OpenRaiser/Intern-Atlas huggingface dataset:
Mobile-D: An Agile Approach for Mobile Application Development
Venue: arXiv cs.SE 2017
Year: 2017

Mobile phones have been closed environments until recent years. The change brought by open platform technologies such as the Symbian operating system and Java technologies has opened up a significant business opportunity for anyone to develop application software such as games for mobile terminals. However, developing mobile applications is currently a challenging task due to the specific demands and technical constraints of mobile development. Furthermore, at the moment very little is known about the suitability of the different development processes for mobile application development. Due to these issues, we have developed an agile development approach called Mobile-D. The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed.
A modularity based spectral method for simultaneous community and anti-community detection
Venue: arXiv cs.SI 2017
Year: 2017

In a graph or complex network, communities and anti-communities are node sets whose modularity attains extremely large values, positive and negative, respectively. We consider the simultaneous detection of communities and anti-communities, by looking at spectral methods based on various matrix-based definitions of the modularity of a vertex set. Invariant subspaces associated to extreme eigenvalues of these matrices provide indications on the presence of both kinds of modular structure in the network. The localization of the relevant invariant subspaces can be estimated by looking at particular matrix angles based on Frobenius inner products.
REACT to Cyber Attacks on Power Grids
Venue: arXiv cs.SY 2017
Year: 2017

Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control center. In particular, we assume that an adversary attacks an area by: (i) remotely disconnecting some lines within the attacked area, and (ii) modifying the information received from the attacked area to mask the line failures and hide the attacked area from the control center. For the latter, we consider two types of attacks: (i) data distortion: which distorts the data by adding powerful noise to the actual data, and (ii) data replay: which replays a locally consistent old data instead of the actual data. We use the DC power flow model and prove that the problem of finding the set of line failures given the phase angles of the nodes outside of the attacked area is strongly NP-hard, even when the attacked area is known. However, we introduce the polynomial time REcurrent Attack Containment and deTection (REACT) Algorithm to approximately detect the attacked area and line failures after a cyber attack. We numerically show that it performs very well in detecting the attacked area, and detecting single, double, and triple line failures in small and large attacked areas.
Radio Irregularity Model in OMNeT++
Venue: arXiv cs.NI 2017
Year: 2017

Radio irregularity is a non-negligible phenomenon that has an impact on protocol performances. For instance, irregularity in radio range leads to asymmetric links that cause the loss of packets in different directions. In order to investigate its effect, the Radio Irregularity Model (RIM) is proposed that takes into account the irregularity of a radio range and estimates path losses in an anisotropic environment. The purpose of this paper is to provide details of the RIM model developed in the INET Framework of the OMNeT++ simulator that can be used to investigate the impact of radio irregularity on protocol performance.
Multi-camera Multi-Object Tracking
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a pipeline for multi-target visual tracking under multi-camera system. For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking. However, most of the multi-camera tracking algorithms emphasis on single camera across frame data association. Thus in our work, we model our tracking problem as a global graph, and adopt Generalized Maximum Multi Clique optimization problem as our core algorithm to take both across frame and across camera data correlation into account all together. Furthermore, in order to compute good similarity scores as the input of our graph model, we extract both appearance and dynamic motion similarities. For appearance feature, Local Maximal Occurrence Representation(LOMO) feature extraction algorithm for ReID is conducted. When it comes to capturing the dynamic information, we build Hankel matrix for each tracklet of target and apply rank estimation with Iterative Hankel Total Least Squares(IHTLS) algorithm to it. We evaluate our tracker on the challenging Terrace Sequences from EPFL CVLAB as well as recently published Duke MTMC dataset.
Automatic Detection of Malware-Generated Domains with Recurrent Neural Models
Venue: arXiv cs.CR 2017
Year: 2017

