OpenRaiser/Intern-Atlas huggingface dataset:
Shape-based defect classification for Non Destructive Testing
Venue: arXiv cs.CV 2016
Year: 2016

The aim of this work is to classify the aerospace structure defects detected by eddy current non-destructive testing. The proposed method is based on the assumption that the defect is bound to the reaction of the probe coil impedance during the test. Impedance plane analysis is used to extract a feature vector from the shape of the coil impedance in the complex plane, through the use of some geometric parameters. Shape recognition is tested with three different machine-learning based classifiers: decision trees, neural networks and Naive Bayes. The performance of the proposed detection system are measured in terms of accuracy, sensitivity, specificity, precision and Matthews correlation coefficient. Several experiments are performed on dataset of eddy current signal samples for aircraft structures. The obtained results demonstrate the usefulness of our approach and the competiveness against existing descriptors.
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
Venue: arXiv cs.CV 2016
Year: 2016

Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this technical report, we compare several classification models and temporal smoothing methods. Our submitted solution is a fine tuned Residual Network-200 on 80% of the training set with temporal smoothing using simple temporal averaging of the predictions and a Hidden Markov Model modeling the sequence.
Entropy-based link prediction in weighted networks
Venue: arXiv cs.SI 2016
Year: 2016

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.
Vietnamese Named Entity Recognition using Token Regular Expressions and Bidirectional Inference
Venue: arXiv cs.CL 2016
Year: 2016

This paper describes an efficient approach to improve the accuracy of a named entity recognition system for Vietnamese. The approach combines regular expressions over tokens and a bidirectional inference method in a sequence labelling model. The proposed method achieves an overall $F_1$ score of 89.66% on a test set of an evaluation campaign, organized in late 2016 by the Vietnamese Language and Speech Processing (VLSP) community.
Semantic Decomposition and Recognition of Long and Complex Manipulation Action Sequences
Venue: arXiv cs.CV 2016
Year: 2016

Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and complex manipulation action tasks, such as "preparing a breakfast" or "making a sandwich". We represent manipulations with our recently introduced "Semantic Event Chain" (SEC) concept, which captures the underlying spatiotemporal structure of an action invariant to motion, velocity, and scene context. Solely based on the spatiotemporal interactions between manipulated objects and hands in the extracted SEC, the framework automatically parses individual manipulation streams performed either sequentially or concurrently. Using event chains, our method further extracts basic primitive elements of each parsed manipulation. Without requiring any prior object knowledge, the proposed framework can also extract object-like scene entities that exhibit the same role in semantically similar manipulations. We conduct extensive experiments on various recent datasets to validate the robustness of the framework.
Fast L1-NMF for Multiple Parametric Model Estimation
Venue: arXiv cs.CV 2016
Year: 2016

In this work we introduce a comprehensive algorithmic pipeline for multiple parametric model estimation. The proposed approach analyzes the information produced by a random sampling algorithm (e.g., RANSAC) from a machine learning/optimization perspective, using a \textit{parameterless} biclustering algorithm based on L1 nonnegative matrix factorization (L1-NMF). The proposed framework exploits consistent patterns that naturally arise during the RANSAC execution, while explicitly avoiding spurious inconsistencies. Contrarily to the main trends in the literature, the proposed technique does not impose non-intersecting parametric models. A new accelerated algorithm to compute L1-NMFs allows to handle medium-sized problems faster while also extending the usability of the algorithm to much larger datasets. This accelerated algorithm has applications in any other context where an L1-NMF is needed, beyond the biclustering approach to parameter estimation here addressed. We accompany the algorithmic presentation with theoretical foundations and numerous and diverse examples.
Feature-Based Classification of Networks
Venue: arXiv cs.SI 2016
Year: 2016

Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. At a finer scale of classification within each such class, networks describing more similar systems tend to have more similar features. This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify these networks into categories based on their features at various structural levels. Here we describe and demonstrate a new, hybrid approach that combines manual selection of features of potential interest with existing automated classification methods. In particular, selecting well-known and well-studied features that have been used throughout social network analysis and network science and then classifying with methods such as random forests that are of special utility in the presence of feature collinearity, we find that we achieve higher accuracy, in shorter computation time, with greater interpretability of the network classification results.
Parallel Construction of Wavelet Trees on Multicore Architectures
Venue: arXiv cs.DS 2016
Year: 2016

