OpenRaiser/Intern-Atlas huggingface dataset:
Jointly Trained Image and Video Generation using Residual Vectors
Venue: arXiv cs.LG 2019
Year: 2019

In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real images and interpolate over images to generate videos. The proposed approach models the variations in representations using residual vectors encoding the change at each time step over a summary vector for the entire video. We utilize the technique to jointly train an image generation model with a fixed prior along with a video generation model lacking constraints such as disentanglement. The joint training enables the image generator to exploit temporal information while the video generation model learns to flexibly share information across frames. Moreover, experimental results verify our approach's compatibility with pre-training on videos or images and training on datasets containing a mixture of both. A comprehensive set of quantitative and qualitative evaluations reveal the improvements in sample quality and diversity over both video generation and image generation baselines. We further demonstrate the technique's capabilities of exploiting similarity in features across frames by applying it to a model based on decomposing the video into motion and content. The proposed model allows minor variations in content across frames while maintaining the temporal dependence through latent vectors encoding the pose or motion features.
Visualizing and Analyzing Entity Activity on the Bitcoin Network
Venue: arXiv cs.HC 2019
Year: 2019

We present BitConduite, a visual analytics tool for explorative analysis of financial activity within the Bitcoin network. Bitcoin is the largest cryptocurrency worldwide and a phenomenon that challenges the underpinnings of traditional financial systems - its users can send money pseudo-anonymously while circumventing traditional banking systems. Yet, despite the fact that all financial transactions in Bitcoin are available in an openly accessible online ledger - the blockchain - not much is known about how different types of actors in the network (we call them entities) actually use Bitcoin. BitConduite offers an entity-centered view on transactions, making the data accessible to non-technical experts through a guided workflow for classification of entities according to several activity metrics. Other novelties are the possibility to cluster entities by similarity and exploration of transaction data at different scales, from large groups of entities down to a single entity and the associated transactions. Two use cases illustrate the workflow of the system and its analytic power. We report on feedback regarding the approach and the the software tool gathered during a workshop with domain experts, and we discuss the potential of the approach based on our findings.
Competitive Contagion with Sparse Seeding
Venue: arXiv cs.SI 2019
Year: 2019

This paper studies a strategic model of marketing and product diffusion in social networks. We consider two firms offering substitutable products which can improve their market share by seeding the key individuals in the market. Consumers update their consumption level for each of the two products as the best response to the consumption of their neighbors in the previous period. This results in linear update dynamics for the product consumption. Each consumer receives externality from the consumption of each neighbor where the strength of the externality is higher for consumption of the products of the same firm. We represent the above setting as a duopoly game between the firms and introduce a novel framework that allows for sparse seeding to emerge as an equilibrium strategy. We then study the effect of the network structure on the optimal seeding strategies and the extent to which the strategies can be sparsified. In particular, we derive conditions under which near Nash equilibrium strategies can asymptotically lead to sparse seeding in large populations. The results are illustrated using a core-periphery network.
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
Venue: arXiv cs.LG 2019
Year: 2019

In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cost in money, computational time, or human time, depending on the problem domain. This leads us to the problem of choosing which features to use at the prediction time. The chosen features should increase the prediction accuracy for a low cost, but determining which features will do that is challenging. The choice should take into account the previously acquired feature values as well as the feature costs. This paper proposes a novel approach to address this problem. The proposed approach chooses the most useful features adaptively based on how relevant they are for the prediction task as well as what the corresponding feature costs are. Our approach uses a generic neural network architecture, which is suitable for a wide range of problems. We evaluate our approach on three cost-sensitive datasets, including Yahoo! Learning to Rank Competition dataset as well as two health datasets. We show that our approach achieves high accuracy with a lower cost than the current state-of-the-art approaches.
Enumeration of Minimum Hamming Weight Polar Codewords with Sublinear Complexity
Venue: arXiv cs.IT 2019
Year: 2019

Polar code, with explicit construction and recursive structure, is the latest breakthrough in channel coding field for its low-complexity and theoretically capacity-achieving property. Since polar codes can approach the maximum likelihood performance under successive cancellation list decoding (SCLD), its decoding performance can be evaluated by Bonferroni-type bounds (e.g., union bound) in which the Hamming weight spectrum will be used. Especially, the polar codewords with minimum Hamming weight (PC-MHW) are the most important item in that bound because they make major contributions to the decoding error pattern particularly at high signal-to-noise-ratio. In this work, we propose an efficient strategy for enumerating the PC-MHW and its number. By reviewing the inherent reason that PC-MHW can be generated by SCLD, we obtain some common features of PC-MHW captured by SCLD. Using these features, we introduce a concept of zero-capacity bit-channel to obtain a tight upper bound for the number of PC-MHW, whose computing complexity is sublinear with code length. Furthermore, we prove that the proposed upper bound is really the exact number of PC-MHW in most cases. Guided by the bound and its theoretical analysis, we devise an efficient SCLD-based method to enumerate PC-MHW, which requires less than half of the list size compared with the existing methods.
P-CapsNets: a General Form of Convolutional Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST\&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99\% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.
Iterative and Adaptive Sampling with Spatial Attention for Black-Box Model Explanations
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks have achieved great success in many real-world applications, yet it remains unclear and difficult to explain their decision-making process to an end-user. In this paper, we address the explainable AI problem for deep neural networks with our proposed framework, named IASSA, which generates an importance map indicating how salient each pixel is for the model's prediction with an iterative and adaptive sampling module. We employ an affinity matrix calculated on multi-level deep learning features to explore long-range pixel-to-pixel correlation, which can shift the saliency values guided by our long-range and parameter-free spatial attention. Extensive experiments on the MS-COCO dataset show that our proposed approach matches or exceeds the performance of state-of-the-art black-box explanation methods.
Feature engineering workflow for activity recognition from synchronized inertial measurement units
Venue: arXiv cs.LG 2019
Year: 2019

