OpenRaiser/Intern-Atlas huggingface dataset:
Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm
Venue: arXiv cs.AI 2016
Year: 2016

This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.
Edge Based Grid Super-Imposition for Crowd Emotion Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Numerous automatic continuous emotion detection system studies have examined mostly use of videos and images containing individual person expressing emotions. This study examines the detection of spontaneous emotions in a group and crowd settings. Edge detection was used with a grid of lines superimposition to extract the features. The feature movement in terms of movement from the reference point was used to track across sequences of images from the color channel. Additionally the video data capturing was done on spontaneous emotions invoked by watching sports events from group of participants. The method was view and occlusion independent and the results were not affected by presence of multiple people chaotically expressing various emotions. The edge thresholds of 0.2 and grid thresholds of 20 showed the best accuracy results. The overall accuracy of the group emotion classifier was 70.9%.
Understanding and maintaining tactics graphically OR how we are learning that a diagram can be worth more than 10K LoC
Venue: arXiv cs.LO 2016
Year: 2016

The use of a functional language to implement proof strategies as proof tactics in interactive theorem provers, often provides short, concise and elegant implementations. Whilst being elegant, the use of higher order features and combinator languages often results in a very procedural view of a strategy, which may deviate significantly from the high-level ideas behind it. This can make a tactic hard to understand and hence difficult to to debug and maintain for experts and non-experts alike: one often has to tear apart complex combinations of lower level tactics manually in order to analyse a failure in the overall strategy. In an industrial technology transfer project, we have been working on porting a very large and complex proof tactic into PSGraph, a graphical language for representing proof strategies, supported by the Tinker tool. The goal of this work is to improve understandability and maintainability of tactics. Motivated by some initial successes with this, we here extend PSGraph with additional features for development and debugging. Through the re-implementation and refactoring of several existing tactics, we demonstrates the advantages of PSGraph compared with a typical linear (term-based) tactic language with respect to debugging, readability and maintenance. In order to act as guidance for others, we give a fairly detailed comparison of the user experience with the two approaches. The paper is supported by a web page providing further details about the implementation as well as interactive illustrations of the examples.
Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we define a novel census signal temporal logic (CensusSTL) that focuses on the number of agents in different subsets of a group that complete a certain task specified by the signal temporal logic (STL). CensusSTL consists of an "inner logic" STL formula and an "outer logic" STL formula. We present a new inference algorithm to infer CensusSTL formulae from the trajectory data of a group of agents. We first identify the "inner logic" STL formula and then infer the subgroups based on whether the agents' behaviors satisfy the "inner logic" formula at each time point. We use two different approaches to infer the subgroups based on similarity and complementarity, respectively. The "outer logic" CensusSTL formula is inferred from the census trajectories of different subgroups. We apply the algorithm in analyzing data from a soccer match by inferring the CensusSTL formula for different subgroups of a soccer team.
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Venue: arXiv cs.CV 2016
Year: 2016

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilise a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.
Mixed context networks for semantic segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy. Recently, FCN-based systems gained great improvement in this area. Unlike classification networks, combining features of different layers plays an important role in these dense prediction models, as these features contains information of different levels. A number of models have been proposed to show how to use these features. However, what is the best architecture to make use of features of different layers is still a question. In this paper, we propose a module, called mixed context network, and show that our presented system outperforms most existing semantic segmentation systems by making use of this module.
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation
Venue: arXiv cs.CV 2016
Year: 2016

Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g. clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. The tool and dataset are available at www.scenenn.net.
Proximity-Aware Balanced Allocations in Cache Networks
Venue: arXiv cs.DS 2016
Year: 2016

We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems that reduces network imbalance. In this paper, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. Since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the power of two choices in general. However, we prove that in certain regimes, in terms of memory limitation and proximity constraint, our scheme results in the maximum load of order $Θ(\log\log n)$ (here $n$ is the number of servers and requests), and at the same time, leads to a low communication cost. This is an exponential improvement in the maximum load compared to the scheme which assigns each request to the nearest available replica. Finally, we investigate our scheme performance by extensive simulations.
Practical estimation of rotation distance and induced partial order for binary trees
Venue: arXiv cs.DS 2016
Year: 2016

