OpenRaiser/Intern-Atlas huggingface dataset:
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy
Venue: arXiv cs.SD 2018
Year: 2018

Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre- and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 ~ 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. An experiment with the DSD100 dataset on the full-tracks song evaluation task also shows that our model is able to compete with cutting-edge singing voice separation systems which use multi-channel modeling, data augmentation, and model blending.
QoS-aware Energy-Efficient Algorithms for Ethernet Link Aggregates in Software-Defined Networks
Venue: arXiv cs.NI 2018
Year: 2018

In this paper we discuss the implementation of an ONOS application that leverages Energy-Efficient Ethernet links between a pair of switches and shares incoming traffic among the link in the way that minimizes overall energy usage. As the straightforward solution can result in excessive traffic delay, we provide two alternative solutions to meet the demands of real time traffic arriving to the link aggregate. Experimental results show that our final application can keep low energy usage while meeting the demands of time-sensitive traffic, as long as the latter does not represent an excessive share of traffic demand.
Implementing energy saving algorithms for Ethernet link aggregates with ONOS
Venue: arXiv cs.NI 2018
Year: 2018

During the last few years, there has been plenty of research for reducing energy consumption in telecommunication infrastructure. However, many of the proposals remain unim-plemented due to the lack of flexibility in legacy networks. In this paper we demonstrate how the software defined networking (SDN) capabilities of current networking equipment can be used to implement some of these energy saving algorithms. In particular, we developed an ONOS application to realize an energy-aware traffic scheduler to a bundle link made up of Energy Efficient Ethernet (EEE) links between two SDN switches. We show how our application is able to dynamically adapt to the traffic characteristics and save energy by concentrating the traffic on as few ports as possible. This way, unused ports remain in Low Power Idle (LPI) state most of the time, saving energy.
Finding the vertices of the convex hull, even unordered, takes Omega(n log n) time -- a proof by reduction from epsilon-closeness
Venue: arXiv cs.CG 2018
Year: 2018

We consider the problem of computing, given a set S of n points in the plane, which points of S are vertices of the convex hull of S. For certain variations of this problem, different proofs exist that the complexity of this problem in the algebraic decision tree model is Omega(n log n). This paper provides a relatively simple proof by reduction from epsilon-closeness.
From biological vision to unsupervised hierarchical sparse coding
Venue: arXiv cs.CV 2018
Year: 2018

The formation of connections between neural cells is emerging essentially from an unsupervised learning process. For instance, during the development of the primary visual cortex of mammals (V1), we observe the emergence of cells selective to localized and oriented features. This leads to the development of a rough contour-based representation of the retinal image in area V1. We propose a biological model of the formation of this representation along the thalamo-cortical pathway. To achieve this goal, we replicated the Multi-Layer Convolutional Sparse Coding (ML-CSC) algorithm developed by Michael Elad's group.
Verlässliche Software im 21. Jahrhundert
Venue: arXiv cs.SE 2018
Year: 2018

Software is the main innovation driver in many different areas, like cloud services, autonomous driving, connected medical devices, and high-frequency trading. All these areas have in common that they require high dependability. In this paper, we discuss challenges and research directions imposed by these new areas on guaranteeing the dependability. On the one hand challenges include characteristics of the systems themselves, e. g., open systems and ad-hoc structures. On the other hand, we see new aspects of dependability like behavioral traceability.
Cross-spectral Periocular Recognition: A Survey
Venue: arXiv cs.CV 2018
Year: 2018

Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole face region (very relaxed large area). Research have shown that this is the region which does not get affected much because of various poses, aging, expression, facial changes and other artifacts, which otherwise would change to a large variation. Active research has been carried out on this topic since past few years due to its obvious advantages over face and iris biometrics in unconstrained and uncooperative scenarios. Many researchers have explored periocular biometrics involving both visible (VIS) and infra-red (IR) spectrum images. For a system to work for 24/7 (such as in surveillance scenarios), the registration process may depend on the day time VIS periocular images (or any mug shot image) and the testing or recognition process may occur in the night time involving only IR periocular images. This gives rise to a challenging research problem called the cross-spectral matching of images where VIS images are used for registration or as gallery images and IR images are used for testing or recognition process and vice versa. After intensive research of more than two decades on face and iris biometrics in cross-spectral domain, a number of researchers have now focused their work on matching heterogeneous (cross-spectral) periocular images. Though a number of surveys have been made on existing periocular biometric research, no study has been done on its cross-spectral aspect. This paper analyses and reviews current state-of-the-art techniques in cross-spectral periocular recognition including various methodologies, databases, their protocols and current-state-of-the-art recognition performances.
Natural Option Critic
Venue: arXiv cs.LG 2018
Year: 2018

