OpenRaiser/Intern-Atlas huggingface dataset:
SBG-Sketch: A Self-Balanced Sketch for Labeled-Graph Stream Summarization
Venue: arXiv cs.DB 2017
Year: 2017

Applications in various domains rely on processing graph streams, e.g., communication logs of a cloud-troubleshooting system, road-network traffic updates, and interactions on a social network. A labeled-graph stream refers to a sequence of streamed edges that form a labeled graph. Label-aware applications need to filter the graph stream before performing a graph operation. Due to the large volume and high velocity of these streams, it is often more practical to incrementally build a lossy-compressed version of the graph, and use this lossy version to approximately evaluate graph queries. Challenges arise when the queries are unknown in advance but are associated with filtering predicates based on edge labels. Surprisingly common, and especially challenging, are labeled-graph streams that have highly skewed label distributions that might also vary over time. This paper introduces Self-Balanced Graph Sketch (SBG-Sketch, for short), a graphical sketch for summarizing and querying labeled-graph streams that can cope with all these challenges. SBG-Sketch maintains synopsis for both the edge attributes (e.g., edge weight) as well as the topology of the streamed graph. SBG-Sketch allows efficient processing of graph-traversal queries, e.g., reachability queries. Experimental results over a variety of real graph streams show SBG-Sketch to reduce the estimation errors of state-of-the-art methods by up to 99%.
Improved Key Generation Algorithm for Gentry's Fully Homomorphic Encryption Scheme
Venue: arXiv cs.CR 2017
Year: 2017

At EUROCRYPT 2011, Gentry and Halevi implemented a variant of Gentry's fully homomorphic encryption scheme. The core part in their key generation is to generate an odd-determinant ideal lattice having a particular type of Hermite Normal Form. However, they did not give a rigorous proof for the correctness. We present a better key generation algorithm, improving their algorithm from two aspects. -We show how to deterministically generate ideal lattices with odd determinant, thus increasing the success probability close to 1. -We give a rigorous proof for the correctness. To be more specific, we present a simpler condition for checking whether the ideal lattice has the desired Hermite Normal Form. Furthermore, our condition can be checked more efficiently. As a result, our key generation is about 1.5 times faster. We also give experimental results supporting our claims. Our optimizations are based on the properties of ideal lattices, which might be of independent interests.
An improvement on LSB+ method
Venue: arXiv cs.CR 2017
Year: 2017

The Least Significant Bit (LSB) substitution is an old and simple data hiding method that could almost effortlessly be implemented in spatial or transform domain over any digital media. This method can be attacked by several steganalysis methods, because it detectably changes statistical and perceptual characteristics of the cover signal. A typical method for steganalysis of the LSB substitution is the histogram attack that attempts to diagnose anomalies in the cover image's histogram. A well-known method to stand the histogram attack is the LSB+ steganography that intentionally embeds some extra bits to make the histogram look natural. However, the LSB+ method still affects the perceptual and statistical characteristics of the cover signal. In this paper, we propose a new method for image steganography, called LSB++, which improves over the LSB+ image steganography by decreasing the amount of changes made to the perceptual and statistical attributes of the cover image. We identify some sensitive pixels affecting the signal characteristics, and then lock and keep them from the extra bit embedding process of the LSB+ method, by introducing a new embedding key. Evaluation results show that, without reducing the embedding capacity, our method can decrease potentially detectable changes caused by the embedding process.
Linear Quadratic Games with Costly Measurements
Venue: arXiv cs.GT 2017
Year: 2017

In this work we consider a stochastic linear quadratic two-player game. The state measurements are observed through a switched noiseless communication link. Each player incurs a finite cost every time the link is established to get measurements. Along with the usual control action, each player is equipped with a switching action to control the communication link. The measurements help to improve the estimate and hence reduce the quadratic cost but at the same time the cost is increased due to switching. We study the subgame perfect equilibrium control and switching strategies for the players. We show that the problem can be solved in a two-step process by solving two dynamic programming problems. The first step corresponds to solving a dynamic programming for the control strategy and the second step solves another dynamic programming for the switching strategy
Constructing a Hierarchical User Interest Structure based on User Profiles
Venue: arXiv cs.CL 2017
Year: 2017

