OpenRaiser/Intern-Atlas huggingface dataset:
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
Venue: arXiv cs.CL 2019
Year: 2019

Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produced by the popular back-translation technique, raises the question if data selection could also be useful for synthetic data? In this work we use Infrequent N-gram Recovery (INR) and Feature Decay Algorithms (FDA), two transductive data selection methods to obtain subsets of sentences from synthetic data. These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it. Performing data selection on back-translated data creates new challenges as the source-side may contain noise originated by the model used in the back-translation. Hence, finding n-grams present in the test set become more difficult. Despite that, in our work we show that adapting a model with a selection of synthetic data is an useful approach.
Key Instance Selection for Unsupervised Video Object Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for each frame. We link objects according to their similarity until the M-th frame and then assign them unique IDs (i.e., instances). Similarity measure takes into account multiple properties such as ReID descriptor, expected trajectory, and semantic co-segmentation result. After M-th frame, we select K IDs based on video saliency and frequency of appearance; then only these key IDs are tracked through the remaining frames. Thanks to these technical contributions, our results are ranked third on the leaderboard of UVOS DAVIS challenge.
Indexing Graph Search Trees and Applications
Venue: arXiv cs.DS 2019
Year: 2019

We consider the problem of compactly representing the Depth First Search (DFS) tree of a given undirected or directed graph having $n$ vertices and $m$ edges while supporting various DFS related queries efficiently in the RAM with logarithmic word size. We study this problem in two well-known models: {\it indexing} and {\it encoding} models. While most of these queries can be supported easily in constant time using $O(n \lg n)$ bits\footnote{We use $\lg$ to denote logarithm to the base $2$.} of extra space, our goal here is, more specifically, to beat this trivial $O(n \lg n)$ bit space bound, yet not compromise too much on the running time of these queries. In the {\it indexing} model, the space bound of our solution involves the quantity $m$, hence, we obtain different bounds for sparse and dense graphs respectively. In the {\it encoding} model, we first give a space lower bound, followed by an almost optimal data structure with extremely fast query time. Central to our algorithm is a partitioning of the DFS tree into connected subtrees, and a compact way to store these connections. Finally, we also apply these techniques to compactly index the shortest path structure, biconnectivity structures among others.
Learning with Partially Ordered Representations
Venue: arXiv cs.FL 2019
Year: 2019

This paper examines the characterization and learning of grammars defined with enriched representational models. Model-theoretic approaches to formal language theory traditionally assume that each position in a string belongs to exactly one unary relation. We consider unconventional string models where positions can have multiple, shared properties, which are arguably useful in many applications. We show the structures given by these models are partially ordered, and present a learning algorithm that exploits this ordering relation to effectively prune the hypothesis space. We prove this learning algorithm, which takes positive examples as input, finds the most general grammar which covers the data.
Generative approach to unsupervised deep local learning
Venue: arXiv cs.CV 2019
Year: 2019

Most existing feature learning methods optimize inflexible handcrafted features and the affinity matrix is constructed by shallow linear embedding methods. Different from these conventional methods, we pretrain a generative neural network by stacking convolutional autoencoders to learn the latent data representation and then construct an affinity graph with them as a prior. Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss. The locality-preserving loss designed by the constructed affinity graph serves as prior to preserve the local structure during the fine-tuning stage, which in turn improves the quality of feature representation effectively. Furthermore, the self-expressive layer between the encoder and decoder is based on the assumption that each latent feature is a linear combination of other latent features, so the weighted combination coefficients of the self-expressive layer are used to construct a new refined affinity graph for representing the data structure. We conduct experiments on four datasets to demonstrate the superiority of the representation ability of our proposed model over the state-of-the-art methods.
Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring
Venue: arXiv cs.CV 2019
Year: 2019

Computing the gradient of an image is a common step in computer vision pipelines. The image gradient quantifies the magnitude and direction of edges in an image and is used in creating features for downstream machine learning tasks. Typically, the image gradient is represented as a grayscale image. This paper introduces directional pseudo-coloring, an approach to color the image gradient in a deliberate and coherent manner. By pseudo-coloring the image gradient magnitude with the image gradient direction, we can enhance the visual quality of image edges and achieve an artistic transformation of the original image. Additionally, we present a simple style transfer pipeline which learns a color map from a style image and then applies that color map to color the edges of a content image through the directional pseudo-coloring technique. Code for the algorithms and experiments is available at https://github.com/shouvikmani/edge-colorizer.
Subtle Censorship via Adversarial Fakeness in Kyrgyzstan
Venue: arXiv cs.CY 2019
Year: 2019