Modern malware families often rely on domain-generation algorithms (DGAs) to determine rendezvous points to their command-and-control server. Traditional defence strategies (such as blacklisting domains or IP addresses) are inadequate against such techniques due to the large and continuously changing list of domains produced by these algorithms. This paper demonstrates that a machine learning approach based on recurrent neural networks is able to detect domain names generated by DGAs with high precision. The neural models are estimated on a large training set of domains generated by various malwares. Experimental results show that this data-driven approach can detect malware-generated domain names with a F_1 score of 0.971. To put it differently, the model can automatically detect 93 % of malware-generated domain names for a false positive rate of 1:100.
Deconvolutional Latent-Variable Model for Text Sequence Matching
Venue: arXiv cs.CL 2017
Year: 2017

A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing learned latent codes with more semantic information and better generalization. Our model, trained in an unsupervised manner, yields stronger empirical predictive performance than a decoder based on Long Short-Term Memory (LSTM), with less parameters and considerably faster training. Further, we apply it to text sequence-matching problems. The proposed model significantly outperforms several strong sentence-encoding baselines, especially in the semi-supervised setting.
Semi-Automated Nasal PAP Mask Sizing using Facial Photographs
Venue: arXiv cs.CV 2017
Year: 2017

We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The semi-automated system performed comparably to sizing from manual measurements taken from the same images which produced 89% and 100% accuracy respectively.
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
Venue: arXiv cs.CV 2017
Year: 2017

This paper addresses the question of emotion classification. The task consists in predicting emotion labels (taken among a set of possible labels) best describing the emotions contained in short video clips. Building on a standard framework -- lying in describing videos by audio and visual features used by a supervised classifier to infer the labels -- this paper investigates several novel directions. First of all, improved face descriptors based on 2D and 3D Convo-lutional Neural Networks are proposed. Second, the paper explores several fusion methods, temporal and multimodal, including a novel hierarchical method combining features and scores. In addition, we carefully reviewed the different stages of the pipeline and designed a CNN architecture adapted to the task; this is important as the size of the training set is small compared to the difficulty of the problem, making generalization difficult. The so-obtained model ranked 4th at the 2017 Emotion in the Wild challenge with the accuracy of 58.8 %.
3D Deformable Object Manipulation using Fast Online Gaussian Process Regression
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we present a general approach to automatically visual-servo control the position and shape of a deformable object whose deformation parameters are unknown. The servo-control is achieved by online learning a model mapping between the robotic end-effector's movement and the object's deformation measurement. The model is learned using the Gaussian Process Regression (GPR) to deal with its highly nonlinear property, and once learned, the model is used for predicting the required control at each time step. To overcome GPR's high computational cost while dealing with long manipulation sequences, we implement a fast online GPR by selectively removing uninformative observation data from the regression process. We validate the performance of our controller on a set of deformable object manipulation tasks and demonstrate that our method can achieve effective and accurate servo-control for general deformable objects with a wide variety of goal settings. Experiment videos are available at https://sites.google.com/view/mso-fogpr
Assumption-Based Approaches to Reasoning with Priorities
Venue: arXiv cs.AI 2017
Year: 2017

This paper maps out the relation between different approaches for handling preferences in argumentation with strict rules and defeasible assumptions by offering translations between them. The systems we compare are: non-prioritized defeats i.e. attacks, preference-based defeats, and preference-based defeats extended with reverse defeat.
Secure Energy Efficiency Optimization for MISO Cognitive Radio Network with Energy Harvesting
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates a secure energy efficiency (SEE) optimization problem in a multiple-input single-output (MISO) underlay cognitive radio (CR) network. In particular, a multi-antenna secondary transmitter (SU-Tx) simultaneously sends secured information and energy to a secondary receiver (SU-Rx) and an energy receiver (ER), respectively, in the presence of a primary receiver (PU-Rx). It is assumed that the SU-Rx, ER and PU-Rx are each equipped with a single antenna. In addition, the SU-Tx should satisfy constraints on maximum interference leakage to the PU-Rx and minimum harvested energy at the ER. In this CR network, we consider the transmit covariance matrix design with the assumption of perfect channel state information (CSI) at the SU-Tx. In addition, it is assumed that the ER is a potential passive eavesdropper due to broadcast nature of wireless transmission. On the other hand, we consider the worst-case scenario that ER's energy harvesting requirement is only satisfied when it performs only energy harvesting without intercepting or eavesdropping information intended for the SU-Rx. We formulate this transmit covariance matrix design as a SEE maximization problem which is a non-convex problem due the non-linear fractional objective function. To realize the solution for this non-convex problem, we utilize the non-linear fractional programming and difference of concave (DC) functions approaches to reformulate into a tractable form. Based on these techniques and the Dinkelbach's method, we propose iterative algorithms to determine the solution for the original SEE maximization problem. Numerical simulation results are provided to demonstrate the performance of the proposed transmit covariance matrix design and convergence of the proposed algorithms.
Constant Bearing Pursuit on Branching Graphs
Venue: arXiv cs.SY 2017
Year: 2017