The recently proposed option-critic architecture Bacon et al. provide a stochastic policy gradient approach to hierarchical reinforcement learning. Specifically, they provide a way to estimate the gradient of the expected discounted return with respect to parameters that define a finite number of temporally extended actions, called \textit{options}. In this paper we show how the option-critic architecture can be extended to estimate the natural gradient of the expected discounted return. To this end, the central questions that we consider in this paper are: 1) what is the definition of the natural gradient in this context, 2) what is the Fisher information matrix associated with an option's parameterized policy, 3) what is the Fisher information matrix associated with an option's parameterized termination function, and 4) how can a compatible function approximation approach be leveraged to obtain natural gradient estimates for both the parameterized policy and parameterized termination functions of an option with per-time-step time and space complexity linear in the total number of parameters. Based on answers to these questions we introduce the natural option critic algorithm. Experimental results showcase improvement over the vanilla gradient approach.
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Venue: arXiv cs.CV 2018
Year: 2018

While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available. Recent sparse multilinear regression methods based on tensor are emerging as promising solutions for fMRI, yet existing works rely on unfolding/folding operations and a tensor rank relaxation with limited tightness. The newly proposed tensor singular value decomposition (t-SVD) sheds light on new directions. In this work, we study t-SVD for sparse multilinear regression and propose a Sparse tubal-regularized multilinear regression (Sturm) method for fMRI. Specifically, the Sturm model performs multilinear regression with two regularization terms: a tubal tensor nuclear norm based on t-SVD and a standard L1 norm. We further derive the algorithm under the alternating direction method of multipliers framework. We perform experiments on four classification problems, including both resting-state fMRI for disease diagnosis and task-based fMRI for neural decoding. The results show the superior performance of Sturm in classifying fMRI using just a small number of voxels.
An Idea to Increase the Security of EAP-MD5 Protocol Against Dictionary Attack
Venue: arXiv cs.CR 2018
Year: 2018

IEEE 802.1X is an international standard for Port-based Network Access Control which provides authentication for devices applicant of either local network or wireless local network. This standard defines the packing of EAP protocol on IEEE 802. In this standard, authentication protocols become a complementary part of network security. There is a variety in EAP family protocols, regarding their speed and security. One of the fastest of these protocols is EAP-MD5 which is the main subject of this paper. Moreover, in order to improve EAP-MD5 security, a series of attacks against it have been investigated. In this paper at first EAP-MD5 protocol is introduced briefly and a series of the dictionary attacks against it are described. Then, based on observed weaknesses, by proposing an appropriate idea while maintaining the speed of execution, its security against dictionary attack is improved.
A small and adaptive coprocessor for information flow tracking in ARM SoCs
Venue: arXiv cs.CR 2018
Year: 2018

DIFT (Dynamic Information Flow Tracking) has been a hot topic for more than a decade. Unfortunately, existing hardware DIFT approaches have not been widely used neither by research community nor by hardware vendors. It is due to two major reasons: current hardware DIFT solutions lack support for multi-threaded applications and implementations for hardcore processors. This work addresses both issues by introducing an approach with some unique features: DIFT for multi-threaded software, virtual memory protection (rather than physical memory as in related works) and Linux kernel support using an information flow monitor called RFBlare. These goals are accomplished by taking advantage of a notable feature of ARM CoreSight components (context ID) combined with a custom DIFT coprocessor and RFBlare. The communication time overhead, major source of slowdown in total DIFT time overhead, is divided by a factor 3.8 compared to existing solutions with similar software constraints as in this work. The area overhead of this work is lower than 1% and power overhead is 16.2% on a middle-class Xilinx Zynq SoC.
Information Extraction Framework to Build Legislation Network
Venue: arXiv cs.IR 2018
Year: 2018

This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network data-sets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
Venue: arXiv cs.CV 2018
Year: 2018

We present the first method to capture the 3D total motion of a target person from a monocular view input. Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable mesh model. We use an efficient representation called 3D Part Orientation Fields (POFs), to encode the 3D orientations of all body parts in the common 2D image space. POFs are predicted by a Fully Convolutional Network (FCN), along with the joint confidence maps. To train our network, we collect a new 3D human motion dataset capturing diverse total body motion of 40 subjects in a multiview system. We leverage a 3D deformable human model to reconstruct total body pose from the CNN outputs by exploiting the pose and shape prior in the model. We also present a texture-based tracking method to obtain temporally coherent motion capture output. We perform thorough quantitative evaluations including comparison with the existing body-specific and hand-specific methods, and performance analysis on camera viewpoint and human pose changes. Finally, we demonstrate the results of our total body motion capture on various challenging in-the-wild videos. Our code and newly collected human motion dataset will be publicly shared.
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
Venue: arXiv cs.LG 2018
Year: 2018

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standard method for agent evaluation in reinforcement learning, Vanilla Monte Carlo, can miss failures entirely, leading to the deployment of unsafe agents. We demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. To address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. Our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities. The key difficulty is in identifying these adversarial situations -- since failures are rare there is little signal to drive optimization. To solve this we propose a continuation approach that learns failure modes in related but less robust agents. Our approach also allows reuse of data already collected for training the agent. We demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving. Experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days.
Rapid 3D Reconstruction of Indoor Environments to Generate Virtual Reality Serious Games Scenarios
Venue: arXiv cs.HC 2018
Year: 2018

