OpenRaiser/Intern-Atlas huggingface dataset:
Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction
Venue: arXiv cs.HC 2016
Year: 2016

Recent years have seen significant market penetration for voice-based personal assistants such as Apple's Siri. However, despite this success, user take-up is frustratingly low. This position paper argues that there is a habitability gap caused by the inevitable mismatch between the capabilities and expectations of human users and the features and benefits provided by contemporary technology. Suggestions are made as to how such problems might be mitigated, but a more worrisome question emerges: "is spoken language all-or-nothing"? The answer, based on contemporary views on the special nature of (spoken) language, is that there may indeed be a fundamental limit to the interaction that can take place between mismatched interlocutors (such as humans and machines). However, it is concluded that interactions between native and non-native speakers, or between adults and children, or even between humans and dogs, might provide critical inspiration for the design of future speech-based human-machine interaction.
Scanning the IPv6 Internet: Towards a Comprehensive Hitlist
Venue: arXiv cs.NI 2016
Year: 2016

Active network measurements constitute an impor- tant part in gaining a better understanding of the Internet. Although IPv4-wide scans are now easily possible, random active probing is infeasible in the IPv6 Internet. Therefore, we propose a hybrid approach to generate a hitlist of IPv6 addresses for scanning: First, we extract IPv6 addresses from passive flow data. Second, we leverage publicly available resources such as rDNS data to gather further IPv6 addresses. Third, we conduct traceroute measurements from several vantage points to obtain additional addresses. We perform multiple active measurements on gathered IPv6 addresses and evaluate response rates over time. We extensively compare all IPv6 address sources. In total we found 150M unique IPv6 addresses over the course of four weeks. Our hitlist covers 72% of announced prefixes and 84% of Autonomous Systems. Finally, we give concrete recommendations to maximize source efficiency for different scan types.
A Novel Hybrid Fast Switching Adaptive No Delay Tanlock Loop Frequency Synthesizer
Venue: arXiv cs.IT 2016
Year: 2016

This paper presents a new fast switching hybrid frequency synthesizer with wide locking range. The hybrid synthesizer is based on the tanlock loop with no delay block (NDTL) and is capable of integer as well as fractional frequency division. The system maintains the in-lock state following the division process using an efficient adaptation mechanism. The fast switching and acquisition as well as the wide locking range and the robust jitter performance of the new hybrid NDTL synthesizer outperforms conventional time-delay tanlock loop (TDTL) synthesizer by orders of magnitude, making it attractive for synthesis even in Doppler environment. The performance of the hybrid synthesizer was evaluated under various conditions and the results demonstrate that it achieves the desired frequency division.
Polynomial Lower Bound for Distributed Graph Coloring in a Weak LOCAL Model
Venue: arXiv cs.DC 2016
Year: 2016

We show an $Ω\big(Δ^{\frac{1}{3}-\fracη{3}}\big)$ lower bound on the runtime of any deterministic distributed $\mathcal{O}\big(Δ^{1+η}\big)$-graph coloring algorithm in a weak variant of the \LOCAL\ model. In particular, given a network graph \mbox{$G=(V,E)$}, in the weak \LOCAL\ model nodes communicate in synchronous rounds and they can use unbounded local computation. We assume that the nodes have no identifiers, but that instead, the computation starts with an initial valid vertex coloring. A node can \textbf{broadcast} a \textbf{single} message of \textbf{unbounded} size to its neighbors and receives the \textbf{set of messages} sent to it by its neighbors. That is, if two neighbors of a node $v\in V$ send the same message to $v$, $v$ will receive this message only a single time; without any further knowledge, $v$ cannot know whether a received message was sent by only one or more than one neighbor. Neighborhood graphs have been essential in the proof of lower bounds for distributed coloring algorithms, e.g., \cite{linial92,Kuhn2006On}. Our proof analyzes the recursive structure of the neighborhood graph of the respective model to devise an $Ω\big(Δ^{\frac{1}{3}-\fracη{3}}\big)$ lower bound on the runtime for any deterministic distributed $\mathcal{O}\big(Δ^{1+η}\big)$-graph coloring algorithm. Furthermore, we hope that the proof technique improves the understanding of neighborhood graphs in general and that it will help towards finding a lower (runtime) bound for distributed graph coloring in the standard \LOCAL\ model. Our proof technique works for one-round algorithms in the standard \LOCAL\ model and provides a simpler and more intuitive proof for an existing $Ω(Δ^2)$ lower bound.
Generating Images Part by Part with Composite Generative Adversarial Networks
Venue: arXiv cs.AI 2016
Year: 2016

Image generation remains a fundamental problem in artificial intelligence in general and deep learning in specific. The generative adversarial network (GAN) was successful in generating high quality samples of natural images. We propose a model called composite generative adversarial network, that reveals the complex structure of images with multiple generators in which each generator generates some part of the image. Those parts are combined by alpha blending process to create a new single image. It can generate, for example, background and face sequentially with two generators, after training on face dataset. Training was done in an unsupervised way without any labels about what each generator should generate. We found possibilities of learning the structure by using this generative model empirically.
Discriminating between similar languages in Twitter using label propagation
Venue: arXiv cs.CL 2016
Year: 2016