With the shift of public discourse to social media, we see simultaneously an expansion of civic engagement as the bar to enter the conversation is lowered, and the reaction by both state and non-state adversaries of free speech to silence these voices. Traditional forms of censorship struggle in this new situation to enforce the preferred narrative of those in power. Consequently, they have developed new methods for controlling the conversation that use the social media platform itself. Using the Central Asian republic of Kyrgyzstan as a main case study, this talk explores how this new form of "subtle" censorship relies on pretence and imitation, and why interdisciplinary methods of research are needed to grapple with it. We examine how "fakeness" in the form of fake news and profiles is used as methods of subtle censorship.
Arc-disjoint in- and out-branchings rooted at the same vertex in compositions of digraphs
Venue: arXiv cs.DM 2019
Year: 2019

A digraph $D=(V, A)$ has a good pair at a vertex $r$ if $D$ has a pair of arc-disjoint in- and out-branchings rooted at $r$. Let $T$ be a digraph with $t$ vertices $u_1,\dots , u_t$ and let $H_1,\dots H_t$ be digraphs such that $H_i$ has vertices $u_{i,j_i},\ 1\le j_i\le n_i.$ Then the composition $Q=T[H_1,\dots , H_t]$ is a digraph with vertex set $\{u_{i,j_i}\mid 1\le i\le t, 1\le j_i\le n_i\}$ and arc set $$A(Q)=\cup^t_{i=1}A(H_i)\cup \{u_{ij_i}u_{pq_p}\mid u_iu_p\in A(T), 1\le j_i\le n_i, 1\le q_p\le n_p\}.$$ When $T$ is arbitrary, we obtain the following result: every strong digraph composition $Q$ in which $n_i\ge 2$ for every $1\leq i\leq t$, has a good pair at every vertex of $Q.$ The condition of $n_i\ge 2$ in this result cannot be relaxed. When $T$ is semicomplete, we characterize semicomplete compositions with a good pair, which generalizes the corresponding characterization by Bang-Jensen and Huang (J. Graph Theory, 1995) for quasi-transitive digraphs. As a result, we can decide in polynomial time whether a given semicomplete composition has a good pair rooted at a given vertex.
PLANE: An Extensible Open Source Framework for modeling the Internet of Drones
Venue: arXiv cs.RO 2019
Year: 2019

Python Library for simulating unManNed vehiclEs(PLANE) is an open source software module, written in Python, that focuses on Unmanned Aerial Vehicles (UAVs), on their movements and on the mechanics of flight, thus devoting particular attention to the equations that describe drones' movement. In the context of the Internet of Drones (IoD), the module can be widely used for the study of the mutual control of position/coordination in scenarios in which drones may find obstacles, as it happens in densely populated urban scenarios. Emphasis is put on ease of use, performance evaluation, documentation, and Application Programming Interface (API) consistency. The software tool has minimal dependencies and is distributed under MIT License. Source code, binaries, and documentation can be downloaded from GitHub.
Neuromorphic Liquid Marbles With Aqueous Carbon Nanotube Cores
Venue: arXiv cs.ET 2019
Year: 2019

Neuromorphic computing devices attempt to emulate features of biological nervous systems through mimicking the properties of synapses, towards implementing the emergent properties of their counterparts, such as learning. Inspired by recent advances in the utilisation of liquid marbles (microlitre quantities of fluid coated in hydrophobic powder) for the creation of unconventional computing devices, we describe the development of liquid marbles with neuromorphic properties through the use of copper coatings and 1.0 mg/ml carbon nanotube-containing fluid cores. Experimentation was performed through sandwiching the marbles between two cup-style electrodes and stimulating them with repeated DC pulses at 3.0 V. Our results demonstrate that `entrainment' of a carbon nanotube filled-copper liquid marble via periodic pulses can cause their electrical resistance to rapidly switch between high to low resistance profiles, upon inverting the polarity of stimulation: the reduction in resistance between high and low profiles was approximately 88\% after two rounds of entrainment. This effect was found to be reversible through reversion to the original stimulus polarity and was strengthened by repeated experimentation, as evidenced by a mean reduction in time to switching onset of 43\%. These effects were not replicated in nanotube solutions not bound inside liquid marbles. Our electrical characterisation also reveals that nanotube-filled liquid marbles exhibit pinched loop hysteresis IV profiles consistent with the description of memristors. We conclude by discussing the applications of this technology to the development of unconventional computing devices and the study of emergent characteristics in biological neural tissue.
Secure Handshake Mechanism for Autonomous Flying Agents Using Robust Cryptosystem
Venue: arXiv cs.CR 2019
Year: 2019