Tree rotations (left and right) are basic local deformations allowing to transform between two unlabeled binary trees of the same size. Hence, there is a natural problem of practically finding such transformation path with low number of rotations, the optimal minimal number is called the rotation distance. Such distance could be used for instance to quantify similarity between two trees for various machine learning problems, for example to compare hierarchical clusterings or arbitrarily chosen spanning trees of two graphs, like in SMILES notation popular for describing chemical molecules. There will be presented inexpensive practical greedy algorithm for finding a short rotation path, optimality of which has still to be determined. It uses introduced partial order for binary trees of the same size: $t_1 \leq t_2$ iff $t_2$ can be obtained from $t_1$ by a sequence of only right rotations. Intuitively, the shortest rotation path should go through the least upper bound or the greatest lower bound for this partial order. The algorithm finds a path through candidates for both points in representation of binary tree as stack graph: describing evolution of content of stack while processing a formula described by a given binary tree. The article is accompanied with Mathematica implementation of all used procedures (Appendix).
Fast and Accurate Surface Normal Integration on Non-Rectangular Domains
Venue: arXiv cs.NA 2016
Year: 2016

The integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision. However, even nowadays it is still a challenging task to devise a method that combines the flexibility to work on non-trivial computational domains with high accuracy, robustness and computational efficiency. By uniting a classic approach for surface normal integration with modern computational techniques we construct a solver that fulfils these requirements. Building upon the Poisson integration model we propose to use an iterative Krylov subspace solver as a core step in tackling the task. While such a method can be very efficient, it may only show its full potential when combined with a suitable numerical preconditioning and a problem-specific initialisation. We perform a thorough numerical study in order to identify an appropriate preconditioner for our purpose. To address the issue of a suitable initialisation we propose to compute this initial state via a recently developed fast marching integrator. Detailed numerical experiments illuminate the benefits of this novel combination. In addition, we show on real-world photometric stereo datasets that the developed numerical framework is flexible enough to tackle modern computer vision applications.
Scheduling Broadcasts in a Network of Timelines
Venue: arXiv cs.SI 2016
Year: 2016

Broadcasts and timelines are the primary mechanism of information exchange in online social platforms today. Services like Facebook, Twitter and Instagram have enabled ordinary people to reach large audiences spanning cultures and countries, while their massive popularity has created increasingly competitive marketplaces of attention. Timing broadcasts to capture the attention of such geographically diverse audiences has sparked interest from many startups and social marketing gurus. However, formal study is lacking on both the timing and frequency problems. We study for the first time the broadcast scheduling problem of specifying the timing and frequency of publishing content to maximise the attention received. We validate and quantify three interacting behavioural phenomena to parametrise social platform users: information overload, bursty circadian rhythms and monotony aversion, which is defined here for the first time. We formalise a timeline information exchange process based on these phenomena, and formulate an objective function that quantifies the expected collective attention. We finally present experiments on real data from Twitter, where we discover a counter-intuitive scheduling strategy that outperforms popular heuristics while producing fewer posts.
On the Security of Carrier Phase-based Ranging
Venue: arXiv cs.CR 2016
Year: 2016

Multicarrier phase-based ranging is fast emerging as a cost-optimized solution for a wide variety of proximity-based applications due to its low power requirement, low hardware complexity and compatibility with existing standards such as ZigBee and 6LoWPAN. Given potentially critical nature of the applications in which phase-based ranging can be deployed (e.g., access control, asset tracking), it is important to evaluate its security guarantees. Therefore, in this work, we investigate the security of multicarrier phase-based ranging systems and specifically focus on distance decreasing relay attacks that have proven detrimental to the security of proximity-based access control systems (e.g., vehicular passive keyless entry and start systems). We show that phase-based ranging, as well as its implementations, are vulnerable to a variety of distance reduction attacks. We describe different attack realizations and verify their feasibility by simulations and experiments on a commercial ranging system. Specifically, we successfully reduced the estimated range to less than 3 m even though the devices were more than 50 m apart. We discuss possible countermeasures against such attacks and illustrate their limitations, therefore demonstrating that phase-based ranging cannot be fully secured against distance decreasing attacks.
Efficiency of active learning for the allocation of workers on crowdsourced classification tasks
Venue: arXiv cs.HC 2016
Year: 2016

Crowdsourcing has been successfully employed in the past as an effective and cheap way to execute classification tasks and has therefore attracted the attention of the research community. However, we still lack a theoretical understanding of how to collect the labels from the crowd in an optimal way. In this paper we focus on the problem of worker allocation and compare two active learning policies proposed in the empirical literature with a uniform allocation of the available budget. To this end we make a thorough mathematical analysis of the problem and derive a new bound on the performance of the system. Furthermore we run extensive simulations in a more realistic scenario and show that our theoretical results hold in practice.
Honey Onions: a Framework for Characterizing and Identifying Misbehaving Tor HSDirs
Venue: arXiv cs.CR 2016
Year: 2016

