OpenRaiser/Intern-Atlas huggingface dataset:
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Venue: arXiv cs.CV 2017
Year: 2017

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods
Venue: arXiv cs.SI 2017
Year: 2017

During the 2016 US elections Twitter experienced unprecedented levels of propaganda and fake news through the collaboration of bots and hired persons, the ramifications of which are still being debated. This work proposes an approach to identify the presence of organized behavior in tweets. The Random Forest, Support Vector Machine, and Logistic Regression algorithms are each used to train a model with a data set of 850 records consisting of 299 features extracted from tweets gathered during the 2016 US presidential election. The features represent user and temporal synchronization characteristics to capture coordinated behavior. These models are trained to classify tweet sets among the categories: organic vs organized, political vs non-political, and pro-Trump vs pro-Hillary vs neither. The random forest algorithm performs better with greater than 95% average accuracy and f-measure scores for each category. The most valuable features for classification are identified as user based features, with media use and marking tweets as favorite to be the most dominant.
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
Venue: arXiv cs.CV 2017
Year: 2017

Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Many researchers focus on how to generate images with one attribute. But image generation under multiple attributes is still a tough work. In this paper, we try to generate a variety of face images under multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative Adversarial Network) we present employs a pipeline network structure which can generate a complex facial image step by step using a neutral face image. We applied our method on two face image databases and demonstrate its ability to generate convincing novel images of unseen identities under multiple conditions previously.
An Abstract Method Linearization for Detecting Source Code Plagiarism in Object-Oriented Environment
Venue: arXiv cs.SE 2017
Year: 2017

Despite the fact that plagiarizing source code is a trivial task for most CS students, detecting such unethical behavior requires a considerable amount of effort. Thus, several plagiarism detection systems were developed to handle such issue. This paper extends Karnalim's work, a low-level approach for detecting Java source code plagiarism, by incorporating abstract method linearization. Such extension is incorporated to enhance the accuracy of low-level approach in term of detecting plagiarism in object-oriented environment. According to our evaluation, which was conducted based on 23 design-pattern source code pairs, our extended low-level approach is more effective than state-of-the-art and Karnalim's approach. On the one hand, when compared to state-of-the-art approach, our approach can generate less coincidental similarities and provide more accurate result. On the other hand, when compared to Karnalim's approach, our approach, at some extent, can generate higher similarity when simple abstract method invocation is incorporated.
Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT
Venue: arXiv cs.IT 2017
Year: 2017

This paper studies energy efficient resource allocation for a machine-to-machine (M2M) enabled cellular network with non-linear energy harvesting, especially focusing on two different multiple access strategies, namely non-orthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.
Blind estimation of white Gaussian noise variance in highly textured images
Venue: arXiv cs.CV 2017
Year: 2017

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.
La production de nitrites lors de la dénitrification des eaux usées par biofiltration - Stratégie de contrôle et de réduction des concentrations résiduelles
Venue: arXiv cs.SY 2017
Year: 2017

The recent popularity of post-denitrification processes in the greater Paris area wastewater treatment plants has caused a resurgence of the presence of nitrite in the Seine river. Controlling the production of nitrite during the post-denitrification has thus become a major technical issue. Research studies have been led in the MOCOPEE program (www.mocopee.com) to better understand the underlying mechanisms behind the production of nitrite during wastewater denitrification and to develop technical tools (measurement and control solutions) to assist on-site reductions of nitrite productions. Prior studies have shown that typical methanol dosage strategies produce a varying carbon-to-nitrogen ratio in the reactor, which in turn leads to unstable nitrite concentrations in the effluent. The possibility of adding a model-free control to the actual classical dosage strategy has thus been tested on the SimBio model, which simulates the behavior of wastewater biofilters. The corresponding "intelligent" feedback loop, which is using effluent nitrite concentrations, compensates the classical strategy only when needed. Simulation results show a clear improvement in average nitrite concentration level and level stability in the effluent, without a notable overcost in methanol.
TLib: A Flexible C++ Tensor Framework for Numerical Tensor Calculus
Venue: arXiv cs.MS 2017
Year: 2017