The wavelet tree has become a very useful data structure to efficiently represent and query large volumes of data in many different domains, from bioinformatics to geographic information systems. One problem with wavelet trees is their construction time. In this paper, we introduce two algorithms that reduce the time complexity of a wavelet tree's construction by taking advantage of nowadays ubiquitous multicore machines. Our first algorithm constructs all the levels of the wavelet in parallel in $O(n)$ time and $O(n\lgσ+ σ\lg n)$ bits of working space, where $n$ is the size of the input sequence and $σ$ is the size of the alphabet. Our second algorithm constructs the wavelet tree in a domain-decomposition fashion, using our first algorithm in each segment, reaching $O(\lg n)$ time and $O(n\lgσ+ pσ\lg n/\lgσ)$ bits of extra space, where $p$ is the number of available cores. Both algorithms are practical and report good speedup for large real datasets.
Büchi automata recognizing sets of reals definable in first-order logic with addition and order
Venue: arXiv cs.FL 2016
Year: 2016

This work considers weak deterministic Büchi automata reading encodings of non-negative reals in a fixed base. A Real Number Automaton is an automaton which recognizes all encoding of elements of a set of reals. It is explained how to decide in linear time whether a set of reals recognized by a given minimal weak deterministic RNA is ${FO}[\mathbb R;+,<,1]$-definable. Furthermore, it is explained how to compute in quasi-quadratic (respectively, quasi-linear) time an existential (respectively, existential-universal) ${FO}[\mathbb R;+,<,1]$-formula which defines the set of reals recognized by the automaton. It is also shown that techniques given by Muchnik and by Honkala for automata over vector of natural numbers also works on vector of real numbers. It implies that some problems such as deciding whether a set of tuples of reals $R\subseteq\mathbb R^{d}$ is a subsemigroup of $(\mathbb R^{d},+)$ or is ${FO}[\mathbb R;+,<,1]$-definable is decidable.
Enhancing mobile learning security
Venue: arXiv cs.CY 2016
Year: 2016

Mobile devices have been playing vital roles in modern day education delivery as students can access or download learning materials on their smartphones and tablets, they can also install educational apps and study anytime, anywhere. The need to provide adequate security for portable devices being used for learning cannot be underestimated. In this paper, we present a mobile security enhancement app, designed and developed for Android smart mobile devices in order to promote security awareness among students. The app can also identify major and the most significant security weaknesses, scan or check for vulnerabilities in m-learning devices and report any security threat.
Chinese Restaurant Process for cognate clustering: A threshold free approach
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we introduce a threshold free approach, motivated from Chinese Restaurant Process, for the purpose of cognate clustering. We show that our approach yields similar results to a linguistically motivated cognate clustering system known as LexStat. Our Chinese Restaurant Process system is fast and does not require any threshold and can be applied to any language family of the world.
On the Approximation of Functionals of Very Large Hermitian Matrices represented as Matrix Product Operators
Venue: arXiv cs.NA 2016
Year: 2016

We present a method to approximate functionals $\text{Tr} \, f(A)$ of very high-dimensional hermitian matrices $A$ represented as Matrix Product Operators (MPOs). Our method is based on a reformulation of a block Lanczos algorithm in tensor network format. We state main properties of the method and show how to adapt the basic Lanczos algorithm to the tensor network formalism to allow for high-dimensional computations. Additionally, we give an analysis of the complexity of our method and provide numerical evidence that it yields good approximations of the entropy of density matrices represented by MPOs while being robust against truncations.
Fundamental Limits on Throughput Capacity in Information-Centric Network
Venue: arXiv cs.NI 2016
Year: 2016

Wireless information-centric networks consider storage as one of the network primitives, and propose to cache data within the network in order to improve latency and reduce bandwidth consumption. We study the throughput capacity and latency in an information-centric network when the data cached in each node has a limited lifetime. The results show that with some fixed request and cache expiration rates, the order of the data access time does not change with network growth, and the maximum throughput order is not changing with the network growth in grid networks, and is inversely proportional to the number of nodes in one cell in random networks. Comparing these values with the corresponding throughput and latency with no cache capability (throughput inversely proportional to the network size, and latency of order $\sqrt{n}$ and the inverse of the transmission range in grid and random networks, respectively), we can actually quantify the asymptotic advantage of caching. Moreover, we compare these scaling laws for different content discovery mechanisms and illustrate that not much gain is lost when a simple path search is used.
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning
Venue: arXiv cs.LG 2016
Year: 2016