The ubiquitous availability of wearable sensors is responsible for driving the Internet-of-Things but is also making an impact on sport sciences and precision medicine. While human activity recognition from smartphone data or other types of inertial measurement units (IMU) has evolved to one of the most prominent daily life examples of machine learning, the underlying process of time-series feature engineering still seems to be time-consuming. This lengthy process inhibits the development of IMU-based machine learning applications in sport science and precision medicine. This contribution discusses a feature engineering workflow, which automates the extraction of time-series feature on based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis tests) to identify statistically significant features from synchronized IMU sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main steps: time-series engineering, automated time-series feature extraction, optimized feature extraction, fitting of a specialized classifier, and deployment of optimized machine learning pipeline. The workflow is discussed for the case of a user-specific running-walking classification, and the generalization to a multi-user multi-activity classification is demonstrated.
Generating summaries tailored to target characteristics
Venue: arXiv cs.CL 2019
Year: 2019

Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries. While there have been attempts to incorporate a few handpicked characteristics such as length or entities, a holistic view around these preferences is missing and crucial insights on why certain characteristics should be incorporated in a specific manner are absent. With this objective, we provide a categorization around these characteristics relevant to the task of text summarization: one, focusing on what content needs to be generated and second, focusing on the stylistic aspects of the output summaries. We use our insights to provide guidelines on appropriate methods to incorporate various classes characteristics in sequence-to-sequence summarization framework. Our experiments with incorporating topics, readability and simplicity indicate the viability of the proposed prescriptions
Multi-display Visual Analysis: Model, Interface, and Layout Computation
Venue: arXiv cs.GR 2019
Year: 2019

Modern display environments offer great potential for involving multiple users in presentations, discussions, and data analysis sessions. By showing multiple views on multiple displays, information exchange can be improved, several perspectives on the data can be combined, and different analysis strategies can be pursued. In this report, we describe concepts to support display composition, information distribution, and analysis coordination for visual data analysis in multi-display environments. In particular, a basic model for layout modeling is introduced, a graphical interface for interactive generation of the model is presented, and a layout mechanism is described that arranges multiple views on multiple displays automatically. Furthermore, approaches to meta-analysis will be discussed. The developed approaches are demonstrated in a use case that focuses on parameter space analysis for the segmentation of time series data.
Towards an automatic recognition of mixed languages: The Ukrainian-Russian hybrid language Surzhyk
Venue: arXiv cs.CL 2019
Year: 2019

Language interference is common in today's multilingual societies where more languages are being in contact and as a global final result leads to the creation of hybrid languages. These, together with doubts on their right to be officially recognised made emerge in the area of computational linguistics the problem of their automatic identification and further elaboration. In this paper, we propose a first attempt to identify the elements of a Ukrainian-Russian hybrid language, Surzhyk, through the adoption of the example-based rules created with the instruments of programming language R. Our example-based study consists of: 1) analysis of spoken samples of Surzhyk registered by Del Gaudio (2010) in Kyiv area and creation of the written corpus; 2) production of specific rules on the identification of Surzhyk patterns and their implementation; 3) testing the code and analysing the effectiveness.
Comparison of Classification Methods for Very High-Dimensional Data in Sparse Random Projection Representation
Venue: arXiv cs.LG 2019
Year: 2019

The big data trend has inspired feature-driven learning tasks, which cannot be handled by conventional machine learning models. Unstructured data produces very large binary matrices with millions of columns when converted to vector form. However, such data is often sparse, and hence can be manageable through the use of sparse random projections. This work studies efficient non-iterative and iterative methods suitable for such data, evaluating the results on two representative machine learning tasks with millions of samples and features. An efficient Jaccard kernel is introduced as an alternative to the sparse random projection. Findings indicate that non-iterative methods can find larger, more accurate models than iterative methods in different application scenarios.
One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block
Venue: arXiv cs.CV 2019
Year: 2019

