OpenRaiser/Intern-Atlas huggingface dataset:
Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study
Venue: arXiv cs.NE 2017
Year: 2017

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors.
Secure Location-Aided Routing Protocols With Wi-Fi Direct For Vehicular Ad Hoc Networks
Venue: arXiv cs.CR 2017
Year: 2017

Secure routing protocols are proposed for the vehicular ad hoc networks. The protocols integrate the security authentication process with the Location-Aided Routing (LAR) protocol to support Wi-Fi Direct communications between the vehicles. The methods are robust against various security threats. The security authentication process adopts a modified Diffie-Hellman key agreement protocol. The Diffie-Hellman protocol is used with a short authentication string (SAS)-based key agreement over Wi-Fi Direct out-of-band communication channels. It protects the communication from any man-in-the-middle security threats. In particular, the security process is integrated into two LAR routing schemes, i.e., the request-zone LAR scheme and the distance-based LAR scheme. We conduct extensive simulations with different network parameters such as the vehicular node density, the number of the malicious nodes, and the speed of the nodes. Simulation results show that the proposed routing protocols provide superior performance in secure data delivery and average total packet delay. Also, the secure distance-based LAR protocol outperforms the secure request-zone LAR protocol.
Efficient Discovering of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data
Venue: arXiv cs.DB 2017
Year: 2017

We consider the problem of discovering sequential patterns from event-based spatio-temporal data. The dataset is described by a set of event types and their instances. Based on the given dataset, the task is to discover all significant sequential patterns denoting some attraction relation between event types occurring in a pattern. Already proposed algorithms discover all significant sequential patterns based on the significance threshold, which minimal value is given by an expert. Due to the nature of described data and complexity of discovered patterns, it may be very difficult to provide reasonable value of significance threshold. We consider the problem of effective discovering of K most important patterns in a given dataset (that is discovering of Top-K patterns).
Deep-learning-based data page classification for holographic memory
Venue: arXiv cs.CV 2017
Year: 2017

We propose a deep-learning-based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multi-layer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. The MLP was found to have a classification accuracy of 91.58%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is two orders of magnitude better than the MLP.
Arabic Character Segmentation Using Projection Based Approach with Profile's Amplitude Filter
Venue: arXiv cs.CV 2017
Year: 2017

Arabic is one of the languages that present special challenges to Optical character recognition (OCR). The main challenge in Arabic is that it is mostly cursive. Therefore, a segmentation process must be carried out to determine where the character begins and where it ends. This step is essential for character recognition. This paper presents Arabic character segmentation algorithm. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. This is done using profile's amplitude filter and simple edge tool to find characters separations. Our algorithm shows promising performance when applied on different machine printed documents with different Arabic fonts.
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Venue: arXiv cs.LG 2017
Year: 2017

We describe a parallel bayesian online deep learning framework (PBODL) for click-through rate (CTR) prediction within today's Tencent advertising system, which provides quick and accurate learning of user preferences. We first explain the framework with a deep probit regression model, which is trained with probabilistic back-propagation in the mode of assumed Gaussian density filtering. Then we extend the model family to a variety of bayesian online models with increasing feature embedding capabilities, such as Sparse-MLP, FM-MLP and FFM-MLP. Finally, we implement a parallel training system based on a stream computing infrastructure and parameter servers. Experiments with public available datasets and Tencent industrial datasets show that models within our framework perform better than several common online models, such as AdPredictor, FTRL-Proximal and MatchBox. Online A/B test within Tencent advertising system further proves that our framework could achieve CTR and CPM lift by learning more quickly and accurately.
XBadges. Identifying and training soft skills with commercial video games
Venue: arXiv cs.CY 2017
Year: 2017

XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, spatial reasoning and risk taking before and after subjects participate in controlled game playing sessions. In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development.
Identification of non-linear behavior models with restricted or redundant data
Venue: arXiv cs.NE 2017
Year: 2017

This study presents a new strategy for the identification of material parameters in the case of restricted or redundant data, based on a hybrid approach combining a genetic algorithm and the Levenberg-Marquardt method. The proposed methodology consists essentially in a statistically based topological analysis of the search domain, after this one has been reduced by the analysis of the parameters ranges. This is used to identify the parameters of a model representing the behavior of damaged elastic, visco-elastic, plastic and visco-plastic composite laminates. Optimization of the experimental tests on tubular samples leads to the selective identification of these parameters.
Prediction of Domain Values: High throughput screening of domain names using Support Vector Machines
Venue: arXiv cs.CY 2017
Year: 2017