The interests of individual internet users fall into a hierarchical structure which is useful in regards to building personalized searches and recommendations. Most studies on this subject construct the interest hierarchy of a single person from the document perspective. In this study, we constructed the user interest hierarchy via user profiles. We organized 433,397 user interests, referred to here as "attentions", into a user attention network (UAN) from 200 million user profiles; we then applied the Louvain algorithm to detect hierarchical clusters in these attentions. Finally, a 26-level hierarchy with 34,676 clusters was obtained. We found that these attention clusters were aggregated according to certain topics as opposed to the hyponymy-relation based conceptual ontologies. The topics can be entities or concepts, and the relations were not restrained by hyponymy. The concept relativity encapsulated in the user's interest can be captured by labeling the attention clusters with corresponding concepts.
Enabling Community Health Care with Microservices
Venue: arXiv cs.SE 2017
Year: 2017

Microservice architectures (MA) are composed of loosely coupled, course-grained services that emphasise resilience and autonomy, enabling more scalable applications to be developed. Such architectures are more tolerant of changing demands from users and enterprises, in response to emerging technologies and their associated influences upon human interaction and behaviour. This article looks at microservices in the Internet of Things (IoT) through the lens of agency, and using an example in the community health care domain explores how a complex application scenario (both in terms of software and hardware interactions) might be modelled.
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterative soft-thresholding algorithm (ISTA) that solves the optimization problem for sparse nonnegative matrix factorization (NMF) of spectrograms. We name this network architecture deep recurrent NMF (DR-NMF). The proposed DR-NMF network has three distinct advantages. First, DR-NMF provides better interpretability than other deep architectures, since the weights correspond to NMF model parameters, even after training. This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization. Second, like many deep networks, DR-NMF is an order of magnitude faster at test time than NMF, since computation of the network output only requires evaluating a few layers at each time step. Third, when a limited amount of training data is available, DR-NMF exhibits stronger generalization and separation performance compared to sparse NMF and state-of-the-art long-short term memory (LSTM) networks. When a large amount of training data is available, DR-NMF achieves lower yet competitive separation performance compared to LSTM networks.
Learning to Prove Safety over Parameterised Concurrent Systems (Full Version)
Venue: arXiv cs.LO 2017
Year: 2017

We revisit the classic problem of proving safety over parameterised concurrent systems, i.e., an infinite family of finite-state concurrent systems that are represented by some finite (symbolic) means. An example of such an infinite family is a dining philosopher protocol with any number n of processes (n being the parameter that defines the infinite family). Regular model checking is a well-known generic framework for modelling parameterised concurrent systems, where an infinite set of configurations (resp. transitions) is represented by a regular set (resp. regular transducer). Although verifying safety properties in the regular model checking framework is undecidable in general, many sophisticated semi-algorithms have been developed in the past fifteen years that can successfully prove safety in many practical instances. In this paper, we propose a simple solution to synthesise regular inductive invariants that makes use of Angluin's classic L* algorithm (and its variants). We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular. We have tested L* algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and German). Our experiments show that, despite the simplicity of our solution, it can perform at least as well as existing semi-algorithms.
Feature Engineering for Predictive Modeling using Reinforcement Learning
Venue: arXiv cs.AI 2017
Year: 2017

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.
In-depth comparison of the Berlekamp -- Massey -- Sakata and the Scalar-FGLM algorithms: the non adaptive variants
Venue: arXiv cs.SC 2017
Year: 2017

We compare thoroughly the Berlekamp -- Massey -- Sakata algorithm and the Scalar-FGLM algorithm, which compute both the ideal of relations of a multi-dimensional linear recurrent sequence. Suprisingly, their behaviors differ. We detail in which way they do and prove that it is not possible to tweak one of the algorithms in order to mimic exactly the behavior of the other.
Self-Dual Codes better than the Gilbert--Varshamov bound
Venue: arXiv cs.IT 2017
Year: 2017