We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning. Our proposal is simpler and more biologically-plausible. Unlike previous approaches, our technique can be applied out of the box to all learning scenarios (e.g., online learning, batch learning, fully-connected, convolutional, feedforward, recurrent and mixed --- recurrent and convolutional) and compare favorably with existing approaches. We also propose Lp Normalization for normalizing by different orders of statistical moments. In particular, L1 normalization is well-performing, simple to implement, fast to compute, more biologically-plausible and thus ideal for GPU or hardware implementations.
RadioHound: A Pervasive Sensing Network for Sub-6 GHz Dynamic Spectrum Monitoring
Venue: arXiv cs.NI 2016
Year: 2016

We design a custom spectrum sensing network, called RadioHound, capable of tuning from 25 MHz to 6 GHz, which covers nearly all widely-deployed wireless activity. We describe the system hardware and network infrastructure in detail with a view towards driving the cost, size, and power usage of the sensors as low as possible. The system estimates the spatial variation of radio-frequency power from an unknown random number of sources. System performance is measured by computing the mean square error against a simulated radio-frequency environment. We find that the system performance depends heavily on the deployment density of the sensors. Consequently, we derive an expression for the sensor density as a function of environmental characteristics and confidence in measurement quality.
DeepGraph: Graph Structure Predicts Network Growth
Venue: arXiv cs.SI 2016
Year: 2016

The topological (or graph) structures of real-world networks are known to be predictive of multiple dynamic properties of the networks. Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features. These representations either fail to highlight local and global properties of the graph or suffer from a severe loss of structural information. There lacks an effective graph representation, which hinges the realization of the predictive power of network structures. In this study, we propose to learn the represention of a graph, or the topological structure of a network, through a deep learning model. This end-to-end prediction model, named DeepGraph, takes the input of the raw adjacency matrix of a real-world network and outputs a prediction of the growth of the network. The adjacency matrix is first represented using a graph descriptor based on the heat kernel signature, which is then passed through a multi-column, multi-resolution convolutional neural network. Extensive experiments on five large collections of real-world networks demonstrate that the proposed prediction model significantly improves the effectiveness of existing methods, including linear or nonlinear regressors that use hand-crafted features, graph kernels, and competing deep learning methods.
Embodiment of Learning in Electro-Optical Signal Processors
Venue: arXiv cs.ET 2016
Year: 2016

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.
Breakdown of a Benchmark Score Without Internal Analysis of Benchmarking Program
Venue: arXiv cs.PF 2016
Year: 2016

A breakdown of a benchmark score is how much each aspect of the system performance affects the score. Existing methods require internal analysis on the benchmarking program and then involve the following problems: (1) require a certain amount of labor for code analysis, profiling, simulation, and so on and (2) require the benchmarking program itself. In this paper, we present a method for breaking down a benchmark score without internal analysis of the benchmarking program. The method utilizes regression analysis of benchmark scores on a number of systems. Experimental results with 3 benchmarks on 15 Android smartphones showed that our method could break down those benchmark scores even though there is room for improvement in accuracy.
Maximizing positive opinion influence using an evidential approach
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we propose a new data based model for influence maximization in online social networks. We use the theory of belief functions to overcome the data imperfection problem. Besides, the proposed model searches to detect influencer users that adopt a positive opinion about the product, the idea, etc, to be propagated. Moreover, we present some experiments to show the performance of our model.
(Universal) Unconditional Verifiability in E-Voting without Trusted Parties
Venue: arXiv cs.CR 2016
Year: 2016

