OpenRaiser/Intern-Atlas huggingface dataset:
Dual-Domain Fusion Convolutional Neural Network for Contrast Enhancement Forensics
Venue: arXiv cs.MM 2019
Year: 2019

Contrast enhancement (CE) forensics techniques have always been of great interest for image forensics community, as they can be an effective tool for recovering image history and identifying tampered images. Although several CE forensic algorithms have been proposed, their accuracy and robustness against some kinds of processing are still unsatisfactory. In order to attenuate such deficiency, in this paper we propose a new framework based on dual-domain fusion convolutional neural network to fuse the features of pixel and histogram domains for CE forensics. Specifically, we first present a pixel-domain convolutional neural network (P-CNN) to automatically capture the patterns of contrast-enhanced images in the pixel domain. Then, we present a histogram-domain convolutional neural network (H-CNN) to extract the features in the histogram domain. The feature representations of pixel and histogram domains are fused and fed into two fully connected layers for the classification of contrast-enhanced images. Experimental results show that the proposed method achieve better performance and is robust against pre-JPEG compression and anti-forensics attacks. In addition, a strategy for performance improvement of CNN-based forensics is explored, which could provide guidance for the design of CNN-based forensics tools.
Explainable Authorship Verification in Social Media via Attention-based Similarity Learning
Venue: arXiv cs.CL 2019
Year: 2019

Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not. The analysis is traditionally performed by experts who consider linguistic features, which include spelling mistakes, grammatical inconsistencies, and stylistics for example. Machine learning algorithms, on the other hand, can be trained to accomplish the same, but have traditionally relied on so-called stylometric features. The disadvantage of such features is that their reliability is greatly diminished for short and topically varied social media texts. In this interdisciplinary work, we propose a substantial extension of a recently published hierarchical Siamese neural network approach, with which it is feasible to learn neural features and to visualize the decision-making process. For this purpose, a new large-scale corpus of short Amazon reviews for text comparison research is compiled and we show that the Siamese network topologies outperform state-of-the-art approaches that were built up on stylometric features. Our linguistic analysis of the internal attention weights of the network shows that the proposed method is indeed able to latch on to some traditional linguistic categories.
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces
Venue: arXiv cs.LG 2019
Year: 2019

We present an efficient algorithm for model-free episodic reinforcement learning on large (potentially continuous) state-action spaces. Our algorithm is based on a novel $Q$-learning policy with adaptive data-driven discretization. The central idea is to maintain a finer partition of the state-action space in regions which are frequently visited in historical trajectories, and have higher payoff estimates. We demonstrate how our adaptive partitions take advantage of the shape of the optimal $Q$-function and the joint space, without sacrificing the worst-case performance. In particular, we recover the regret guarantees of prior algorithms for continuous state-action spaces, which additionally require either an optimal discretization as input, and/or access to a simulation oracle. Moreover, experiments demonstrate how our algorithm automatically adapts to the underlying structure of the problem, resulting in much better performance compared both to heuristics and $Q$-learning with uniform discretization.
Online Learning in Planar Pushing with Combined Prediction Model
Venue: arXiv cs.RO 2019
Year: 2019

Pushing is a useful robotic capability for positioning and reorienting objects. The ability to accurately predict the effect of pushes can enable efficient trajectory planning and complicated object manipulation. Physical prediction models for planar pushing have long been established, but their assumptions and requirements usually don't hold in most practical settings. Data-driven approaches can provide accurate predictions for offline data, but they often have generalizability issues. In this paper, we propose a combined prediction model and an online learning framework for planar push prediction. The combined model consists of a neural network module and analytical components with a low-dimensional parameter. We train the neural network offline using pre-collected pushing data. In online situations, the low-dimensional analytical parameter is learned directly from online pushes to quickly adapt to the new environments. We test our combined model and learning framework on real pushing experiments. Our experimental results show that our model is able to quickly adapt to new environments while achieving similar final prediction performance as that of pure neural network models.
Map-Predictive Motion Planning in Unknown Environments
Venue: arXiv cs.RO 2019
Year: 2019

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient autonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot's observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot's position to the goal without requiring frontier selection. We demonstrate that our map-predictive motion planning strategy yields a substantial improvement in trajectory time over a naive frontier pursuit method and demonstrates similar performance to methods using more sophisticated frontier selection heuristics with significantly shorter computation time.
Embedded discrepancy operators in reduced models of interacting species
Venue: arXiv cs.CE 2019
Year: 2019

