OpenRaiser/Intern-Atlas huggingface dataset:
Real-Time Wind Noise Detection and Suppression with Neural-Based Signal Reconstruction for Mult-Channel, Low-Power Devices
Venue: arXiv cs.SD 2017
Year: 2017

Active wind noise detection and suppression techniques are a new and essential paradigm for enhancing ASR-based functionality with smart glasses, in addition to other wearable and smart devices in the broader IoT (Internet of things). In this paper, we develop two separate algorithms for wind noise detection and suppression, respectively, operational in a challenging, low-energy regime. Together, these algorithms comprise a robust wind noise suppression system. In the first case, we advance a real-time wind detection algorithm (RTWD) that uses two distinct sets of low-dimensional signal features to discriminate the presence of wind noise with high accuracy. For wind noise suppression, we employ an additional algorithm - attentive neural wind suppression (ANWS) - that utilizes a neural network to reconstruct the wearer speech signal from wind-corrupted audio in the spectral regions that are most adversely affected by wind noise. Finally, we test our algorithms through real-time experiments using low-power, multi-microphone devices with a wind simulator under challenging detection criteria and a variety of wind intensities.
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
Venue: arXiv cs.DC 2017
Year: 2017

In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community. These top data mining and machine learning algorithms cover classification, clustering, regression, graphical model-based learning, and dimensionality reduction. The goal of this study is to guide the focus of scalable computing experts in the endeavor of applying new storage and scalable computation designs to bioinformatics algorithms that merit their attention most, following the engineering maxim of "optimize the common case".
Dense RGB-D semantic mapping with Pixel-Voxel neural network
Venue: arXiv cs.CV 2017
Year: 2017

For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene's geometrical features but also simultaneously understand the higher level meaning of the scene contexts. Most previous methods focus on geometric 3D reconstruction and scene understanding independently notwithstanding the fact that joint estimation can boost the accuracy of the semantic mapping. In this paper, a dense RGB-D semantic mapping system with a Pixel-Voxel network is proposed, which can perform dense 3D mapping while simultaneously recognizing and semantically labelling each point in the 3D map. The proposed Pixel-Voxel network obtains global context information by using PixelNet to exploit the RGB image and meanwhile, preserves accurate local shape information by using VoxelNet to exploit the corresponding 3D point cloud. Unlike the existing architecture that fuses score maps from different models with equal weights, we proposed a Softmax weighted fusion stack that adaptively learns the varying contributions of PixelNet and VoxelNet, and fuses the score maps of the two models according to their respective confidence levels. The proposed Pixel-Voxel network achieves the state-of-the-art semantic segmentation performance on the SUN RGB-D benchmark dataset. The runtime of the proposed system can be boosted to 11-12Hz, enabling near to real-time performance using an i7 8-cores PC with Titan X GPU.
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
Venue: arXiv cs.RO 2017
Year: 2017

We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.
Towards Open Data for the Citation Content Analysis
Venue: arXiv cs.DL 2017
Year: 2017

The paper presents first results of the CitEcCyr project funded by RANEPA. The project aims to create a source of open citation data for research papers written in Russian. Compared to existing sources of citation data, CitEcCyr is working to provide the following added values: a) a transparent and distributed architecture of a technology that generates the citation data; b) an openness of all built/used software and created citation data; c) an extended set of citation data sufficient for the citation content analysis; d) services for public control over a quality of the citation data and a citing activity of researchers.
Large-scale weakly supervised audio classification using gated convolutional neural network
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labeled with one or a few audio tags but without timestamps of the audio events, which is called as weakly labeled data. Two sub-tasks are defined in this challenge including audio tagging and sound event detection using this weakly labeled data. A convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram is proposed. In addition, a temporal attention method is proposed along the frames to predicate the locations of each audio event in a chunk from the weakly labeled data. We ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6\% and Equal error 0.73, respectively.
S-CHIRP: Secure Communication for Heterogeneous IoTs with Round-Robin Protection
Venue: arXiv cs.CR 2017
Year: 2017