In the last decade, Tor proved to be a very successful and widely popular system to protect users' anonymity. However, Tor remains a practical system with a variety of limitations, some of which were indeed exploited in the recent past. In particular, Tor's security relies on the fact that a substantial number of its nodes do not misbehave. In this work we introduce, the concept of honey onions, a framework to detect misbehaving Tor relays with HSDir capability. This allows to obtain lower bounds on misbehavior among relays. We propose algorithms to both estimate the number of snooping HSDirs and identify the most likely snoopers. Our experimental results indicate that during the period of the study (72 days) at least 110 such nodes were snooping information about hidden services they host. We reveal that more than half of them were hosted on cloud infrastructure and delayed the use of the learned information to prevent easy traceback.
Structured adaptive and random spinners for fast machine learning computations
Venue: arXiv cs.LG 2016
Year: 2016

We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections via structured matrices that we call Structured Spinners, which are formed as products of three structured matrix-blocks that incorporate rotations. The approach is highly generic, i.e. i) structured matrices under consideration can either be fully-randomized or learned, ii) our structured family contains as special cases all previously considered structured schemes, iii) the setting extends to the non-linear case where the projections are followed by non-linear functions, and iv) the method finds numerous applications including kernel approximations via random feature maps, dimensionality reduction algorithms, new fast cross-polytope LSH techniques, deep learning, convex optimization algorithms via Newton sketches, quantization with random projection trees, and more. The proposed framework comes with theoretical guarantees characterizing the capacity of the structured model in reference to its unstructured counterpart and is based on a general theoretical principle that we describe in the paper. As a consequence of our theoretical analysis, we provide the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the HD3HD2HD1 structured matrix [Andoni et al., 2015]. The exhaustive experimental evaluation confirms the accuracy and efficiency of structured spinners for a variety of different applications.
A model of infant speech perception and learning
Venue: arXiv cs.SD 2016
Year: 2016

Infant speech perception and learning is modeled using Echo State Network classification and Reinforcement Learning. Ambient speech for the modeled infant learner is created using the speech synthesizer Vocaltractlab. An auditory system is trained to recognize vowel sounds from a series of speakers of different anatomies in Vocaltractlab. Having formed perceptual targets, the infant uses Reinforcement Learning to imitate his ambient speech. A possible way of bridging the problem of speaker normalisation is proposed, using direct imitation but also including a caregiver who listens to the infants sounds and imitates those that sound vowel-like.
Clinical Text Prediction with Numerically Grounded Conditional Language Models
Venue: arXiv cs.CL 2016
Year: 2016

Assisted text input techniques can save time and effort and improve text quality. In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion. These extensions incorporate a structured knowledge base and numerical values from the text into the context used to predict the next word. Our automated evaluation on a clinical dataset shows extended models significantly outperform standard models. Our best system uses both conditioning and grounding, because of their orthogonal benefits. For word prediction with a list of 5 suggestions, it improves recall from 25.03% to 71.28% and for word completion it improves keystroke savings from 34.35% to 44.81%, where theoretical bound for this dataset is 58.78%. We also perform a qualitative investigation of how models with lower perplexity occasionally fare better at the tasks. We found that at test time numbers have more influence on the document level than on individual word probabilities.
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
Venue: arXiv cs.AI 2016
Year: 2016

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.
An Image Dataset of Text Patches in Everyday Scenes
Venue: arXiv cs.CV 2016
Year: 2016

This paper describes a dataset containing small images of text from everyday scenes. The purpose of the dataset is to support the development of new automated systems that can detect and analyze text. Although much research has been devoted to text detection and recognition in scanned documents, relatively little attention has been given to text detection in other types of images, such as photographs that are posted on social-media sites. This new dataset, known as COCO-Text-Patch, contains approximately 354,000 small images that are each labeled as "text" or "non-text". This dataset particularly addresses the problem of text verification, which is an essential stage in the end-to-end text detection and recognition pipeline. In order to evaluate the utility of this dataset, it has been used to train two deep convolution neural networks to distinguish text from non-text. One network is inspired by the GoogLeNet architecture, and the second one is based on CaffeNet. Accuracy levels of 90.2% and 90.9% were obtained using the two networks, respectively. All of the images, source code, and deep-learning trained models described in this paper will be publicly available
Authorship Attribution Based on Life-Like Network Automata
Venue: arXiv cs.CL 2016
Year: 2016

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over traditional analysis relying only on topological measurements. Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works. The optimized results obtained here pave the way for a better characterization of textual networks.
Jointly Learning to Align and Convert Graphemes to Phonemes with Neural Attention Models
Venue: arXiv cs.CL 2016
Year: 2016

We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including global and local attention, and our best models achieve state-of-the-art results on three standard data sets (CMUDict, Pronlex, and NetTalk).