As connected devices multiply and the internet matures into a ubiquitous platform for exchange and communication, the question of what makes a domain name valuable is ever more significant. Due to the scarcity of meaningful vocabulary and the persistence of domain-related data, the buying and selling of previously owned domain names, also known as the domain aftermarket, has evolved into a billion dollar industry. Each day over a 100,000 domain names expire and become available for re-registration. Manual appraisal is impossible at such a volume; thus a method for the automated identification of valuable domain names is called for. The aim of our study was to develop a method for high throughput screening of domain names for rapid identification of the valuable ones. Five different aspects that make a domain name valuable were identified: name quality, domain authority, domain traffic, active domain age and domain health. An SVM method was developed for high throughput screening of domain names. Our method was able to identify valuable domain names with 97% accuracy for the test set and 93% for the external set and can be used for routinely screening the domain aftermarket.
Properties of a Projected Network of a Bipartite Network
Venue: arXiv cs.SI 2017
Year: 2017

Bipartite Graph is often a realistic model of complex networks where two different sets of entities are involved and relationship exist only two entities belonging to two different sets. Examples include the user-item relationship of a recommender system, actor-movie relationship of an online movie database systems. One way to compress a bipartite graph is to take unweighted or weighted one mode projection of one side vertices. Properties of this projected network are extremely important in many practical situations (say the selection process of influencing nodes for viral marketing). In this paper, we have studied the topological properties for projected network and some theoretical results are proved including the presence of cliques, connectedness for unweighted projection and maximum edge weight for weighted projected network.
Window-of-interest based Multi-objective Evolutionary Search for Satisficing Concepts
Venue: arXiv cs.AI 2017
Year: 2017

The set-based concept approach has been suggested as a means to simultaneously explore different design concepts, which are meaningful sub-sets of the entire set of solutions. Previous efforts concerning the suggested approach focused on either revealing the global front (s-Pareto front), of all the concepts, or on finding the concepts' fronts, within a relaxation zone. In contrast, here the aim is to reveal which of the concepts have at least one solution with a performance vector within a pre-defined window-of-interest (WOI). This paper provides the rational for this new concept-based exploration problem, and suggests a WOI-based rather than Pareto-based multi-objective evolutionary algorithm. The proposed algorithm, which simultaneously explores different concepts, is tested using a recently suggested concept-based benchmarking approach. The numerical study of this paper shows that the algorithm can cope with various numerical difficulties in a simultaneous way, which outperforms a sequential exploration approach.
Characterizing videos, audience and advertising in Youtube channels for kids
Venue: arXiv cs.CY 2017
Year: 2017

Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in children's everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South.
Statistical Properties of Loss Rate Estimators in Tree Topology (2)
Venue: arXiv cs.NI 2017
Year: 2017

Four types of explicit estimators are proposed here to estimate the loss rates of the links in a network with the tree topology and all of them are derived by the maximum likelihood principle. One of the four is developed from an estimator that was used but neglected because it was suspected to have a higher variance. All of the estimators are proved to be either unbiased or asymptotic unbiased. In addition, a set of formulae are derived to compute the efficiencies and variances of the estimates obtained by the estimators. One of the formulae shows that if a path is divided into two segments, the variance of the estimates obtained for the pass rate of a segment is equal to the variance of the pass rate of the path divided by the square of the pass rate of the other segment. A number of theorems and corollaries are derived from the formulae that can be used to evaluate the performance of an estimator. Using the theorems and corollaries, we find the estimators from the neglected one are the best estimator for the networks with the tree topology in terms of efficiency and computation complexity.
Control Flow Information Analysis in Process Model Matching Techniques
Venue: arXiv cs.OH 2017
Year: 2017

Online Appendix to: "Analyzing Control Flow Information to Improve the Effectiveness of Process Model Matching Techniques" by the same authors.
Hidden-Markov-Model Based Speech Enhancement
Venue: arXiv cs.SD 2017
Year: 2017

The goal of this contribution is to use a parametric speech synthesis system for reducing background noise and other interferences from recorded speech signals. In a first step, Hidden Markov Models of the synthesis system are trained. Two adequate training corpora consisting of text and corresponding speech files have been set up and cleared of various faults, including inaudible utterances or incorrect assignments between audio and text data. Those are tested and compared against each other regarding e.g. flaws in the synthesized speech, it's naturalness and intelligibility. Thus different voices have been synthesized, whose quality depends less on the number of training samples used, but much more on the cleanliness and signal-to-noise ratio of those. Generalized voice models have been used for synthesis and the results greatly differ between the two speech corpora. Tests regarding the adaptation to different speakers show that a resemblance to the original speaker is audible throughout all recordings, yet the synthesized voices sound robotic and unnatural in smaller parts. The spoken text, however, is usually intelligible, which shows that the models are working well. In a novel approach, speech is synthesized using side information of the original audio signal, particularly the pitch frequency. Results show an increase of speech quality and intelligibility in comparison to speech synthesized solely from text, up to the point of being nearly indistinguishable from the original.
Unsupervised Submodular Rank Aggregation on Score-based Permutations
Venue: arXiv cs.LG 2017
Year: 2017

Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet. This work studies the use of submodular optimization for rank aggregation on score-based permutations in an unsupervised way. Specifically, we propose an unsupervised approach based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave objective functions. In addition, stochastic optimization methods are applied in the training process and efficient algorithms for inference can be guaranteed. The experimental results from Information Retrieval, Combining Distributed Neural Networks, Influencers in Social Networks, and Distributed Automatic Speech Recognition tasks demonstrate the effectiveness of the proposed methods.
The minimal measurement number problem in phase retrieval: a review of recent developments
Venue: arXiv cs.IT 2017
Year: 2017

Phase retrieval is to recover the signals from phaseless measurements which is raised in many areas. A fundamental problem in phase retrieval is to determine the minimal measurement number $m$ so that one can recover $d$-dimensional signals from $m$ phaseless measurements. This problem attracts much attention of experts from different areas. In this paper, we review the recent development on the minimal measurement number and also raise many interesting open questions.
Random Matching under Priorities: Stability and No Envy Concepts
Venue: arXiv cs.GT 2017
Year: 2017

We consider stability concepts for random matchings where agents have preferences over objects and objects have priorities for the agents. When matchings are deterministic, the standard stability concept also captures the fairness property of no (justified) envy. When matchings can be random, there are a number of natural stability / fairness concepts that coincide with stability / no envy whenever matchings are deterministic. We formalize known stability concepts for random matchings for a general setting that allows weak preferences and weak priorities, unacceptability, and an unequal number of agents and objects. We then present a clear taxonomy of the stability concepts and identify logical relations between them. Furthermore, we provide no envy / claims interpretations for some of the stability concepts that are based on a consumption process interpretation of random matchings. Finally, we present a transformation from the most general setting to the most restricted setting, and show how almost all our stability concepts are preserved by that transformation.
Exploration of object recognition from 3D point cloud
Venue: arXiv cs.CV 2017
Year: 2017

We present our latest experiment results of object recognition from 3D point cloud data collected through moving car.
Outage Probability of Power-based Non-Orthogonal Multiple Access (NOMA) on the Uplink of a 5G Cell
Venue: arXiv cs.IT 2017
Year: 2017

This letter puts forth an analytical approach to evaluate the outage probability of power-based NOMA on the uplink of a 5G cell, the outage being defined as the event where the receiver fails to successfully decode all the simultaneously received signals. In the examined scenario, Successive Interference Cancellation (SIC) is considered and an arbitrary number of superimposed signals is present. For the Rayleigh fading case, the outage probability is provided in closed-form, clearly outlining its dependency on the signal-to-noise ratio of the users that are simultaneously transmitting, as well as on their distance from the receiver.
R-PHOC: Segmentation-Free Word Spotting using CNN
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our net- work takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.
An algorithm for optimal transport between a simplex soup and a point cloud
Venue: arXiv cs.CG 2017
Year: 2017

We propose a numerical method to find the optimal transport map between a measure supported on a lower-dimensional subset of R^d and a finitely supported measure. More precisely, the source measure is assumed to be supported on a simplex soup, i.e. on a union of simplices of arbitrary dimension between 2 and d. As in [Aurenhammer, Hoffman, Aronov, Algorithmica 20 (1), 1998, 61--76] we recast this optimal transport problem as the resolution of a non-linear system where one wants to prescribe the quantity of mass in each cell of the so-called Laguerre diagram. We prove the convergence with linear speed of a damped Newton's algorithm to solve this non-linear system. The convergence relies on two conditions: (i) a genericity condition on the point cloud with respect to the simplex soup and (ii) a (strong) connectedness condition on the support of the source measure defined on the simplex soup. Finally, we apply our algorithm in R^3 to compute optimal transport plans between a measure supported on a triangulation and a discrete measure. We also detail some applications such as optimal quantization of a probability density over a surface, remeshing or rigid point set registration on a mesh.
An Attention Mechanism for Answer Selection Using a Combined Global and Local View
Venue: arXiv cs.CL 2017
Year: 2017

We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input. Previous work focused on augmenting recurrent neural networks with simple attention mechanisms which are a function of the similarity between a question embedding and an answer embeddings across time. We extend this by making the attention mechanism dependent on a global embedding of the answer attained using a separate network. We evaluate our system on InsuranceQA, a large question answering dataset. Our model outperforms current state-of-the-art results on InsuranceQA. Further, we visualize which sections of text our attention mechanism focuses on, and explore its performance across different parameter settings.
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
Venue: arXiv cs.CV 2017
Year: 2017

We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge. In this multi-label video classification task, our pipeline achieved 84.675% and 84.662% GAP on our evaluation split and the official test set. We attribute the good performance to three components: 1) Refined video representation learning with residual links and hypercolumns 2) Latent concept mining which captures interactions among concepts. 3) Learning with temporal segments and weighted multi-model ensemble. We conduct experiments to validate and analyze the contribution of our models. We also share some unsuccessful trials leveraging conventional approaches such as recurrent neural networks for video representation learning for this large-scale video dataset. All the codes to reproduce our results are publicly available at https://github.com/Martini09/informedia-yt8m-release.