In traditional e-voting protocols, privacy is often provided by a trusted authority that learns the votes and computes the tally. Some protocols replace the trusted authority by a set of authorities, and privacy is guaranteed if less than a threshold number of authorities are corrupt. For verifiability, stronger security guarantees are demanded. Typically, corrupt authorities that try to fake the result of the tally must always be detected. To provide verifiability, many e-voting protocols use Non-Interactive Zero-Knowledge proofs (NIZKs). Thanks to their non-interactive nature, NIZKs allow anybody, including third parties that do not participate in the protocol, to verify the correctness of the tally. Therefore, NIZKs can be used to obtain universal verifiability. Additionally, NIZKs also improve usability because they allow voters to cast a vote using a non-interactive protocol. The disadvantage of NIZKs is that their security is based on setup assumptions such as the common reference string (CRS) or the random oracle (RO) model. The former requires a trusted party for the generation of a common reference string. The latter, though a popular methodology for designing secure protocols, has been shown to be unsound. In this paper, we address the design of an e-voting protocol that provides verifiability without any trust assumptions, where verifiability here is meant without eligibility verification. We show that Non-Interactive Witness-Indistinguishable proofs (NIWI) can be used for this purpose. The e-voting scheme is private under the Decision Linear assumption, while verifiability holds unconditionally. To our knowledge, this is the first private e-voting scheme with perfect universal verifiability, i.e. one in which the probability of a fake tally not being detected is 0, and with {\em non-interactive} protocols that does not rely on trust assumptions.
Another $q$-Polynomial Approach to Cyclic Codes
Venue: arXiv cs.IT 2016
Year: 2016

Recently, a $q$-polynomial approach to the construction and analysis of cyclic codes over $\gf(q)$ was given by Ding and Ling. The objective of this paper is to give another $q$-polynomial approach to all cyclic codes over $\gf(q)$.
Retrieving challenging vessel connections in retinal images by line co-occurrence statistics
Venue: arXiv cs.CV 2016
Year: 2016

Natural images contain often curvilinear structures, which might be disconnected, or partly occluded. Recovering the missing connection of disconnected structures is an open issue and needs appropriate geometric reasoning. We propose to find line co-occurrence statistics from the centerlines of blood vessels in retinal images and show its remarkable similarity to a well-known probabilistic model for the connectivity pattern in the primary visual cortex. Furthermore, the probabilistic model is trained from the data via statistics and used for automated grouping of interrupted vessels in a spectral clustering based approach. Several challenging image patches are investigated around junction points, where successful results indicate the perfect match of the trained model to the profiles of blood vessels in retinal images. Also, comparisons among several statistical models obtained from different datasets reveals their high similarity i.e., they are independent of the dataset. On top of that, the best approximation of the statistical model with the symmetrized extension of the probabilistic model on the projective line bundle is found with a least square error smaller than 2%. Apparently, the direction process on the projective line bundle is a good continuation model for vessels in retinal images.
$μ$-Bicomplete Categories and Parity Games
Venue: arXiv cs.LO 2016
Year: 2016

For an arbitrary category, we consider the least class of functors con- taining the projections and closed under finite products, finite coproducts, parameterized initial algebras and parameterized final coalgebras, i.e. the class of functors that are definable by $μ$-terms. We call the category $μ$-bicomplete if every $μ$-term defines a functor. We provide concrete ex- amples of such categories and explicitly characterize this class of functors for the category of sets and functions. This goal is achieved through par- ity games: we associate to each game an algebraic expression and turn the game into a term of a categorical theory. We show that $μ$-terms and parity games are equivalent, meaning that they define the same property of being $μ$-bicomplete. Finally, the interpretation of a parity game in the category of sets is shown to be the set of deterministic winning strategies for a chosen player.
Dynamic Probabilistic Network Based Human Action Recognition
Venue: arXiv cs.CV 2016
Year: 2016

This paper examines use of dynamic probabilistic networks (DPN) for human action recognition. The actions of lifting objects and walking in the room, sitting in the room and neutral standing pose were used for testing the classification. The research used the dynamic interrelation between various different regions of interest (ROI) on the human body (face, body, arms, legs) and the time series based events related to the these ROIs. This dynamic links are then used to recognize the human behavioral aspects in the scene. First a model is developed to identify the human activities in an indoor scene and this model is dependent on the key features and interlinks between the various dynamic events using DPNs. The sub ROI are classified with DPN to associate the combined interlink with a specific human activity. The recognition accuracy performance between indoor (controlled lighting conditions) is compared with the outdoor lighting conditions. The accuracy in outdoor scenes was lower than the controlled environment.