OpenRaiser/Intern-Atlas huggingface dataset:
Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a sparse representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way and spectral information are embodied by training samples. A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image datasets. They illustrate that our proposed approach shows superior results in comparison to sparse representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers.
Perceptual audio loss function for deep learning
Venue: arXiv cs.SD 2017
Year: 2017

PESQ and POLQA , are standards are standards for automated assessment of voice quality of speech as experienced by human beings. The predictions of those objective measures should come as close as possible to subjective quality scores as obtained in subjective listening tests. Wavenet is a deep neural network originally developed as a deep generative model of raw audio wave-forms. Wavenet architecture is based on dilated causal convolutions, which exhibit very large receptive fields. In this short paper we suggest using the Wavenet architecture, in particular its large receptive filed in order to learn PESQ algorithm. By doing so we can use it as a differentiable loss function for speech enhancement.
Modelling Word Burstiness in Natural Language: A Generalised Polya Process for Document Language Models in Information Retrieval
Venue: arXiv cs.IR 2017
Year: 2017

We introduce a generalised multivariate Polya process for document language modelling. The framework outlined here generalises a number of statistical language models used in information retrieval for modelling document generation. In particular, we show that the choice of replacement matrix M ultimately defines the type of random process and therefore defines a particular type of document language model. We show that a particular variant of the general model is useful for modelling term-specific burstiness. Furthermore, via experimentation we show that this variant significantly improves retrieval effectiveness over a strong baseline on a number of small test collections.
Balanced partitions of 3-colored geometric sets in the plane
Venue: arXiv cs.CG 2017
Year: 2017

Let $S$ be a finite set of geometric objects partitioned into classes or \emph{colors}. A subset $S'\subseteq S$ is said to be \emph{balanced} if $S'$ contains the same amount of elements of $S$ from each of the colors. We study several problems on partitioning $3$-colored sets of points and lines in the plane into two balanced subsets: (a) We prove that for every 3-colored arrangement of lines there exists a segment that intersects exactly one line of each color, and that when there are $2m$ lines of each color, there is a segment intercepting $m$ lines of each color. (b) Given $n$ red points, $n$ blue points and $n$ green points on any closed Jordan curve $γ$, we show that for every integer $k$ with $0 \leq k \leq n$ there is a pair of disjoint intervals on $γ$ whose union contains exactly $k$ points of each color. (c) Given a set $S$ of $n$ red points, $n$ blue points and $n$ green points in the integer lattice satisfying certain constraints, there exist two rays with common apex, one vertical and one horizontal, whose union splits the plane into two regions, each one containing a balanced subset of $S$.
Distantly Supervised Road Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

We present an approach for road segmentation that only requires image-level annotations at training time. We leverage distant supervision, which allows us to train our model using images that are different from the target domain. Using large publicly available image databases as distant supervisors, we develop a simple method to automatically generate weak pixel-wise road masks. These are used to iteratively train a fully convolutional neural network, which produces our final segmentation model. We evaluate our method on the Cityscapes dataset, where we compare it with a fully supervised approach. Further, we discuss the trade-off between annotation cost and performance. Overall, our distantly supervised approach achieves 93.8% of the performance of the fully supervised approach, while using orders of magnitude less annotation work.
Evasion Attacks against Machine Learning at Test Time
Venue: arXiv cs.CR 2017
Year: 2017

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.
To Infinity and Beyond
Venue: arXiv cs.FL 2017
Year: 2017

We prove that if a group generated by a bireversible Mealy automaton contains an element of infinite order, its growth blows up and is necessarily exponential. As a direct consequence, no infinite virtually nilpotent group can be generated by a bireversible Mealy automaton.
Algorithm Substitution Attacks from a Steganographic Perspective
Venue: arXiv cs.CR 2017
Year: 2017

The goal of an algorithm substitution attack (ASA), also called a subversion attack (SA), is to replace an honest implementation of a cryptographic tool by a subverted one which allows to leak private information while generating output indistinguishable from the honest output. Bellare, Paterson, and Rogaway provided at CRYPTO'14 a formal security model to capture this kind of attacks and constructed practically implementable ASAs against a large class of symmetric encryption schemes. At CCS'15, Ateniese, Magri, and Venturi extended this model to allow the attackers to work in a fully-adaptive and continuous fashion and proposed subversion attacks against digital signature schemes. Both papers also showed the impossibility of ASAs in cases where the cryptographic tools are deterministic. Also at CCS'15, Bellare, Jaeger, and Kane strengthened the original model and proposed a universal ASA against sufficiently random encryption schemes. In this paper we analyze ASAs from the perspective of steganography - the well known concept of hiding the presence of secret messages in legal communications. While a close connection between ASAs and steganography is known, this lacks a rigorous treatment. We consider the common computational model for secret-key steganography and prove that successful ASAs correspond to secure stegosystems on certain channels and vice versa. This formal proof allows us to conclude that ASAs are stegosystems and to "rediscover" several results concerning ASAs known in the steganographic literature.
Givers & Receivers perceive handover tasks differently: Implications for Human-Robot collaborative system design
Venue: arXiv cs.HC 2017
Year: 2017