Cyclic pursuit frameworks provide an efficient way to create useful global behaviors out of pairwise interactions in a collective of autonomous robots. Earlier work studied cyclic pursuit with a constant bearing (CB) pursuit law, and has demonstrated the existence of a variety of interesting behaviors for the corresponding dynamics. In this work, by attaching multiple branches to a single cycle, we introduce a modified version of this framework which allows us to consider any weakly connected pursuit graph where each node has an outdegree of 1. This provides a further generalization of the cyclic pursuit setting. Then, after showing existence of relative equilibria (rectilinear or circling motion), pure shape equilibria (spiraling motion) and periodic orbits, we also derive necessary conditions for stability of a 3-agent collective. By paving a way for individual agents to join or leave a collective without perturbing the motion of others, our approach leads to improved reliability of the overall system.
Analysis of Big Data Maturity Stage in Hospitality Industry
Venue: arXiv cs.CY 2017
Year: 2017

Big data analytics has an extremely significant impact on many areas in all businesses and industries including hospitality. This study aims to guide information technology (IT) professionals in hospitality on their big data expedition. In particular, the purpose of this study is to identify the maturity stage of the big data in hospitality industry in an objective way so that hotels be able to understand their progress, and realize what it will take to get to the next stage of big data maturity through the scores they will receive based on the survey.
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings
Venue: arXiv cs.CL 2017
Year: 2017

Mining suggestion expressing sentences from a given text is a less investigated sentence classification task, and therefore lacks hand labeled benchmark datasets. In this work, we propose and evaluate two approaches for distant supervision in suggestion mining. The distant supervision is obtained through a large silver standard dataset, constructed using the text from wikiHow and Wikipedia. Both the approaches use a LSTM based neural network architecture to learn a classification model for suggestion mining, but vary in their method to use the silver standard dataset. The first approach directly trains the classifier using this dataset, while the second approach only learns word embeddings from this dataset. In the second approach, we also learn POS embeddings, which interestingly gives the best classification accuracy.
Robust Optimization of Unconstrained Binary Quadratic Problems
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers. Experimental design techniques are used to generate a diverse subset of possible scenarios, from which robust solutions are identified. An illustrative example with practical application to business decision making is examined. The approach presented also generates a surface response equation which is used to estimate upper bounds in constant time for Q instantiations within the scenario extremes. In addition, a theoretical framework for the robustness of individual x_i variables is considered by examining the range of Q values over which the x_i are predetermined.
Attention-based Mixture Density Recurrent Networks for History-based Recommendation
Venue: arXiv cs.LG 2017
Year: 2017

The goal of personalized history-based recommendation is to automatically output a distribution over all the items given a sequence of previous purchases of a user. In this work, we present a novel approach that uses a recurrent network for summarizing the history of purchases, continuous vectors representing items for scalability, and a novel attention-based recurrent mixture density network, which outputs each component in a mixture sequentially, for modelling a multi-modal conditional distribution. We evaluate the proposed approach on two publicly available datasets, MovieLens-20M and RecSys15. The experiments show that the proposed approach, which explicitly models the multi-modal nature of the predictive distribution, is able to improve the performance over various baselines in terms of precision, recall and nDCG.
On self-dual four circulant codes
Venue: arXiv cs.IT 2017
Year: 2017

