OpenRaiser/Intern-Atlas huggingface dataset:
Rumour Detection via News Propagation Dynamics and User Representation Learning
Venue: arXiv cs.SI 2019
Year: 2019

Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus becomes important to early detect them. Many methods have been introduced to detect rumours using the content or the social context of news. However, most existing methods ignore or do not explore effectively the propagation pattern of news in social media, including the sequence of interactions of social media users with news across time. In this work, we propose a novel method for rumour detection based on deep learning. Our method leverages the propagation process of the news by learning the users' representation and the temporal interrelation of users' responses. Experiments conducted on Twitter and Weibo datasets demonstrate the state-of-the-art performance of the proposed method.
Forest Representation Learning Guided by Margin Distribution
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we reformulate the forest representation learning approach as an additive model which boosts the augmented feature instead of the prediction. We substantially improve the upper bound of generalization gap from $\mathcal{O}(\sqrt\frac{\ln m}{m})$ to $\mathcal{O}(\frac{\ln m}{m})$, while $λ$ - the margin ratio between the margin standard deviation and the margin mean is small enough. This tighter upper bound inspires us to optimize the margin distribution ratio $λ$. Therefore, we design the margin distribution reweighting approach (mdDF) to achieve small ratio $λ$ by boosting the augmented feature. Experiments and visualizations confirm the effectiveness of the approach in terms of performance and representation learning ability. This study offers a novel understanding of the cascaded deep forest from the margin-theory perspective and further uses the mdDF approach to guide the layer-by-layer forest representation learning.
Unsupervised Learning through Temporal Smoothing and Entropy Maximization
Venue: arXiv cs.LG 2019
Year: 2019

This paper proposes a method for machine learning from unlabeled data in the form of a time-series. The mapping that is learned is shown to extract slowly evolving information that would be useful for control applications, while efficiently filtering out unwanted, higher-frequency noise. The method consists of training a feedforward artificial neural network with backpropagation using two opposing objectives. The first of these is to minimize the squared changes in activations between time steps of each unit in the network. This "temporal smoothing" has the effect of correlating inputs that occur close in time with outputs that are close in the L2-norm. The second objective is to maximize the log determinant of the covariance matrix of activations in each layer of the network. This objective ensures that information from each layer is passed through to the next. This second objective acts as a balance to the first, which on its own would result in a network with all input weights equal to zero.
Locality-Sensitive Sketching for Resilient Network Flow Monitoring
Venue: arXiv cs.NI 2019
Year: 2019

Network monitoring is vital in modern clouds and data center networks for traffic engineering, network diagnosis, network intrusion detection, which need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions. In addition, deploying the sketch involves fine-grained performance control and instrumentation. This paper presents a locality-sensitive sketch (LSS) to be resilient to hash collisions. LSS proactively minimizes the estimation error due to hash collisions with an autoencoder based optimization model, and reduces the estimation variance by keeping similar network flows to the same bucket array. To illustrate the feasibility of the sketch, we develop a disaggregated monitoring application that supports non-intrusive sketching deployment and native network-wide analysis. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over $10^3$ to $10^5$ times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10\% to 0.1\%.
Blitz-starting QUIC Connections
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we revisit the idea to remove Slow Start from congestion control. To do so, we build upon the newly gained freedom of transport protocol extendability offered by QUIC to hint bandwidth estimates from a typical web client to a server. Using this bandwidth estimate, we bootstrap congestion windows of new connections to quickly utilize available bandwidth. This custom flow initialization removes the common early exit of Slow Start and thus fuels short flow fairness with long-running connections. Our results indicate that we can drastically reduce flow completion time accepting some losses and thereby an inflated transmission volume. For example, for a typical DSL client, loading a 2 MB YouTube video chunk is accelerated by nearly 2x. In the worst case, we find an inflation of the transfer volume by 12% due to losses.
Unsupervised Domain Adaptation using Generative Adversarial Networks for Semantic Segmentation of Aerial Images
Venue: arXiv cs.CV 2019
Year: 2019

Segmenting aerial images is being of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pre-trained segmentation model to survey a new city that is not included in the training set significantly decreases the accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We design an algorithm that reduces the domain shift impact using Generative Adversarial Networks (GANs). In the experiments, we test the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves the overall accuracy from 35% to 52% when passing from Potsdam domain (considered as source domain) to Vaihingen domain (considered as target domain). In addition, the method allows recovering efficiently the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.
Evaluating the Stability of Recurrent Neural Models during Training with Eigenvalue Spectra Analysis
Venue: arXiv cs.NE 2019
Year: 2019

We analyze the stability of recurrent networks, specifically, reservoir computing models during training by evaluating the eigenvalue spectra of the reservoir dynamics. To circumvent the instability arising in examining a closed loop reservoir system with feedback, we propose to break the closed loop system. Essentially, we unroll the reservoir dynamics over time while incorporating the feedback effects that preserve the overall temporal integrity of the system. We evaluate our methodology for fixed point and time varying targets with least squares regression and FORCE training, respectively. Our analysis establishes eigenvalue spectra (which is, shrinking of spectral circle as training progresses) as a valid and effective metric to gauge the convergence of training as well as the convergence of the chaotic activity of the reservoir toward stable states.
Interdisciplinary Relationships Between Biological and Physical Sciences
Venue: arXiv cs.DL 2019
Year: 2019