Identifying the language of social media messages is an important first step in linguistic processing. Existing models for Twitter focus on content analysis, which is successful for dissimilar language pairs. We propose a label propagation approach that takes the social graph of tweet authors into account as well as content to better tease apart similar languages. This results in state-of-the-art shared task performance of $76.63\%$, $1.4\%$ higher than the top system.
Challenges for Efficient Query Evaluation on Structured Probabilistic Data
Venue: arXiv cs.DB 2016
Year: 2016

Query answering over probabilistic data is an important task but is generally intractable. However, a new approach for this problem has recently been proposed, based on structural decompositions of input databases, following, e.g., tree decompositions. This paper presents a vision for a database management system for probabilistic data built following this structural approach. We review our existing and ongoing work on this topic and highlight many theoretical and practical challenges that remain to be addressed.
Optimal Water Heater Control in Smart Home Environments
Venue: arXiv cs.SY 2016
Year: 2016

In this work, we develop an optimal water heater control method for a smart home environment. It is important to notice that unlike battery storage systems, energy flow in a water heater control system is not reversible. In order to increase the eigen consumption of photovoltaic energy, i.e. direct consumption of generated energy in the house, we propose to employ a dynamic programming approach to optimize heating schedules using forecasted consumption and weather data. Simulation results demonstrate the capability of our proposed system in reducing the overall energy cost while maintaining residents' comfort.
An Event Grouping Based Algorithm for University Course Timetabling Problem
Venue: arXiv cs.AI 2016
Year: 2016

This paper presents the study of an event grouping based algorithm for a university course timetabling problem. Several publications which discuss the problem and some approaches for its solution are analyzed. The grouping of events in groups with an equal number of events in each group is not applicable to all input data sets. For this reason, a universal approach to all possible groupings of events in commensurate in size groups is proposed here. Also, an implementation of an algorithm based on this approach is presented. The methodology, conditions and the objectives of the experiment are described. The experimental results are analyzed and the ensuing conclusions are stated. The future guidelines for further research are formulated.
Object Capabilities and Lightweight Affinity in Scala: Implementation, Formalization, and Soundness
Venue: arXiv cs.PL 2016
Year: 2016

Aliasing is a known source of challenges in the context of imperative object-oriented languages, which have led to important advances in type systems for aliasing control. However, their large-scale adoption has turned out to be a surprisingly difficult challenge. While new language designs show promise, they do not address the need of aliasing control in existing languages. This paper presents a new approach to isolation and uniqueness in an existing, widely-used language, Scala. The approach is unique in the way it addresses some of the most important obstacles to the adoption of type system extensions for aliasing control. First, adaptation of existing code requires only a minimal set of annotations. Only a single bit of information is required per class. Surprisingly, the paper shows that this information can be provided by the object-capability discipline, widely-used in program security. We formalize our approach as a type system and prove key soundness theorems. The type system is implemented for the full Scala language, providing, for the first time, a sound integration with Scala's local type inference. Finally, we empirically evaluate the conformity of existing Scala open-source code on a corpus of over 75,000 LOC.
Trainable Frontend For Robust and Far-Field Keyword Spotting
Venue: arXiv cs.CL 2016
Year: 2016

Robust and far-field speech recognition is critical to enable true hands-free communication. In far-field conditions, signals are attenuated due to distance. To improve robustness to loudness variation, we introduce a novel frontend called per-channel energy normalization (PCEN). The key ingredient of PCEN is the use of an automatic gain control based dynamic compression to replace the widely used static (such as log or root) compression. We evaluate PCEN on the keyword spotting task. On our large rerecorded noisy and far-field eval sets, we show that PCEN significantly improves recognition performance. Furthermore, we model PCEN as neural network layers and optimize high-dimensional PCEN parameters jointly with the keyword spotting acoustic model. The trained PCEN frontend demonstrates significant further improvements without increasing model complexity or inference-time cost.
Stochastic Backpropagation through Mixture Density Distributions
Venue: arXiv cs.NE 2016
Year: 2016

The ability to backpropagate stochastic gradients through continuous latent distributions has been crucial to the emergence of variational autoencoders and stochastic gradient variational Bayes. The key ingredient is an unbiased and low-variance way of estimating gradients with respect to distribution parameters from gradients evaluated at distribution samples. The "reparameterization trick" provides a class of transforms yielding such estimators for many continuous distributions, including the Gaussian and other members of the location-scale family. However the trick does not readily extend to mixture density models, due to the difficulty of reparameterizing the discrete distribution over mixture weights. This report describes an alternative transform, applicable to any continuous multivariate distribution with a differentiable density function from which samples can be drawn, and uses it to derive an unbiased estimator for mixture density weight derivatives. Combined with the reparameterization trick applied to the individual mixture components, this estimator makes it straightforward to train variational autoencoders with mixture-distributed latent variables, or to perform stochastic variational inference with a mixture density variational posterior.
FusionNet: 3D Object Classification Using Multiple Data Representations
Venue: arXiv cs.CV 2016
Year: 2016