This work introduces CHIRP - an algorithm for communication between ultra-portable heterogeneous IoT devices with a type of round-robin protection mechanism. This algorithm is presented both in its basic form as well as in a secured form in order to secure and maintain trust boundaries and communication within specific groups of heterogeneous devices. The specific target application scenarios includes resource constrained environments where a co-located swarm of devices (adversarial in mission or objective) is also present. CHIRP, and its secured version (S-CHIRP), enables complete peer-to-peer communication of a $n$-agent network of devices in as few as n rounds. In addition to the n-round cycle length, the proposed communication mechanism has the following major properties: nodes communication is entirely decentralized, communication is resilient to the loss of nodes, and finally communication is resilient to the (re)-entry of nodes. Theoretical models show that even the secure implementation of this mechanism is capable of scaling to IoT swarms in the million device range with memory constraints in the < 10 MB range
Leaky Abstraction In Online Experimentation Platforms: A Conceptual Framework To Categorize Common Challenges
Venue: arXiv cs.HC 2017
Year: 2017

Online experimentation platforms abstract away many of the details of experimental design, ensuring experimenters do not have to worry about sampling, randomisation, subject tracking, data collection, metric definition and interpretation of results. The recent success and rapid adoption of these platforms in the industry might in part be attributed to the ease-of-use these abstractions provide. Previous authors have pointed out there are common pitfalls to avoid when running controlled experiments on the web and emphasised the need for experts familiar with the entire software stack to be involved in the process. In this paper, we argue that these pitfalls and the need to understand the underlying complexity are not the result of shortcomings specific to existing platforms which might be solved by better platform design. We postulate that they are a direct consequence of what is commonly referred to as "the law of leaky abstractions". That is, it is an inherent feature of any software platform that details of its implementation leak to the surface, and that in certain situations, the platform's consumers necessarily need to understand details of underlying systems in order to make proficient use of it. We present several examples of this concept, including examples from literature, and suggest some possible mitigation strategies that can be employed to reduce the impact of abstraction leakage. The conceptual framework put forward in this paper allows us to explicitly categorize experimentation pitfalls in terms of which specific abstraction is leaking, thereby aiding implementers and users of these platforms to better understand and tackle the challenges they face.
Square-Contact Representations of Partial 2-Trees and Triconnected Simply-Nested Graphs
Venue: arXiv cs.CG 2017
Year: 2017

A square-contact representation of a planar graph $G=(V,E)$ maps vertices in $V$ to interior-disjoint axis-aligned squares in the plane and edges in $E$ to adjacencies between the sides of the corresponding squares. In this paper, we study proper square-contact representations of planar graphs, in which any two squares are either disjoint or share infinitely many points. We characterize the partial $2$-trees and the triconnected cycle-trees allowing for such representations. For partial $2$-trees our characterization uses a simple forbidden subgraph whose structure forces a separating triangle in any embedding. For the triconnected cycle-trees, a subclass of the triconnected simply-nested graphs, we use a new structural decomposition for the graphs in this family, which may be of independent interest. Finally, we study square-contact representations of general triconnected simply-nested graphs with respect to their outerplanarity index.
The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty
Venue: arXiv cs.AI 2017
Year: 2017

In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands. The log is robust in terms of data, containing a total of 262 200 event logs, belonging to 13 087 different credit applications. The dataset is heterogeneous and consists of a mixture of computer generated automatic processes and manual human tasks. The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service, and to assess potential areas of improvement of the institution's internal processes. To this end we study the impact of incomplete event logs for the extraction and analysis of business processes. It is quite common that event logs are incomplete with several amounts of missing information (for instance, workers forget to register their tasks). Absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results. We investigate how classical probabilistic models are affected by incomplete event logs and we explore quantum-like probabilistic inferences as an alternative mathematical model to classical probability. This work represents a first step towards systematic investigation of the impact of quantum interference in a real life large scale decision scenario. The results obtained in this study indicate that, under high levels of uncertainty, the quantum-like models generate quantum interference terms, which allow an additional non-linear parameterisation of the data. Experimental results attest the efficiency of the quantum-like Bayesian networks, since the application of interference terms is able to reduce the error percentage of inferences performed over quantum-like models when compared to inferences produced by classical models.
Learning-based Caching in Cloud-Aided Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