Virtual Reality (VR) for Serious Games (SGs) is attracting increasing attention for training applications due to its potential to provide significantly enhanced learning to users. Some examples of the application of VR for SGs are complex training evacuation problems such as indoor earthquake evacuation or fire evacuation. The indoor 3D geometry of existing buildings can largely influence evacuees' behaviour, being instrumental in the design of VR SGs storylines and simulation scenarios. The VR scenarios of existing buildings can be generated from drawings and models. However, these data may not reflect the 'as-is' state of the indoor environment and may not be suitable to reflect dynamic changes of the system (e.g. Earthquakes), resulting in excessive development efforts to design credible and meaningful user experience. This paper explores several workflows for the rapid and effective reconstruction of 3D indoor environments of existing buildings that are suitable for earthquake simulations. These workflows start from Building Information Modelling (BIM), laser scanning and 360-degree panoramas. We evaluated the feasibility and efficiency of different approaches by using an earthquake-based case study developed for VR SGs.
Coverage and Rate Analysis for Unmanned Aerial Vehicle Base Stations with LoS/NLoS Propagation
Venue: arXiv cs.IT 2018
Year: 2018

The use of unmanned aerial vehicle base stations (UAV-BSs) as airborne base stations has recently gained great attention. In this paper, we model a network of UAV-BSs as a Poisson point process (PPP) operating at a certain altitude above the ground users. We adopt an air-to-ground (A2G) channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) propagation. Thus, UAV-BSs can be decomposed into two independent inhomogeneous PPPs. Under the assumption that NLoS and LoS channels experience Rayleigh and Nakagami-m fading, respectively, we derive approximations for the coverage probability and average achievable rate, and show that these approximations match the simulations with negligible errors. Numerical simulations have shown that the coverage probability and average achievable rate decrease as the height of the UAV-BSs increases.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Venue: arXiv cs.LG 2018
Year: 2018

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.
Optimal (and Benchmark-Optimal) Competition Complexity for Additive Buyers over Independent Items
Venue: arXiv cs.GT 2018
Year: 2018

The Competition Complexity of an auction setting refers to the number of additional bidders necessary in order for the (deterministic, prior-independent, dominant strategy truthful) Vickrey-Clarke-Groves mechanism to achieve greater revenue than the (randomized, prior-dependent, Bayesian-truthful) optimal mechanism without the additional bidders. We prove that the competition complexity of $n$ bidders with additive valuations over $m$ independent items is at most $n(\ln(1+m/n)+2)$, and also at most $9\sqrt{nm}$. When $n \leq m$, the first bound is optimal up to constant factors, even when the items are i.i.d. and regular. When $n \geq m$, the second bound is optimal for the benchmark introduced in [EFFTW17a] up to constant factors, even when the items are i.i.d. and regular. We further show that, while the Eden et al. benchmark is not necessarily tight in the $n \geq m$ regime, the competition complexity of $n$ bidders with additive valuations over even $2$ i.i.d. regular items is indeed $ω(1)$. Our main technical contribution is a reduction from analyzing the Eden et al. benchmark to proving stochastic dominance of certain random variables.
Enriching Article Recommendation with Phrase Awareness
Venue: arXiv cs.IR 2018
Year: 2018

Recent deep learning methods for recommendation systems are highly sophisticated. For article recommendation task, a neural network encoder which generates a latent representation of the article content would prove useful. However, using raw text with embedding for models could degrade sentence meanings and deteriorate performance. In this paper, we propose PhrecSys (Phrase-based Recommendation System), which injects phrase-level features into content-based recommendation systems to enhance feature informativeness and model interpretability. Experiments conducted on six months of real-world data demonstrate that phrase features boost content-based models in predicting both user click and view behavior. Furthermore, the attention mechanism illustrates that phrase awareness benefits the learning of textual focus by putting the model's attention on meaningful text spans, which leads to interpretable article recommendation.
Machine-learned epidemiology: real-time detection of foodborne illness at scale
Venue: arXiv cs.CY 2018
Year: 2018

Machine learning has become an increasingly powerful tool for solving complex problems, and its application in public health has been underutilized. The objective of this study is to test the efficacy of a machine-learned model of foodborne illness detection in a real-world setting. To this end, we built FINDER, a machine-learned model for real-time detection of foodborne illness using anonymous and aggregated web search and location data. We computed the fraction of people who visited a particular restaurant and later searched for terms indicative of food poisoning to identify potentially unsafe restaurants. We used this information to focus restaurant inspections in two cities and demonstrated that FINDER improves the accuracy of health inspections; restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe during the inspection as restaurants identified by existing methods. Additionally, FINDER enables us to ascertain previously intractable epidemiological information, for example, in 38% of cases the restaurant potentially causing food poisoning was not the last one visited, which may explain the lower precision of complaint-based inspections. We found that FINDER is able to reliably identify restaurants that have an active lapse in food safety, allowing for implementation of corrective actions that would prevent the potential spread of foodborne illness.