elements (i.e., methods). The clustering is achieved through the incorporation of network Lasso regularization, which incentivizes the model parameters of similar bug reports and similar program elements to be close together. To estimate the model parameters of both bug reports and methods, NetML employs an adaptive learning procedure based on Newton method that updates the parameters on a per-feature basis. Extensive experiments on 355 real bugs from seven software systems have been conducted to benchmark NetML against various state-of-the-art localization methods. The results show that NetML surpasses the best-performing baseline by 31.82%, 22.35%, 19.72%, and 19.24%, in terms of the number of bugs successfully localized when a developer inspects the top 1, 5, and 10 methods and Mean Average Precision (MAP), respectively.
ReHAR: Robust and Efficient Human Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Designing a scheme that can achieve a good performance in predicting single person activities and group activities is a challenging task. In this paper, we propose a novel robust and efficient human activity recognition scheme called ReHAR, which can be used to handle single person activities and group activities prediction. First, we generate an optical flow image for each video frame. Then, both video frames and their corresponding optical flow images are fed into a Single Frame Representation Model to generate representations. Finally, an LSTM is used to pre- dict the final activities based on the generated representations. The whole model is trained end-to-end to allow meaningful representations to be generated for the final activity recognition. We evaluate ReHAR using two well-known datasets: the NCAA Basketball Dataset and the UCFSports Action Dataset. The experimental results show that the pro- posed ReHAR achieves a higher activity recognition accuracy with an order of magnitude shorter computation time compared to the state-of-the-art methods.
Time-sensitive Customer Churn Prediction based on PU Learning
Venue: arXiv cs.LG 2018
Year: 2018

With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the observation period, and start to train model as long as enough positive samples are collected, ignoring the absence of the negative examples. We conduct thoroughly experiments on real industry data from Alipay.com. The experimental results demonstrate that TCCP outperforms the rule-based models and the traditional supervised learning models.
Polar codes for empirical coordination over noisy channels with strictly causal encoding
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we propose a coding scheme based on polar codes for empirical coordination of autonomous devices. We consider a two-node network with a noisy link in which the input and output signals have to be coordinated with the source and the reconstruction. In the case of strictly causal encoding, we show that polar codes achieve the empirical coordination region, provided that a vanishing rate of common randomness is available.
Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing
Venue: arXiv cs.CV 2018
Year: 2018

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation.We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods.
Cooperative MIMO Precoding with Distributed CSI: A Hierarchical Approach
Venue: arXiv cs.IT 2018
Year: 2018

The problem of network multiple-input multiple-output precoding under distributed channel state information is a notoriously challenging question, for which optimal solutions with reasonable complexity remain elusive. In this context, we assess the value of hierarchical information exchange, whereby an order is established among the transmitters (TXs) in such a way that a given TX has access not only to its local channel estimate but also to the estimates available at the less informed TXs. Assuming regularized zero forcing (RZF) precoding at the TXs, we propose naive, locally robust, and globally robust suboptimal strategies for the joint precoding design. Numerical results show that hierarchical information exchange brings significant performance gains, with the locally and globally robust algorithms performing remarkably close to the optimal RZF strategy. Lastly, the cost of hierarchical information exchange relative to the cooperation gain is examined and the optimal tradeoff is numerically evaluated.
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Venue: arXiv cs.LG 2018
Year: 2018

We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous active learning strategies scalable for deep networks were mostly based on uncertain sample selection. In this work, we focus on examples lying close to the decision boundary. Based on theoretical works on margin theory for active learning, we know that such examples may help to considerably decrease the number of annotations. While measuring the exact distance to the decision boundaries is intractable, we propose to rely on adversarial examples. We do not consider anymore them as a threat instead we exploit the information they provide on the distribution of the input space in order to approximate the distance to decision boundaries. We demonstrate empirically that adversarial active queries yield faster convergence of CNNs trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
CCP: Conflicts Check Protocol for Bitcoin Block Security
Venue: arXiv cs.CR 2018
Year: 2018

In this work, we present our early stage results on a Conflicts Check Protocol (CCP) that enables preventing potential attacks on bitcoin system. Based on the observation and discovery of a common symptom that many attacks may generate, CCP refines the current bitcoin systems by proposing a novel arbitration mechanism that is capable to determine the approval or abandon of certain transactions involved in confliction. This work examines the security issue of bitcoin from a new perspective, which may extend to a larger scope of attack analysis and prevention
High-Dimensional Vector Semantics
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we explore the "vector semantics" problem from the perspective of "almost orthogonal" property of high-dimensional random vectors. We show that this intriguing property can be used to "memorize" random vectors by simply adding them, and we provide an efficient probabilistic solution to the set membership problem. Also, we discuss several applications to word context vector embeddings, document sentences similarity, and spam filtering.