We show that every self-orthogonal code over $\mathbb F_q$ of length $n$ can be extended to a self-dual code, if there exists self-dual codes of length $n$. Using a family of Galois towers of algebraic function fields we show that over any nonprime field $\mathbb F_q$, with $q\geq 64$, except possibly $q=125$, there are self-dual codes better than the asymptotic Gilbert--Varshamov bound.
High-Performance Derivative Computations using CoDiPack
Venue: arXiv cs.MS 2017
Year: 2017

There are several AD tools available, which all implement different strategies for the reverse mode of AD. The major strategies are primal value taping (implemented e.g. by ADOL-c) and Jacobi taping (implemented e.g. by adept and dco/c++). Especially for Jacobi taping, recent advances by using expression templates make this approach very attractive for large scale software. The current implementations are either closed source or miss essential features and flexibility. Therefore, we present the new AD tool CoDiPack (Code Differentiation Package) in this paper. It is specifically designed for a minimal memory consumption and optimal runtime, such that it can be used for the differentiation of large scale software. An essential part of the design of CoDiPack is the modular layout and the recursive data structures, which do not only allow the efficient implementation of the Jacobi taping approach, but will also enable other approaches like the primal value taping or new research ideas. We will also present the performance value of CoDiPack on a generic PDE example and on the SU2 code.
Learned Features are better for Ethnicity Classification
Venue: arXiv cs.CV 2017
Year: 2017

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features and then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor etc. Thorough experiments are presented on ten different facial databases which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively.
Virtual Blood Vessels in Complex Background using Stereo X-ray Images
Venue: arXiv cs.CV 2017
Year: 2017

We propose a fully automatic system to reconstruct and visualize 3D blood vessels in Augmented Reality (AR) system from stereo X-ray images with bones and body fat. Currently, typical 3D imaging technologies are expensive and carrying the risk of irradiation exposure. To reduce the potential harm, we only need to take two X-ray images before visualizing the vessels. Our system can effectively reconstruct and visualize vessels in following steps. We first conduct initial segmentation using Markov Random Field and then refine segmentation in an entropy based post-process. We parse the segmented vessels by extracting their centerlines and generating trees. We propose a coarse-to-fine scheme for stereo matching, including initial matching using affine transform and dense matching using Hungarian algorithm guided by Gaussian regression. Finally, we render and visualize the reconstructed model in a HoloLens based AR system, which can essentially change the way of visualizing medical data. We have evaluated its performance by using synthetic and real stereo X-ray images, and achieved satisfactory quantitative and qualitative results.
EmuFog: Extensible and Scalable Emulation of Large-Scale Fog Computing Infrastructures
Venue: arXiv cs.DC 2017
Year: 2017

The diversity of Fog Computing deployment models and the lack of publicly available Fog infrastructure makes the design of an efficient application or resource management policy a challenging task. Such research often requires a test framework that facilitates the experimental evaluation of an application or protocol design in a repeatable and controllable manner. In this paper, we present EmuFog---an extensible emulation framework tailored for Fog Computing scenarios---that enables the from-scratch design of Fog Computing infrastructures and the emulation of real applications and workloads. EmuFog enables researchers to design the network topology according to the use-case, embed Fog Computing nodes in the topology and run Docker-based applications on those nodes connected by an emulated network. Each of the sub-modules of EmuFog are easily extensible, although EmuFog provides a default implementation for each of them. The scalability and efficacy of EmuFog are evaluated both on synthetic and real-world network topologies.
Smart Mirror: Intelligent Makeup Recommendation and Synthesis
Venue: arXiv cs.CV 2017
Year: 2017

The female facial image beautification usually requires professional editing softwares, which are relatively difficult for common users. In this demo, we introduce a practical system for automatic and personalized facial makeup recommendation and synthesis. First, a model describing the relations among facial features, facial attributes and makeup attributes is learned as the makeup recommendation model for suggesting the most suitable makeup attributes. Then the recommended makeup attributes are seamlessly synthesized onto the input facial image.
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
Venue: arXiv cs.GR 2017
Year: 2017