In many applications of interacting systems, we are only interested in the dynamic behavior of a subset of all possible active species. For example, this is true in combustion models (many transient chemical species are not of interest in a given reaction) and in epidemiological models (only certain critical populations are truly consequential). Thus it is common to use greatly reduced models, in which only the interactions among the species of interest are retained. However, reduction introduces a model error, or discrepancy, which typically is not well characterized. In this work, we explore the use of an embedded and statistically calibrated discrepancy operator to represent model error. The operator is embedded within the differential equations of the model, which allows the action of the operator to be interpretable. Moreover, it is constrained by available physical information, and calibrated over many scenarios. These qualities of the discrepancy model---interpretability, physical-consistency, and robustness to different scenarios---are intended to support reliable predictions under extrapolative conditions.
HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Venue: arXiv cs.CL 2019
Year: 2019

Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a "seed" taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.
Toward 3D Object Reconstruction from Stereo Images
Venue: arXiv cs.CV 2019
Year: 2019

Inferring the 3D shape of an object from an RGB image has shown impressive results, however, existing methods rely primarily on recognizing the most similar 3D model from the training set to solve the problem. These methods suffer from poor generalization and may lead to low-quality reconstructions for unseen objects. Nowadays, stereo cameras are pervasive in emerging devices such as dual-lens smartphones and robots, which enables the use of the two-view nature of stereo images to explore the 3D structure and thus improve the reconstruction performance. In this paper, we propose a new deep learning framework for reconstructing the 3D shape of an object from a pair of stereo images, which reasons about the 3D structure of the object by taking bidirectional disparities and feature correspondences between the two views into account. Besides, we present a large-scale synthetic benchmarking dataset, namely StereoShapeNet, containing 1,052,976 pairs of stereo images rendered from ShapeNet along with the corresponding bidirectional depth and disparity maps. Experimental results on the StereoShapeNet benchmark demonstrate that the proposed framework outperforms the state-of-the-art methods.
Dynamic Resource Allocation and Activity Management for Energy Efficiency and Fairness in Heterogeneous Networks
Venue: arXiv cs.NI 2019
Year: 2019

Higher energy consumption of Heterogeneous Networks (HetNet), compared to Macro Only Networks (MONET), raises a great concern about the energy efficiency of HetNets. In this work we study a dynamic activation strategy, which changes the state of small cells between Active and Idle according to the dynamically changing user traffic, in order to increase the energy efficiency of HetNets. Moreover, we incorporate dynamic inter-tier bandwidth allocation to our model. The proposed Dynamic Bandwidth Allocation and Dynamic Activation (DBADA) strategy is applied in cell-edge deployment of small cells, where HotSpot regions are located far from the master base station. Our objective is to maximize the sum utility of the network with minimum energy consumption. To ensure proportional fairness among users, we used logarithmic utility function. To evaluate the performance of the proposed strategy, the median, 10-percentile and the sum of users' data rates and the network energy consumption are evaluated by simulation. Our simulation results shows that the DBADA strategy improves the energy consumed per unit of users' data rate by up to $25\%$. It also achieves lower energy consumption by at least $25\%$, compared to always active scenario for small cells.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Venue: arXiv cs.CL 2019
Year: 2019

Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-review samples, would perform better than our proposed baselines, at answering specific, subjective product-related queries using reviews. We validate this hypothesis using a small gold standard dataset of question-review pairs evaluated by human experts, significantly surpassing our chosen baselines. Moreover, our approach, using no labeled question-review sentence pair data for training, gives performance at par with another method utilizing labeled question-review samples for the same task.
$b$-Bit Sketch Trie: Scalable Similarity Search on Integer Sketches
Venue: arXiv cs.LG 2019
Year: 2019

Recently, randomly mapping vectorial data to strings of discrete symbols (i.e., sketches) for fast and space-efficient similarity searches has become popular. Such random mapping is called similarity-preserving hashing and approximates a similarity metric by using the Hamming distance. Although many efficient similarity searches have been proposed, most of them are designed for binary sketches. Similarity searches on integer sketches are in their infancy. In this paper, we present a novel space-efficient trie named $b$-bit sketch trie on integer sketches for scalable similarity searches by leveraging the idea behind succinct data structures (i.e., space-efficient data structures while supporting various data operations in the compressed format) and a favorable property of integer sketches as fixed-length strings. Our experimental results obtained using real-world datasets show that a trie-based index is built from integer sketches and efficiently performs similarity searches on the index by pruning useless portions of the search space, which greatly improves the search time and space-efficiency of the similarity search. The experimental results show that our similarity search is at most one order of magnitude faster than state-of-the-art similarity searches. Besides, our method needs only 10 GiB of memory on a billion-scale database, while state-of-the-art similarity searches need 29 GiB of memory.
Weighted Edge Sampling for Static Graphs
Venue: arXiv cs.DS 2019
Year: 2019