High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton ModelNet challenge. The two representations: 1. Volumetric representation: the 3D object is discretized spatially as binary voxels - $1$ if the voxel is occupied and $0$ otherwise. 2. Pixel representation: the 3D object is represented as a set of projected 2D pixel images. Current leading submissions to the ModelNet Challenge use Convolutional Neural Networks (CNNs) on pixel representations. However, we diverge from this trend and additionally, use Volumetric CNNs to bridge the gap between the efficiency of the above two representations. We combine both representations and exploit them to learn new features, which yield a significantly better classifier than using either of the representations in isolation. To do this, we introduce new Volumetric CNN (V-CNN) architectures.
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.
Minimizing Uncertainty through Sensor Placement with Angle Constraints
Venue: arXiv cs.CG 2016
Year: 2016

We study the problem of sensor placement in environments in which localization is a necessity, such as ad-hoc wireless sensor networks that allow the placement of a few anchors that know their location or sensor arrays that are tracking a target. In most of these situations, the quality of localization depends on the relative angle between the target and the pair of sensors observing it. In this paper, we consider placing a small number of sensors which ensure good angular $α$-coverage: given $α$ in $[0,π/2]$, for each target location $t$, there must be at least two sensors $s_1$ and $s_2$ such that the $\angle(s_1 t s_2)$ is in the interval $[α, π-α]$. One of the main difficulties encountered in such problems is that since the constraints depend on at least two sensors, building a solution must account for the inherent dependency between selected sensors, a feature that generic Set Cover techniques do not account for. We introduce a general framework that guarantees an angular coverage that is arbitrarily close to $α$ for any $α<= π/3$ and apply it to a variety of problems to get bi-criteria approximations. When the angular coverage is required to be at least a constant fraction of $α$, we obtain results that are strictly better than what standard geometric Set Cover methods give. When the angular coverage is required to be at least $(1-1/δ)\cdotα$, we obtain a $\mathcal{O}(\log δ)$- approximation for sensor placement with $α$-coverage on the plane. In the presence of additional distance or visibility constraints, the framework gives a $\mathcal{O}(\logδ\cdot\log k_{OPT})$-approximation, where $k_{OPT}$ is the size of the optimal solution. We also use our framework to give a $\mathcal{O}(\log δ)$-approximation that ensures $(1-1/δ)\cdot α$-coverage and covers every target within distance $3R$.
An Adaptation of Topic Modeling to Sentences
Venue: arXiv cs.CL 2016
Year: 2016

Advances in topic modeling have yielded effective methods for characterizing the latent semantics of textual data. However, applying standard topic modeling approaches to sentence-level tasks introduces a number of challenges. In this paper, we adapt the approach of latent-Dirichlet allocation to include an additional layer for incorporating information about the sentence boundaries in documents. We show that the addition of this minimal information of document structure improves the perplexity results of a trained model.
Indebted households profiling: a knowledge discovery from database approach
Venue: arXiv cs.AI 2016
Year: 2016

A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk. The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels.
Visual Question Answering: A Survey of Methods and Datasets
Venue: arXiv cs.CV 2016
Year: 2016

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation
Venue: arXiv cs.CV 2016
Year: 2016

Echocardiography plays an important part in diagnostic aid in cardiac diseases. A critical step in echocardiography-aided diagnosis is to extract the standard planes since they tend to provide promising views to present different structures that are benefit to diagnosis. To this end, this paper proposes a spatial-temporal embedding framework to extract the standard view planes from 4D STIC (spatial-temporal image corre- lation) volumes. The proposed method is comprised of three stages, the frame smoothing, spatial-temporal embedding and final classification. In first stage, an L 0 smoothing filter is used to preprocess the frames that removes the noise and preserves the boundary. Then a compact repre- sentation is learned via embedding spatial and temporal features into a latent space in the supervised scheme considering both standard plane information and diagnosis result. In last stage, the learned features are fed into support vector machine to identify the standard plane. We eval- uate the proposed method on a 4D STIC volume dataset with 92 normal cases and 93 abnormal cases in three standard planes. It demonstrates that our method outperforms the baselines in both classification accuracy and computational efficiency.
On the Modeling of Error Functions as High Dimensional Landscapes for Weight Initialization in Learning Networks
Venue: arXiv cs.LG 2016
Year: 2016

Next generation deep neural networks for classification hosted on embedded platforms will rely on fast, efficient, and accurate learning algorithms. Initialization of weights in learning networks has a great impact on the classification accuracy. In this paper we focus on deriving good initial weights by modeling the error function of a deep neural network as a high-dimensional landscape. We observe that due to the inherent complexity in its algebraic structure, such an error function may conform to general results of the statistics of large systems. To this end we apply some results from Random Matrix Theory to analyse these functions. We model the error function in terms of a Hamiltonian in N-dimensions and derive some theoretical results about its general behavior. These results are further used to make better initial guesses of weights for the learning algorithm.
Sequence to sequence learning for unconstrained scene text recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like "g" and "9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.