Numerical tensor calculus comprise basic tensor operations such as the entrywise addition and contraction of higher-order tensors. We present, TLib, flexible tensor framework with generic tensor functions and tensor classes that assists users to implement generic and flexible tensor algorithms in C++. The number of dimensions, the extents of the dimensions of the tensors and the contraction modes of the tensor operations can be runtime variable. Our framework provides tensor classes that simplify the management of multidimensional data and utilization of tensor operations using object-oriented and generic programming techniques. Additional stream classes help the user to verify and compare of numerical results with MATLAB. Tensor operations are implemented with generic tensor functions and in terms of multidimensional iterator types only, decoupling data storage representation and computation. The user can combine tensor functions with different tensor types and extend the framework without further modification of the classes or functions. We discuss the design and implementation of the framework and demonstrate its usage with examples that have been discussed in the literature.
Detection-aided liver lesion segmentation using deep learning
Venue: arXiv cs.CV 2017
Year: 2017

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesions on it. Moreover, we train a detector to localize the lesions, and mask the results of the segmentation network with the positive detections. The segmentation architecture is based on DRIU, a Fully Convolutional Network (FCN) with side outputs that work on feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. The main contribution of this work is the use of a detector to localize the lesions, which we show to be beneficial to remove false positives triggered by the segmentation network. Source code and models are available at https://imatge-upc.github.io/liverseg-2017-nipsws/ .
The Art of Crypto Currencies: A Comprehensive Analysis of Popular Crypto Currencies
Venue: arXiv cs.CR 2017
Year: 2017

Crypto Currencies have recently gained enormous popularity amongst the general public. With each passing day, more and more companies are radically accepting crypto cur-rencies in their payment systems, paving way for an economic revolution. Currently more than 700 crypto-currencies are avail-able at Coindesk alone for trade purposes. As of November 2016, the Crypto currencies hold a total market share of over 14 Billion USD1 [5]. With no centralized institution to monitor the movement of funds, Crypto currencies and their users are susceptible to multiple threats. In this paper we present an effort to explain the functionality of some of the most popular crypto currencies available in the online market. We present an analysis of the mining methodologies employed by these currencies to induce new currency into the market and how they compete with each other to provide fast, decentralized transactions to the users. We also share, some of the most dangerous attacks that can be placed on these crypto currencies and how the overall model of the crypto currencies mitigates these attacks. Towards the end, we will present taxonomy of the five highly popular crypto currencies and compare their features.
Towards Alzheimer's Disease Classification through Transfer Learning
Venue: arXiv cs.CV 2017
Year: 2017

Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.
Multimodal Attribute Extraction
Venue: arXiv cs.CL 2017
Year: 2017

The broad goal of information extraction is to derive structured information from unstructured data. However, most existing methods focus solely on text, ignoring other types of unstructured data such as images, video and audio which comprise an increasing portion of the information on the web. To address this shortcoming, we propose the task of multimodal attribute extraction. Given a collection of unstructured and semi-structured contextual information about an entity (such as a textual description, or visual depictions) the task is to extract the entity's underlying attributes. In this paper, we provide a dataset containing mixed-media data for over 2 million product items along with 7 million attribute-value pairs describing the items which can be used to train attribute extractors in a weakly supervised manner. We provide a variety of baselines which demonstrate the relative effectiveness of the individual modes of information towards solving the task, as well as study human performance.
"TenisRank": A new ranking of tennis players based on PageRank
Venue: arXiv cs.SI 2017
Year: 2017

In the light of the need to achieve a ranking which is understood by all tennis supporters, the ATP ranking is exposed to constant complaints from players and at the same time exposes new players to be benefited with a good tournament to be able to start progressing in their careers. Moreover, the ATP ranking is not powerful enough to predict with certainty who will be the winner of a match if we are based solely on the positions. In order to combat these problems, the idea of creating a new ranking that can indicate what are the real chances of victory of a player before the start of a new tournament arises. Based on the PageRank method, generated by Larry Page and Sergey Brin, we created a new ranking that specifically uses the characteristics of the tournament to generate data. Based on a history of 40,000 matches, we intend to evaluate how the new method is performed as compared to other existing rankings in order to analyze if we really achieved an improved and real reflection. Once we have obtained the ranking, we intend to evaluate, taking a sample game, the ranking of the players that dispute it and the characteristics of such game to be able to indicate the precise probability for the player with better ranking to win the game.
Stream Attention for far-field multi-microphone ASR
Venue: arXiv cs.SD 2017
Year: 2017

A stream attention framework has been applied to the posterior probabilities of the deep neural network (DNN) to improve the far-field automatic speech recognition (ASR) performance in the multi-microphone configuration. The stream attention scheme has been realized through an attention vector, which is derived by predicting the ASR performance from the phoneme posterior distribution of individual microphone stream, focusing the recognizer's attention to more reliable microphones. Investigation on the various ASR performance measures has been carried out using the real recorded dataset. Experiments results show that the proposed framework has yielded substantial improvements in word error rate (WER).
Towards Data Quality Assessment in Online Advertising
Venue: arXiv cs.AI 2017
Year: 2017