Graph Sampling provides an efficient yet inexpensive solution for analyzing large graphs. While extracting small representative subgraphs from large graphs, the challenge is to capture the properties of the original graph. Several sampling algorithms have been proposed in previous studies, but they lack in extracting good samples. In this paper, we propose a new sampling method called Weighted Edge Sampling. In this method, we give equal weight to all the edges in the beginning. During the sampling process, we sample an edge with the probability proportional to its weight. When an edge is sampled, we increase the weight of its neighboring edges and this increases their probability to be sampled. Our method extracts the neighborhood of a sampled edge more efficiently than previous approaches. We evaluate the efficacy of our sampling approach empirically using several real-world data sets and compare it with some of the previous approaches. We find that our method produces samples that better match the original graphs. We also calculate the Root Mean Square Error and Kolmogorov Smirnov distance to compare the results quantitatively.
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Venue: arXiv cs.CV 2019
Year: 2019

Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge about the addressed problem, thus, understanding what the model has learned is an open research topic. In this paper, we rely on the unfolding of an iterative algorithm for sparse approximation with side information, and design a deep learning architecture for multimodal image super-resolution that incorporates sparse priors and effectively utilizes information from another image modality. We develop two deep models performing reconstruction of a high-resolution image of a target image modality from its low-resolution variant with the aid of a high-resolution image from a second modality. We apply the proposed models to super-resolve near-infrared images using as side information high-resolution RGB\ images. Experimental results demonstrate the superior performance of the proposed models against state-of-the-art methods including unimodal and multimodal approaches.
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System
Venue: arXiv cs.CL 2019
Year: 2019

Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous model compression methods usually suffer from information loss during the model compression procedure, leading to inferior models compared with the original one. To tackle this challenge, we propose a Two-stage Multi-teacher Knowledge Distillation (TMKD for short) method for web Question Answering system. We first develop a general Q\&A distillation task for student model pre-training, and further fine-tune this pre-trained student model with multi-teacher knowledge distillation on downstream tasks (like Web Q\&A task, MNLI, SNLI, RTE tasks from GLUE), which effectively reduces the overfitting bias in individual teacher models, and transfers more general knowledge to the student model. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.
Implicit Context-aware Learning and Discovery for Streaming Data Analytics
Venue: arXiv cs.LG 2019
Year: 2019

The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-crafting additional features to describe a context. While offline training enjoys the privilege of learning reliable models based on already-defined contextual features, online training for streaming data may be more challenging -- the data is streamed through time, and the underlying context during a data generation process may change. Furthermore, the problem is exacerbated when the number of possible context is not known. In this study, we propose an online-learning algorithm involving the use of a neural network-based autoencoder to identify contextual changes during training, then compares the currently-inferred context to a knowledge base of learned contexts as training advances. Results show that classifier-training benefits from the automatically discovered contexts which demonstrates quicker learning convergence during contextual changes compared to current methods.
Fast Local Planning and Mapping in Unknown Off-Road Terrain
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we present a fast, on-line mapping and planning solution for operation in unknown, off-road, environments. We combine obstacle detection along with a terrain gradient map to make simple and adaptable cost map. This map can be created and updated at 10 Hz. An A* planner finds optimal paths over the map. Finally, we take multiple samples over the control input space and do a kinematic forward simulation to generated feasible trajectories. Then the most optimal trajectory, as determined by the cost map and proximity to A* path, is chosen and sent to the controller. Our method allows real time operation at rates of 30 Hz. We demonstrate the efficiency of our method in various off-road terrain at high speed.
Normal Estimation for 3D Point Clouds via Local Plane Constraint and Multi-scale Selection
Venue: arXiv cs.GR 2019
Year: 2019

In this paper, we propose a normal estimation method for unstructured 3D point clouds. In this method, a feature constraint mechanism called Local Plane Features Constraint (LPFC) is used and then a multi-scale selection strategy is introduced. The LPEC can be used in a single-scale point network architecture for a more stable normal estimation of the unstructured 3D point clouds. In particular, it can partly overcome the influence of noise on a large sampling scale compared to the other methods which only use regression loss for normal estimation. For more details, a subnetwork is built after point-wise features extracted layers of the network and it gives more constraints to each point of the local patch via a binary classifier in the end. Then we use multi-task optimization to train the normal estimation and local plane classification tasks simultaneously.Also, to integrate the advantages of multi-scale results, a scale selection strategy is adopted, which is a data-driven approach for selecting the optimal scale around each point and encourages subnetwork specialization. Specifically, we employed a subnetwork called Scale Estimation Network to extract scale weight information from multi-scale features. More analysis is given about the relations between noise levels, local boundary, and scales in the experiment. These relationships can be a better guide to choosing particular scales for a particular model. Besides, the experimental result shows that our network can distinguish the points on the fitting plane accurately and this can be used to guide the normal estimation and our multi-scale method can improve the results well. Compared to some state-of-the-art surface normal estimators, our method is robust to noise and can achieve competitive results.