The autonomous flying agents in a Network-centric environment and brings out various security threats and various techniques of Cryptography. Primary Focus is on study and implementation of how cryptographic algorithms can be effectively be used in a warfare scenario. The data security is the utmost key factor for the protection of data in such environments. The paper proposes mechanisms secured data transmission from the command center (which can be the sending flying agent) to shooter target. The command center and shooter target have a unique set of encryption and decryption key which are created randomly by calibrating the security level at run time. In the beginning, the encryption key used for encrypting data is received from a shooter target when the communication is authenticated through UDP sockets. The encrypted data is sent to the shooter target with the signed signature and command center's encryption key. The encrypted data and signature are then decrypted and verified respectively at the shooter target. The time analysis is performed and observed inputs are provided to the command center.
Standalone and RTK GNSS on 30,000 km of North American Highways
Venue: arXiv cs.RO 2019
Year: 2019

There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Automated Driving (AD) features. These range from a need for road determination (<5 meters), lane determination (<1.5 meters), and determining where the vehicle is within the lane (<0.3 meters). This work examines the performance of Global Navigation Satellite Systems (GNSS) on 30,000 km of North American highways to better understand the automotive positioning needs it meets today and what might be possible in the near future with wide area GNSS correction services and multi-frequency receivers. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System which couples two survey grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. We assess on-road GNSS accuracy, availability, and continuity. Availability and continuity are broken down in terms of satellite visibility, satellite geometry, position type (RTK fixed, RTK float, or standard positioning), and RTK correction latency over the network. Results show that current automotive solutions are best suited to meet road determination requirements at 98% availability but are less suitable for lane determination at 57%. Multi-frequency receivers with RTK corrections were found more capable with road determination at 99.5%, lane determination at 98%, and highway-level lane departure protection at 91%.
Mathematical modeling of a Cosserat method in finite-strain holonomic plasticity
Venue: arXiv cs.CE 2019
Year: 2019

This article deals with the mathematical derivation and the validation over benchmark examples of a numerical method for the solution of a finite-strain holonomic (rate-independent) Cosserat plasticity problem for materials, possibly with microstructure. Two improvements are made in contrast to earlier approaches: First, the micro-rotations are parameterized with the help of an Euler-Rodrigues formula related to quaternions. Secondly, as main result, a novel two-pass preconditioning scheme for searching the energy-minimizing solutions based on the limited memory Broyden-Fletcher-Goldstein-Shanno quasi-Newton method is proposed that consists of a predictor step and a corrector-iteration. After outlining the necessary adaptations to the model, numerical simulations compare the performance and efficiency of the new and the old algorithm. The proposed numerical model can be effectively employed for studying the mechanical response of complicated materials featuring large size effects.
Solver Recommendation For Transport Problems in Slabs Using Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

The use of machine learning algorithms to address classification problems is on the rise in many research areas. The current study is aimed at testing the potential of using such algorithms to auto-select the best solvers for transport problems in uniform slabs. Three solvers are used in this work: Richardson, diffusion synthetic acceleration, and nonlinear diffusion acceleration. Three parameters are manipulated to create different transport problem scenarios. Five machine learning algorithms are applied: linear discriminant analysis, K-nearest neighbors, support vector machine, random forest, and neural networks. We present and analyze the results of these algorithms for the test problems, showing that random forest and K-nearest neighbors are potentially the best suited candidates for this type of classification problem.
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Venue: arXiv cs.CL 2019
Year: 2019

Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. In this work, we build a unifying framework for RE, applying this on three highly used datasets (from the general, biomedical and clinical domains) with the ability to be extendable to new datasets. By performing a systematic exploration of modeling, pre-processing and training methodologies, we find that choices of pre-processing are a large contributor performance and that omission of such information can further hinder fair comparison. Other insights from our exploration allow us to provide recommendations for future research in this area.
Compressive Closeness in Networks
Venue: arXiv cs.SI 2019
Year: 2019