Four circulant codes form a special class of $2$-generator, index $4$, quasi-cyclic codes. Under some conditions on their generator matrices they can be shown to be self-dual. Artin primitive root conjecture shows the existence of an infinite subclass of these codes satisfying a modified Gilbert-Varshamov bound.
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Venue: arXiv cs.CV 2017
Year: 2017

Image retargeting effectively resizes images by preserving the recognizability of important image regions. Most of retargeting methods rely on good importance maps as a cue to retain or remove certain regions in the input image. In addition, the traditional evaluation exhaustively depends on user ratings. There is a legitimate need for a methodological approach for evaluating retargeted results. Therefore, in this paper, we conduct a study and analysis on the prominent method in image retargeting, Seam Carving. First, we introduce two novel evaluation metrics which can be considered as the proxy of user ratings. Second, we exploit salient object dataset as a benchmark for this task. We then investigate different types of importance maps for this particular problem. The experiments show that humans in general agree with the evaluation metrics on the retargeted results and some importance map methods are consistently more favorable than others.
Inverse Reinforcement Learning with Conditional Choice Probabilities
Venue: arXiv cs.AI 2017
Year: 2017

We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem. Using the language of structural econometrics, we re-frame the optimal decision problem and introduce an alternative representation of value functions due to (Hotz and Miller 1993). In addition to presenting the theoretical connections that bridge the IRL literature between Economics and Robotics, the use of CCPs also has the practical benefit of reducing the computational cost of solving the IRL problem. Specifically, under the CCP representation, we show how one can avoid repeated calls to the dynamic programming subroutine typically used in IRL. We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.
Stochastic Input Models in Online Computing
Venue: arXiv cs.DS 2017
Year: 2017

In this paper, we study twelve stochastic input models for online problems and reveal the relationships among the competitive ratios for the models. The competitive ratio is defined as the worst ratio between the expected optimal value and the expected profit of the solution obtained by the online algorithm where the input distribution is restricted according to the model. To handle a broad class of online problems, we use a framework called request-answer games that is introduced by Ben-David et al. The stochastic input models consist of two types: known distribution and unknown distribution. For each type, we consider six classes of distributions: dependent distributions, deterministic input, independent distributions, identical independent distribution, random order of a deterministic input, and random order of independent distributions. As an application of the models, we consider two basic online problems, which are variants of the secretary problem and the prophet inequality problem, under the twelve stochastic input models. We see the difference of the competitive ratios through these problems.
Directable Fuzzy and Nondeterministic Automata
Venue: arXiv cs.FL 2017
Year: 2017

We study three notions of directability of fuzzy automata akin to the D1-, D2- and D3-directability of nondeterministic automata. Thus an input word $w$ of a fuzzy automaton is D1-directing if a fixed single state is reachable by $w$ from all states, D2-directing if exactly the same states are reachable by $w$ from every state, and D3-directing if there is a state reachable by $w$ from every state. We study the various sets of directing words of fuzzy automata, prove that the directability properties are decidable, and show how such results can be deduced from the theory of directable nondeterministic automata. Moreover, we establish the closure properties of the different classes of directable fuzzy automata under the class operations of forming subautomata, homomorphic images and finite direct products.
Can We Boost the Power of the Viola-Jones Face Detector Using Pre-processing? An Empirical Study
Venue: arXiv cs.CV 2017
Year: 2017

The Viola-Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola-Jones algorithm because of its simplicity. In this paper, we study the influence of a set of blind pre-processing methods on the face detection rate using the Viola-Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind pre-processing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind pre-processing method that may hurt the face detector. The second set is performed to study the effect of the selected pre-processing methods on images that suffer from hard conditions. We present two manners of applying the pre-processing method to the image prior to being used by the Viola-Jones face detector. Four different datasets are used to draw a coherent conclusion about the potential improvement caused by using prior enhanced images. The results demonstrate that some of the pre-processing methods may hurt the accuracy of Viola-Jones face detection algorithm. However, other pre-processing methods have an evident positive impact on the accuracy of the face detector. Overall, we recommend three simple and fast blind photometric normalization methods as a pre-processing step in order to improve the accuracy of the pre-trained Viola-Jones face detector.