Several interdisciplinary areas have appeared at the interface between biological and physical sciences. In this work, we suggest a complex network-based methodology for analyzing the interrelationships between some of these interdisciplinary areas, including Bioinformatics, Computational Biology, Biochemistry, among others. This approach has been applied over respective data derived from Wikipedia. Related reviews from the scientific literature are also considered as a reference, yielding a respective bipartite hypergraph which can be used to gain insights about the interrelationships underlying the considered interdisciplinary areas. Several interesting results are obtained, including greater interconnection between the considered interdisciplinary areas with biological than with physical sciences. A good agreement was also found between the network obtained from Wikipedia and the interrelationships revealed by the literature reviews. At the same time, the former network was found to exhibit more intricate relationships than in the hypergraph derived from the literature review.
Learning Embeddings into Entropic Wasserstein Spaces
Venue: arXiv cs.LG 2019
Year: 2019

Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We exploit this flexibility by learning an embedding that captures semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of our learned Wasserstein embeddings, showing that they can embed a wide variety of metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: We can visualize the high-dimensional embedding directly, since it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques like t-SNE for visualization.
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval
Venue: arXiv cs.AI 2019
Year: 2019

Image retrieval utilizes image descriptors to retrieve the most similar images to a given query image. Convolutional neural network (CNN) is becoming the dominant approach to extract image descriptors for image retrieval. For low-power hardware implementation of image retrieval, the drawback of CNN-based feature descriptor is that it requires hundreds of megabytes of storage. To address this problem, this paper applies deep model quantization and compression to CNN in ASIC chip for image retrieval. It is demonstrated that the CNN-based features descriptor can be extracted using as few as 2-bit weights quantization to deliver a similar performance as floating-point model for image retrieval. In addition, to implement CNN in ASIC, especially for large scale images, the limited buffer size of chips should be considered. To retrieve large scale images, we propose an improved pooling strategy, region nested invariance pooling (RNIP), which uses cropped sub-images for CNN. Testing results on chip show that integrating RNIP with the proposed 2-bit CNN model compression approach is capable of retrieving large scale images.
Variable Neighborhood Search for the Bin Packing Problem with Compatible Categories
Venue: arXiv cs.DS 2019
Year: 2019

Bin Packing with Conflicts (BPC) are problems in which items with compatibility constraints must be packed in the least number of bins, not exceeding the capacity of the bins and ensuring that non-conflicting items are packed in each bin. In this work, we introduce the Bin Packing Problem with Compatible Categories (BPCC), a variant of the BPC in which items belong to conflicting or compatible categories, in opposition to the item-by-item incompatibility found in previous literature. It is a common problem in the context of last mile distribution to nanostores located in densely populated areas. To efficiently solve real-life sized instances of the problem, we propose a Variable Neighborhood Search (VNS) metaheuristic algorithm. Computational experiments suggest that the algorithm yields good solutions in very short times while compared to linear integer programming running on a high-performance computing environment.
The Complexity of Transducer Synthesis from Multi-Sequential Specifications
Venue: arXiv cs.FL 2019
Year: 2019

The transducer synthesis problem on finite words asks, given a specification $S \subseteq I \times O$, where $I$ and $O$ are sets of finite words, whether there exists an implementation $f: I \rightarrow O$ which (1) fulfils the specification, i.e., $(i,f(i))\in S$ for all $i\in I$, and (2) can be defined by some input-deterministic (aka sequential) transducer $\mathcal{T}_f$. If such an implementation $f$ exists, the procedure should also output $\mathcal{T}_f$. The realisability problem is the corresponding decision problem. For specifications given by synchronous transducers (which read and write alternately one symbol), this is the finite variant of the classical synthesis problem on $ω$-words, solved by Büchi and Landweber in 1969, and the realisability problem is known to be ExpTime-c in both finite and $ω$-word settings. For specifications given by asynchronous transducers (which can write a batch of symbols, or none, in a single step), the realisability problem is known to be undecidable. We consider here the class of multi-sequential specifications, defined as finite unions of sequential transducers over possibly incomparable domains. We provide optimal decision procedures for the realisability problem in both the synchronous and asynchronous setting, showing that it is PSpace-c. Moreover, whenever the specification is realisable, we expose the construction of a sequential transducer that realises it and has a size that is doubly exponential, which we prove to be optimal.
Stochastic Fading Channel Models with Multiple Dominant Specular Components for 5G and Beyond
Venue: arXiv cs.IT 2019
Year: 2019