Recent deep learning based image inpainting methods which utilize contextual information and two-stage architecture have exhibited remarkable performance. However, the two-stage architecture is time-consuming, the contextual information lack high-level semantics and ignores both the semantic relevance and distance information of hole's feature patches, these limitations result in blurry textures and distorted structures of final result. Motivated by these observations, we propose a new deep generative model-based approach, which trains a shared network twice with different targets and utilizes a single network during the testing phase, so that we can effectively save inference time. Specifically, the targets of two training steps are structure reconstruction and texture generation respectively. During the second training, we first propose a Pyramid Filling Block (PF-block) to utilize the high-level features that the hole regions has been filled to guide the filling process of low-level features progressively, the missing content can be filled from deep to shallow in a pyramid fashion. Then, inspired by the classical bilateral filter [30], we propose the Bilateral Attention layer (BA-layer) to optimize filled feature map, which synthesizes feature patches at each position by computing weighted sums of the surrounding feature patches, these weights are derived by considering both distance and value relationships between feature patches, thus making the visually plausible inpainting results. Finally, experiments on multiple publicly available datasets show the superior performance of our approach.
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

The effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario due to the quantity of algorithms available, and because of their varying performances. Furthermore, it is not possible to choose one single algorithm that will work optimally for all recommendation requests. We apply meta-learning to this problem of algorithm selection for scholarly article recommendation. We train a random forest, gradient boosting machine, and generalized linear model, to predict a best-algorithm from a pool of content similarity-based algorithms. We evaluate our approach on an offline dataset for scholarly article recommendation and attempt to predict the best algorithm per-instance. The best meta-learning model achieved an average increase in F1 of 88% when compared to the average F1 of all base-algorithms (F1; 0.0708 vs 0.0376) and was significantly able to correctly select each base-algorithm (Paired t-test; p < 0.1). The meta-learner had a 3% higher F1 when compared to the single-best base-algorithm (F1; 0.0739 vs 0.0717). We further perform an online evaluation of our approach, conducting an A/B test through our recommender-as-a-service platform Mr. DLib. We deliver 148K recommendations to users between January and March 2019. User engagement was significantly increased for recommendations generated using our meta-learning approach when compared to a random selection of algorithm (Click-through rate (CTR); 0.51% vs. 0.44%, Chi-Squared test; p < 0.1), however our approach did not produce a higher CTR than the best algorithm alone (CTR; MoreLikeThis (Title): 0.58%).
Formula size games for modal logic and $μ$-calculus
Venue: arXiv cs.LO 2019
Year: 2019

We propose a new version of formula size game for modal logic. The game characterizes the equivalence of pointed Kripke-models up to formulas of given numbers of modal operators and binary connectives. Our game is similar to the well-known Adler-Immerman game. However, due to a crucial difference in the definition of positions of the game, its winning condition is simpler, and the second player does not have a trivial optimal strategy. Thus, unlike the Adler-Immerman game, our game is a genuine two-person game. We illustrate the use of the game by proving a non-elementary succinctness gap between bisimulation invariant first-order logic $\mathrm{FO}$ and (basic) modal logic $\mathrm{ML}$. We also present a version of the game for the modal $μ$-calculus $\mathrm{L}_μ$ and show that $\mathrm{FO}$ is also non-elementarily more succinct than $\mathrm{L}_μ$.
Effective Integration of Symbolic and Connectionist Approaches through a Hybrid Representation
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we present our position for a neuralsymbolic integration strategy, arguing in favor of a hybrid representation to promote an effective integration. Such description differs from others fundamentally, since its entities aim at representing AI models in general, allowing to describe both nonsymbolic and symbolic knowledge, the integration between them and their corresponding processors. Moreover, the entities also support representing workflows, leveraging traceability to keep track of every change applied to models and their related entities (e.g., data or concepts) throughout the lifecycle of the models.
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Venue: arXiv cs.LG 2019
Year: 2019

Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capable edge devices with limited computation and storage requires streamlined methodologies that can efficiently satisfy the constraints of different devices. In contrast, existing methods often rely on heuristic and manual adjustments to maintain accuracy, support only coarse compression policies, or target specific device constraints that limit their applicability. We address these limitations by proposing the TOlerance-based COmpression (TOCO) framework. TOCO uses an in-depth analysis of the model, to maintain the accuracy, in an active learning system. The results of the analysis are tolerances that can be used to perform compression in a fine-grained manner. Finally, by decoupling compression from the tolerance analysis, TOCO allows flexibility to changes in the hardware.
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

Up to the present, an enormous number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. As a consequence, identifying the same materials from spatially different scenes or positions can be difficult. In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral datasets (Houston2013 and Houston2018) demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. The codes will be available from the website: https://sites.google.com/view/danfeng-hong/data-code.
Real-time 2019 Portuguese Parliament Election Results Dataset
Venue: arXiv cs.SI 2019
Year: 2019

This paper presents a data set describing the evolution of results in the Portuguese Parliamentary Elections of October 6$^{th}$ 2019. The data spans a time interval of 4 hours and 25 minutes, in intervals of 5 minutes, concerning the results of the 27 parties involved in the electoral event. The data set is tailored for predictive modelling tasks, mostly focused on numerical forecasting tasks. Regardless, it allows for other tasks such as ordinal regression or learn-to-rank.
Rumor Detection and Classification for Twitter Data
Venue: arXiv cs.SI 2019
Year: 2019

With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.