This paper studies content caching in cloud-aided wireless networks where small cell base stations with limited storage are connected to the cloud via limited capacity fronthaul links. By formulating a utility (inverse of service delay) maximization problem, we propose a cache update algorithm based on spatio-temporal traffic demands. To account for the large number of contents, we propose a content clustering algorithm to group similar contents. Subsequently, with the aid of regret learning at small cell base stations and the cloud, each base station caches contents based on the learned content popularity subject to its storage constraints. The performance of the proposed caching algorithm is evaluated for sparse and dense environments while investigating the tradeoff between global and local class popularity. Simulation results show 15% and 40% gains in the proposed method compared to various baselines.
Orthogonal Vectors Indexing
Venue: arXiv cs.DS 2017
Year: 2017

In the recent years, intensive research work has been dedicated to prove conditional lower bounds in order to reveal the inner structure of the class P. These conditional lower bounds are based on many popular conjectures on well-studied problems. One of the most heavily used conjectures is the celebrated Strong Exponential Time Hypothesis (SETH). It turns out that conditional hardness proved based on SETH goes, in many cases, through an intermediate problem - the Orthogonal Vectors (OV) problem. Almost all research work regarding conditional lower bound was concentrated on time complexity. Very little attention was directed toward space complexity. In a recent work, Goldstein et al.[WADS 2017] set the stage for proving conditional lower bounds regarding space and its interplay with time. In this spirit, it is tempting to investigate the space complexity of a data structure variant of OV which is called \emph{OV indexing}. In this problem $n$ boolean vectors of size $c\log{n}$ are given for preprocessing. As a query, a vector $v$ is given and we are required to verify if there is an input vector that is orthogonal to it or not. This OV indexing problem is interesting in its own, but it also likely to have strong implications on problems known to be conditionally hard, in terms of time complexity, based on OV. Having this in mind, we study OV indexing in this paper from many aspects. We give some space-efficient algorithms for the problem, show a tradeoff between space and query time, describe how to solve its reporting variant, shed light on an interesting connection between this problem and the well-studied SetDisjointness problem and demonstrate how it can be solved more efficiently on random input.
Target Set Selection Parameterized by Clique-Width and Maximum Threshold
Venue: arXiv cs.DM 2017
Year: 2017

The Target Set Selection problem takes as an input a graph $G$ and a non-negative integer threshold $ \mbox{thr}(v) $ for every vertex $v$. A vertex $v$ can get active as soon as at least $ \mbox{thr}(v) $ of its neighbors have been activated. The objective is to select a smallest possible initial set of vertices, the target set, whose activation eventually leads to the activation of all vertices in the graph. We show that Target Set Selection is in FPT when parameterized with the combined parameters clique-width of the graph and the maximum threshold value. This generalizes all previous FPT-membership results for the parameterization by maximum threshold, and thereby solves an open question from the literature. We stress that the time complexity of our algorithm is surprisingly well-behaved and grows only single-exponentially in the parameters.
Aiding Autonomous Vehicles with Fault-tolerant V2V Communication
Venue: arXiv cs.NI 2017
Year: 2017

Vehicle-to-vehicle (V2V) communication is a key component of the future autonomous driving systems. V2V can provide an improved awareness of the surrounding environment, and the knowledge about the future actions of nearby vehicles. However, V2V communication is subject to different kind of failures and delays, so a distributed fault-tolerant approach is required for safe and efficient transportation. This work considers fully autonomous vehicles that operates using local sensory information, and aided with fault-tolerant V2V communication. The sensors provide all basic functionality, but are overridden by V2V whenever is possible to increase the efficiency. As an example scenario, we consider intersection crossing (IC) with autonomous vehicles that cooperate via V2V communication, and propose a fully distributed and a fault-tolerant algorithm for this problem. According to our numerical results, based on a real data set, we show the crossing delay is only slightly increased in the presence of a burst of V2V failures, and that V2V can be successfully used in most scenarios.
Sequential Deliberation for Social Choice
Venue: arXiv cs.GT 2017
Year: 2017