Automatic mesh-based shape generation is of great interest across a wide range of disciplines, from industrial design to gaming, computer graphics and various other forms of digital art. While most traditional methods focus on primitive based model generation, advances in deep learning made it possible to learn 3-dimensional geometric shape representations in an end-to-end manner. However, most current deep learning based frameworks focus on the representation and generation of voxel and point-cloud based shapes, making it not directly applicable to design and graphics communities. This study addresses the needs for automatic generation of mesh-based geometries, and propose a novel framework that utilizes signed distance function representation that generates detail preserving three-dimensional surface mesh by a deep learning based approach.
Semantic Segmentation from Limited Training Data
Venue: arXiv cs.RO 2017
Year: 2017

We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.
Improving Language Modelling with Noise-contrastive estimation
Venue: arXiv cs.CL 2017
Year: 2017

Neural language models do not scale well when the vocabulary is large. Noise-contrastive estimation (NCE) is a sampling-based method that allows for fast learning with large vocabularies. Although NCE has shown promising performance in neural machine translation, it was considered to be an unsuccessful approach for language modelling. A sufficient investigation of the hyperparameters in the NCE-based neural language models was also missing. In this paper, we showed that NCE can be a successful approach in neural language modelling when the hyperparameters of a neural network are tuned appropriately. We introduced the 'search-then-converge' learning rate schedule for NCE and designed a heuristic that specifies how to use this schedule. The impact of the other important hyperparameters, such as the dropout rate and the weight initialisation range, was also demonstrated. We showed that appropriate tuning of NCE-based neural language models outperforms the state-of-the-art single-model methods on a popular benchmark.
Mining User Queries with Information Extraction Methods and Linked Data
Venue: arXiv cs.IR 2017
Year: 2017

Purpose: Advanced usage of Web Analytics tools allows to capture the content of user queries. Despite their relevant nature, the manual analysis of large volumes of user queries is problematic. This paper demonstrates the potential of using information extraction techniques and Linked Data to gather a better understanding of the nature of user queries in an automated manner. Design/methodology/approach: The paper presents a large-scale case-study conducted at the Royal Library of Belgium consisting of a data set of 83 854 queries resulting from 29 812 visits over a 12 month period of the historical newspapers platform BelgicaPress. By making use of information extraction methods, knowledge bases and various authority files, this paper presents the possibilities and limits to identify what percentage of end users are looking for person and place names. Findings: Based on a quantitative assessment, our method can successfully identify the majority of person and place names from user queries. Due to the specific character of user queries and the nature of the knowledge bases used, a limited amount of queries remained too ambiguous to be treated in an automated manner. Originality/value: This paper demonstrates in an empirical manner both the possibilities and limits of gaining more insights from user queries extracted from a Web Analytics tool and analysed with the help of information extraction tools and knowledge bases. Methods and tools used are generalisable and can be reused by other collection holders.
Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapter for the textbook Statistical Machine Translation. Used in the JHU Fall 2017 class on machine translation.
Attention-based Wav2Text with Feature Transfer Learning
Venue: arXiv cs.CL 2017
Year: 2017

Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But, it is widely known that information loss in the earlier stage can propagate through the later stages. After the resurgence of deep learning, interest has emerged in the possibility of developing a purely end-to-end ASR system from the raw waveform to the transcription without any predefined alignments and hand-engineered models. However, the successful attempts in end-to-end architecture still used spectral-based features, while the successful attempts in using raw waveform were still based on the hybrid deep neural network - Hidden Markov model (DNN-HMM) framework. In this paper, we construct the first end-to-end attention-based encoder-decoder model to process directly from raw speech waveform to the text transcription. We called the model as "Attention-based Wav2Text". To assist the training process of the end-to-end model, we propose to utilize a feature transfer learning. Experimental results also reveal that the proposed Attention-based Wav2Text model directly with raw waveform could achieve a better result in comparison with the attentional encoder-decoder model trained on standard front-end filterbank features.