In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data sources provided by the advertisers or third-party data providers are utilized to choose the set of users according to the advertisers' targeting criteria. In this paper, we present a framework that can be applied to assess the quality of such data sources in large scale. This framework efficiently evaluates the similarity of a specific data source categorization to that of the ground truth, especially for those cases when the ground truth is accessible only in aggregate, and the user-level information is anonymized or unavailable due to privacy reasons. We propose multiple methodologies within this framework, present some preliminary assessment results, and evaluate how the methodologies compare to each other. We also present two use cases where we can utilize the data quality assessment results: the first use case is targeting specific user categories, and the second one is forecasting the desirable audiences we can reach for an online advertising campaign with pre-set targeting criteria.
On Asynchrony and Choreographies
Venue: arXiv cs.PL 2017
Year: 2017

Choreographic Programming is a paradigm for the development of concurrent software, where deadlocks are prevented syntactically. However, choreography languages are typically synchronous, whereas many real-world systems have asynchronous communications. Previous attempts at enriching choreographies with asynchrony rely on ad-hoc constructions, whose adequacy is only argued informally. In this work, we formalise the properties that an asynchronous semantics for choreographies should have: messages can be sent without the intended receiver being ready, and all sent messages are eventually received. We explore how out-of-order execution, used in choreographies for modelling concurrency, can be exploited to endow choreographies with an asynchronous semantics. Our approach satisfies the properties we identified. We show how our development yields a pleasant correspondence with FIFO-based asynchronous messaging, modelled in a process calculus, and discuss how it can be adopted in more complex choreography models.
Learning to Compose Skills
Venue: arXiv cs.AI 2017
Year: 2017

We present a differentiable framework capable of learning a wide variety of compositions of simple policies that we call skills. By recursively composing skills with themselves, we can create hierarchies that display complex behavior. Skill networks are trained to generate skill-state embeddings that are provided as inputs to a trainable composition function, which in turn outputs a policy for the overall task. Our experiments on an environment consisting of multiple collect and evade tasks show that this architecture is able to quickly build complex skills from simpler ones. Furthermore, the learned composition function displays some transfer to unseen combinations of skills, allowing for zero-shot generalizations.
High Dynamic Range Imaging Technology
Venue: arXiv cs.GR 2017
Year: 2017

In this lecture note, we describe high dynamic range (HDR) imaging systems; such systems are able to represent luminances of much larger brightness and, typically, also a larger range of colors than conventional standard dynamic range (SDR) imaging systems. The larger luminance range greatly improve the overall quality of visual content, making it appears much more realistic and appealing to observers. HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated today.
Constraint Bubbles: Adding Efficient Zero-Density Bubbles to Incompressible Free Surface Flow
Venue: arXiv cs.GR 2017
Year: 2017

Liquid simulations for computer animation often avoid simulating the air phase to reduce computational costs and ensure good conditioning of the linear systems required to enforce incompressibility. However, this free surface assumption leads to an inability to realistically treat bubbles: submerged gaps in the liquid are interpreted as empty voids that immediately collapse. To address this shortcoming, we present an efficient, practical, and conceptually simple approach to augment free surface flows with negligible density bubbles. Our method adds a new constraint to each disconnected air region that guarantees zero net flux across its entire surface, and requires neither simulating both phases nor reformulating into stream function variables. Implementation of the method requires only minor modifications to the pressure solve of a standard grid-based fluid solver, and yields linear systems that remain sparse and symmetric positive definite. In our evaluations, solving the modified pressure projection system took no more than 10% longer than the corresponding free surface solve. We demonstrate the method's effectiveness and flexibility by incorporating it into commercial fluid animation software and using it to generate a variety of dynamic bubble scenarios showcasing glugging effects, viscous and inviscid bubbles, interactions with irregularly-shaped and moving solid boundaries, and surface tension effects.
Google matrix of Bitcoin network
Venue: arXiv cs.SI 2017
Year: 2017

We construct and study the Google matrix of Bitcoin transactions during the time period from the very beginning in 2009 till April 2013. The Bitcoin network has up to a few millions of bitcoin users and we present its main characteristics including the PageRank and CheiRank probability distributions, the spectrum of eigenvalues of Google matrix and related eigenvectors. We find that the spectrum has an unusual circle-type structure which we attribute to existing hidden communities of nodes linked between their members. We show that the Gini coefficient of the transactions for the whole period is close to unity showing that the main part of wealth of the network is captured by a small fraction of users.