In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208- approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded.
Technical Note: Towards Virtual Monitors for Image Guided Interventions - Real-time Streaming to Optical See-Through Head-Mounted Displays
Venue: arXiv cs.OH 2017
Year: 2017

Purpose: Image guidance is crucial for the success of many interventions. Images are displayed on designated monitors that cannot be positioned optimally due to sterility and spatial constraints. This indirect visualization causes potential occlusion, hinders hand-eye coordination, leads to increased procedure duration and surgeon load. Methods: We propose a virtual monitor system that displays medical images in a mixed reality visualization using optical see-through head-mounted displays. The system streams high-resolution medical images from any modality to the head-mounted display in real-time that are blended with the surgical site. It allows for mixed reality visualization of images in head-, world-, or body-anchored mode and can thus be adapted to specific procedural needs. Results: For typical image sizes, the proposed system exhibits an average end-to-end delay and refresh rate of 214 +- 30 ms and 41:4 +- 32:0 Hz, respectively. Conclusions: The proposed virtual monitor system is capable of real-time mixed reality visualization of medical images. In future, we seek to conduct first pre-clinical studies to quantitatively assess the impact of the system on standard image guided procedures.
Clustering Stream Data by Exploring the Evolution of Density Mountain
Venue: arXiv cs.DB 2017
Year: 2017

Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batch-mode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster evolution activities? Unfortunately, most of existing stream clustering algorithms can neither update the cluster result in real time nor track the evolution of clusters. In this paper, we propose an stream clustering algorithm EDMStream by exploring the Evolution of Density Mountain. The density mountain is used to abstract the data distribution, the changes of which indicate data distribution evolution. We track the evolution of clusters by monitoring the changes of density mountains. We further provide efficient data structures and filtering schemes to ensure the update of density mountains in real time, which makes online clustering possible. The experimental results on synthetic and real datasets show that, comparing to the state-of-the-art stream clustering algorithms, e.g., D-Stream, DenStream, DBSTREAM and MR-Stream, our algorithm can response to a cluster update much faster (say 7-15x faster than the best of the competitors) and at the same time achieve comparable cluster quality. Furthermore, EDMStream can successfully capture the cluster evolution activities.
The Tersoff many-body potential: Sustainable performance through vectorization
Venue: arXiv cs.CE 2017
Year: 2017

Molecular dynamics models materials by simulating each individual particle's trajectory. Many-body potentials lead to a more accurate trajectory simulation, and are used in materials science and computational chemistry. We present optimization results for one multi-body potential on a range of vector instruction sets, targeting both CPUs and accelerators like the Intel Xeon Phi. Parallelization of MD simulations is well-studied; by contrast, vectorization is relatively unexplored. Given the prevalence and power of modern vector units, exploiting them is imperative for high performance software. When running on a highly parallel machine, any improvement to the scalar performance is paid back in hundreds or thousands of saved core hours. Vectorization is already commonly used in the optimization or pair potentials; multi-body potentials pose new, unique challenges. Indeed, their optimization pushes the boundaries of current compilers, forcing us to use explicit vectorization techniques for now. In this study, we add an optimized implementation of Tersoff potential to the LAMMPS molecular dynamics simulation package. To reduce the burden of explicit vectorization, we abstract from the specific vector instruction set and desired precision: From one algorithm, we get optimized implementations for many platforms, from SSE4.2 to AVX512, and the Intel Xeon Phi. We compare the kernels across different architectures, and determine suitable architecture-dependent parameters. Our optimizations benefit any architecture, but have a disproportionate effect on the Intel Xeon Phi, which beats the CPU (2xE5-2650) after optimization.