Distributed algorithms for network science applications are of great importance due to today's large real-world networks. In such algorithms, a node is allowed only to have local interactions with its immediate neighbors. This is because the whole network topological structure is often unknown to each node. Recently, distributed detection of central nodes, concerning different notions of importance, within a network has received much attention. Closeness centrality is a prominent measure to evaluate the importance (influence) of nodes, based on their accessibility, in a given network. In this paper, first, we introduce a local (ego-centric) metric that correlates well with the global closeness centrality; however, it has very low computational complexity. Second, we propose a compressive sensing (CS)-based framework to accurately recover high closeness centrality nodes in the network utilizing the proposed local metric. Both ego-centric metric computation and its aggregation via CS are efficient and distributed, using only local interactions between neighboring nodes. Finally, we evaluate the performance of the proposed method through extensive experiments on various synthetic and real-world networks. The results show that the proposed local metric correlates with the global closeness centrality, better than the current local metrics. Moreover, the results demonstrate that the proposed CS-based method outperforms the state-of-the-art methods with notable improvement.
Unification of Template-Expansion and XML-Validation
Venue: arXiv cs.LO 2019
Year: 2019

The processing of XML documents often includes creation and validation. These two operations are typically performed in two different nodes within a computer network that do not correlate with each other. The process of creation is also called instantiation of a template and can be described by filling a template with data from external repositories. Initial access to arbitrary sources can be formulated as an expression of certain command languages like XPath. Filling means copying invariant element nodes to the target document and unfolding variable parts from a given template. Validation is a descision problem returning true if a given XML document satisfies a schema and false otherwise. The main subject is to find a language that unions the template expansion and the validation. [..].
Extensions of Self-Improving Sorters
Venue: arXiv cs.DS 2019
Year: 2019

Ailon et al. (SICOMP 2011) proposed a self-improving sorter that tunes its performance to an unknown input distribution in a training phase. The input numbers $x_1,x_2,\ldots,x_n$ come from a product distribution, that is, each $x_i$ is drawn independently from an arbitrary distribution ${\cal D}_i$. We study two relaxations of this requirement. The first extension models hidden classes in the input. We consider the case that numbers in the same class are governed by linear functions of the same hidden random parameter. The second extension considers a hidden mixture of product distributions.
Preprocessing Methods and Pipelines of Data Mining: An Overview
Venue: arXiv cs.LG 2019
Year: 2019

Data mining is about obtaining new knowledge from existing datasets. However, the data in the existing datasets can be scattered, noisy, and even incomplete. Although lots of effort is spent on developing or fine-tuning data mining models to make them more robust to the noise of the input data, their qualities still strongly depend on the quality of it. The article starts with an overview of the data mining pipeline, where the procedures in a data mining task are briefly introduced. Then an overview of the data preprocessing techniques which are categorized as the data cleaning, data transformation and data preprocessing is given. Detailed preprocessing methods, as well as their influenced on the data mining models, are covered in this article.
Max-Plus Matching Pursuit for Deterministic Markov Decision Processes
Venue: arXiv cs.LG 2019
Year: 2019

We consider deterministic Markov decision processes (MDPs) and apply max-plus algebra tools to approximate the value iteration algorithm by a smaller-dimensional iteration based on a representation on dictionaries of value functions. The setup naturally leads to novel theoretical results which are simply formulated due to the max-plus algebra structure. For example, when considering a fixed (non adaptive) finite basis, the computational complexity of approximating the optimal value function is not directly related to the number of states, but to notions of covering numbers of the state space. In order to break the curse of dimensionality in factored state-spaces, we consider adaptive basis that can adapt to particular problems leading to an algorithm similar to matching pursuit from signal processing. They currently come with no theoretical guarantees but work empirically well on simple deterministic MDPs derived from low-dimensional continuous control problems. We focus primarily on deterministic MDPs but note that the framework can be applied to all MDPs by considering measure-based formulations.
vireoJD-MM at Activity Detection in Extended Videos
Venue: arXiv cs.CV 2019
Year: 2019

This notebook paper presents an overview and comparative analysis of our system designed for activity detection in extended videos (ActEV-PC) in ActivityNet Challenge 2019. Specifically, we exploit person/vehicle detections in spatial level and action localization in temporal level for action detection in surveillance videos. The mechanism of different tubelet generation and model decomposition methods are studied as well. The detection results are finally predicted by late fusing the results from each component.
testRNN: Coverage-guided Testing on Recurrent Neural Networks
Venue: arXiv cs.NE 2019
Year: 2019

Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce the first coverage-guided testing tool, coined testRNN, for the verification and validation of a major class of RNNs, long short-term memory networks (LSTMs). The tool implements a generic mutation-based test case generation method, and it empirically evaluates the robustness of a network using three novel LSTM structural test coverage metrics. Moreover, it is able to help the model designer go through the internal data flow processing of the LSTM layer. The tool is available through: https://github.com/TrustAI/testRNN under the BSD 3-Clause licence.