We introduce a comprehensive statistical characterization of the multipath wireless channel built as a superposition of a number of scattered waves with random phases. We consider an arbitrary number $N$ of specular (dominant) components plus other diffusely propagating waves. Our approach covers the cases on which the specular components have constant amplitudes, as well as when these components experience random fluctuations. These propagation scenarios are found in different use cases of 5G networks, as well as in the context of large intelligent surface based communications. We show that this class of fading models can be expressed in terms of a continuous mixture of an underlying Rician (or Rician shadowed) fading model, averaged over the phase distributions of the specular waves. It is shown that the fluctuations of the specular components have a detrimental impact on performance, and the best performance is obtained when there is only one specular component.
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
Venue: arXiv cs.SD 2019
Year: 2019

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
Venue: arXiv cs.CV 2019
Year: 2019

At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts. To facilitate this process, we contribute a fully-automatic lesion segmentation pipeline. This work proposes a method as a part of the LiTS (Liver Tumor Segmentation Challenge) competition for ISBI 17 and MICCAI 17 comparing methods for automatics egmentation of liver lesions in CT scans. By utilizing cascaded, densely connected 2D U-Nets and a Tversky-coefficient based loss function, our framework achieves very good shape extractions with high detection sensitivity, with competitive scores at time of publication. In addition, adjusting hyperparameters in our Tversky-loss allows to tune the network towards higher sensitivity or robustness.
Classificação de espécies de peixe utilizando redes neurais convolucional
Venue: arXiv cs.NE 2019
Year: 2019

Data classification is present in different real problems, such as recognizing patterns in images, differentiating defective parts in a production line, classifying benign and malignant tumors, among many others. Many of these problems have data patterns that are hard to identify, which requires more advanced techniques for resolution. Recently, several works addressing different artificial neural network architectures have been applied to solve classification problems. When the classification problem must be obtained through images, currently, the standard methodology is the use of convolutional neural networks. Thus, in this report convolutional neural networks are used to classify fish species. Classificação de dados está presente em diversos problemas reais, tais como: reconhecer padrões em imagens, diferenciar peças defeituosas em uma linha de produção, classificar tumores benignos e malignos, dentre diversas outras. Muitos desses problemas possuem padrões de dados difíceis de serem identificados, o que requer, consequentemente, técnicas mais avançadas para sua resolução. Recentemente, diversos trabalhos abordando diferentes arquiteturas de redes neurais artificiais vêm sendo aplicados para solucionar problemas de classificação. Quando a classificação do problema deve ser obtida por meio de imagens, atualmente a metodologia padrão é uso de redes neurais convolucionais. Sendo assim, neste trabalho são utilizadas redes neurais convolucionais para classificação de espécies de peixes.
Machine Vision in the Context of Robotics: A Systematic Literature Review
Venue: arXiv cs.CV 2019
Year: 2019

Machine vision is critical to robotics due to a wide range of applications which rely on input from visual sensors such as autonomous mobile robots and smart production systems. To create the smart homes and systems of tomorrow, an overview about current challenges in the research field would be of use to identify further possible directions, created in a systematic and reproducible manner. In this work a systematic literature review was conducted covering research from the last 10 years. We screened 172 papers from four databases and selected 52 relevant papers. While robustness and computation time were improved greatly, occlusion and lighting variance are still the biggest problems faced. From the number of recent publications, we conclude that the observed field is of relevance and interest to the research community. Further challenges arise in many areas of the field.
Bilinear discriminant feature line analysis for image feature extraction
Venue: arXiv cs.CV 2019
Year: 2019

A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix-based algorithm. It aims to minimise the within-class scatter and maximise the between-class scatter based on a two-dimensional (2D) NFL. Experimental results on two-image databases confirm the effectiveness.
Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss
Venue: arXiv cs.CV 2019
Year: 2019

We devise a cascade GAN approach to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions. Instead of learning a direct mapping from audio to video frames, we propose first to transfer audio to high-level structure, i.e., the facial landmarks, and then to generate video frames conditioned on the landmarks. Compared to a direct audio-to-image approach, our cascade approach avoids fitting spurious correlations between audiovisual signals that are irrelevant to the speech content. We, humans, are sensitive to temporal discontinuities and subtle artifacts in video. To avoid those pixel jittering problems and to enforce the network to focus on audiovisual-correlated regions, we propose a novel dynamically adjustable pixel-wise loss with an attention mechanism. Furthermore, to generate a sharper image with well-synchronized facial movements, we propose a novel regression-based discriminator structure, which considers sequence-level information along with frame-level information. Thoughtful experiments on several datasets and real-world samples demonstrate significantly better results obtained by our method than the state-of-the-art methods in both quantitative and qualitative comparisons.
Joint Segmentation and Path Classification of Curvilinear Structures
Venue: arXiv cs.CV 2019
Year: 2019

Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segmentation of the image and then refine it using either a set of hand-designed heuristics or a separate classifier that assigns likelihood to paths extracted from the pixel-wise prediction. In our work, we bridge the gap between segmentation and path classification by training a deep network that performs those two tasks simultaneously. We show that this approach is beneficial because it enforces consistency across the whole processing pipeline. We apply our approach on roads and neurons datasets.