Human-human joint-action in short-cycle repetitive handover tasks was investigated for a bottle handover task using a three-fold approach: work-methods field studies in multiple supermarkets, simulation analysis using an ergonomics software package and by conducting an in-house lab experiment on human-human collaboration by re-creating the environment and conditions of a supermarket. Evaluation included both objective and subjective measures. Subjective evaluation was done taking a psychological perspective and showcases among other things, the differences in the way a common joint-action is being perceived by individual team partners depending upon their role (giver or receiver). The proposed approach can provide a systematic method to analyze similar tasks. Combining the results of all the three analyses, this research gives insight into the science of joint-action for short-cycle repetitive tasks and its implications for human-robot collaborative system design.
GraphR: Accelerating Graph Processing Using ReRAM
Venue: arXiv cs.DC 2017
Year: 2017

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.
Mixture Reduction on Matrix Lie Groups
Venue: arXiv cs.SY 2017
Year: 2017

Many physical systems evolve on matrix Lie groups and mixture filtering designed for such manifolds represent an inevitable tool for challenging estimation problems. However, mixture filtering faces the issue of a constantly growing number of components, hence require appropriate mixture reduction techniques. In this letter we propose a mixture reduction approach for distributions on matrix Lie groups, called the concentrated Gaussian distributions (CGDs). This entails appropriate reparametrization of CGD parameters to compute the KL divergence, pick and merge the mixture components. Furthermore, we also introduce a multitarget tracking filter on Lie groups as a mixture filtering study example for the proposed reduction method. In particular, we implemented the probability hypothesis density filter on matrix Lie groups. We validate the filter performance using the optimal subpattern assignment metric on a synthetic dataset consisting of 100 randomly generated multitarget scenarios.
The CARESSES EU-Japan project: making assistive robots culturally competent
Venue: arXiv cs.RO 2017
Year: 2017

The nursing literature shows that cultural competence is an important requirement for effective healthcare. We claim that personal assistive robots should likewise be culturally competent, that is, they should be aware of general cultural characteristics and of the different forms they take in different individuals, and take these into account while perceiving, reasoning, and acting. The CARESSES project is an Europe-Japan collaborative effort that aims at designing, developing and evaluating culturally competent assistive robots. These robots will be able to adapt the way they behave, speak and interact to the cultural identity of the person they assist. This paper describes the approach taken in the CARESSES project, its initial steps, and its future plans.
Dense Disparity Estimation in Ego-motion Reduced Search Space
Venue: arXiv cs.RO 2017
Year: 2017

Depth estimation from stereo images remains a challenge even though studied for decades. The KITTI benchmark shows that the state-of-the-art solutions offer accurate depth estimation, but are still computationally complex and often require a GPU or FPGA implementation. In this paper we aim at increasing the accuracy of depth map estimation and reducing the computational complexity by using information from previous frames. We propose to transform the disparity map of the previous frame into the current frame, relying on the estimated ego-motion, and use this map as the prediction for the Kalman filter in the disparity space. Then, we update the predicted disparity map using the newly matched one. This way we reduce disparity search space and flickering between consecutive frames, thus increasing the computational efficiency of the algorithm. In the end, we validate the proposed approach on real-world data from the KITTI benchmark suite and show that the proposed algorithm yields more accurate results, while at the same time reducing the disparity search space.
SigViewer: Visualizing Multimodal Signals Stored in XDF (Extensible Data Format) Files
Venue: arXiv cs.OH 2017
Year: 2017

Multimodal biosignal acquisition is facilitated by recently introduced software solutions such as LabStreaming Layer (LSL) and its associated data format XDF (Extensible Data Format). However, there are no stand-alone applications that can visualize multimodal time series stored in XDF files. We extended SigViewer, an open source cross-platform Qt C++ application with the capability of loading, resampling, annotating, and visualizing signals stored in XDF files and successfully applied the tool for post-hoc visual verification of the accuracy of a system that aims to predict the phase of alpha oscillations within the electroencephalogram in real-time.
Automated Website Fingerprinting through Deep Learning
Venue: arXiv cs.CR 2017
Year: 2017

Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets. By capturing traffic traces between users and their Tor entry guard, a network eavesdropper can leverage this meta-data to reveal which website Tor users are visiting. The success of such attacks heavily depends on the particular set of traffic features that are used to construct the fingerprint. Typically, these features are manually engineered and, as such, any change introduced to the Tor network can render these carefully constructed features ineffective. In this paper, we show that an adversary can automate the feature engineering process, and thus automatically deanonymize Tor traffic by applying our novel method based on deep learning. We collect a dataset comprised of more than three million network traces, which is the largest dataset of web traffic ever used for website fingerprinting, and find that the performance achieved by our deep learning approaches is comparable to known methods which include various research efforts spanning over multiple years. The obtained success rate exceeds 96% for a closed world of 100 websites and 94% for our biggest closed world of 900 classes. In our open world evaluation, the most performant deep learning model is 2% more accurate than the state-of-the-art attack. Furthermore, we show that the implicit features automatically learned by our approach are far more resilient to dynamic changes of web content over time. We conclude that the ability to automatically construct the most relevant traffic features and perform accurate traffic recognition makes our deep learning based approach an efficient, flexible and robust technique for website fingerprinting.
Sharpness-aware Low dose CT denoising using conditional generative adversarial network
Venue: arXiv cs.CV 2017
Year: 2017

Low Dose Computed Tomography (LDCT) has offered tremendous benefits in radiation restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset shows that the results of the proposed method have very small resolution loss and achieves better performance relative to the-state-of-art methods both quantitatively and visually.
Dyck Words, Lattice Paths, and Abelian Borders
Venue: arXiv cs.FL 2017
Year: 2017

We use results on Dyck words and lattice paths to derive a formula for the exact number of binary words of a given length with a given minimal abelian border length, tightening a bound on that number from Christodoulakis et al. (Discrete Applied Mathematics, 2014). We also extend to any number of distinct abelian borders a result of Rampersad et al. (Developments in Language Theory, 2013) on the exact number of binary words of a given length with no abelian borders. Furthermore, we generalize these results to partial words.
Input-Driven Double-Head Pushdown Automata
Venue: arXiv cs.FL 2017
Year: 2017

We introduce and study input-driven deterministic and nondeterministic double-head pushdown automata. A double-head pushdown automaton is a slight generalization of an ordinary pushdown automaton working with two input heads that move in opposite directions on the common input tape. In every step one head is moved and the automaton decides on acceptance if the heads meet. Demanding the automaton to work input-driven it is required that every input symbol uniquely defines the action on the pushdown store (push, pop, state change). Normally this is modeled by a partition of the input alphabet and is called a signature. Since our automaton model works with two heads either both heads respect the same signature or each head owes its own signature. This results in two variants of input-driven double-head pushdown automata. The induced language families on input-driven double-head pushdown automata are studied from the perspectives of their language describing capability, their closure properties, and decision problems.
CD Grammar Systems with Two Propagating Scattered Context Components Characterize the Family of Context Sensitive Languages
Venue: arXiv cs.FL 2017
Year: 2017

The L(PSCG)=L(CS) problem asks whether propagating scattered context grammars and context sensitive grammars are equivalent. The presented paper reformulates and answers this problem in terms of CD grammar systems. More specifically, it characterizes the family of context sensitive languages by two-component CD grammar systems with propagating scattered context rules.
A Novel Network NOMA Scheme for Downlink Coordinated Three-Point Systems
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a network non-orthogonal multiple access (N-NOMA) technique for the downlink coordinated multipoint (CoMP) communication scenario of a cellular network, with randomly deployed users. In the considered N-NOMA scheme, superposition coding (SC) is employed to serve cell-edge users as well as users close to base stations (BSs) simultaneously, and distributed analog beamforming by the BSs to meet the cell-edge user's quality of service (QoS) requirements. The combination of SC and distributed analog beamforming significantly complicates the expressions for the signal-to-interference-plus-noise ratio (SINR) at the reveiver, which makes the performance analysis particularly challenging. However, by using rational approximations, insightful analytical results are obtained in order to characterize the outage performance of the considered N-NOMA scheme. Computer simulation results are provided to show the superior performance of the proposed scheme as well as to demonstrate the accuracy of the analytical results.
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Venue: arXiv cs.AI 2017
Year: 2017

Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks and options), we show that addressing both problems simultaneously is simpler and more efficient in many cases. More specifically, we make the initiation set of options conditional on the previously-executed option, and show that options with such Option-Observation Initiation Sets (OOIs) are at least as expressive as Finite State Controllers (FSCs), a state-of-the-art approach for learning in POMDPs. OOIs are easy to design based on an intuitive description of the task, lead to explainable policies and keep the top-level and option policies memoryless. Our experiments show that OOIs allow agents to learn optimal policies in challenging POMDPs, while being much more sample-efficient than a recurrent neural network over options.
Long-Short Range Context Neural Networks for Language Modeling
Venue: arXiv cs.CL 2017
Year: 2017

The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.