OpenRaiser/Intern-Atlas huggingface dataset:
Defeating Image Obfuscation with Deep Learning
Venue: arXiv cs.CR 2016
Year: 2016

We demonstrate that modern image recognition methods based on artificial neural networks can recover hidden information from images protected by various forms of obfuscation. The obfuscation techniques considered in this paper are mosaicing (also known as pixelation), blurring (as used by YouTube), and P3, a recently proposed system for privacy-preserving photo sharing that encrypts the significant JPEG coefficients to make images unrecognizable by humans. We empirically show how to train artificial neural networks to successfully identify faces and recognize objects and handwritten digits even if the images are protected using any of the above obfuscation techniques.
A deep learning model for estimating story points
Venue: arXiv cs.SE 2016
Year: 2016

Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the \emph{first} time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is \emph{end-to-end} trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.
Beyond Highway Dimension: Small Distance Labels Using Tree Skeletons
Venue: arXiv cs.DS 2016
Year: 2016

The goal of a hub-based distance labeling scheme for a network G = (V, E) is to assign a small subset S(u) $\subseteq$ V to each node u $\in$ V, in such a way that for any pair of nodes u, v, the intersection of hub sets S(u) $\cap$ S(v) contains a node on the shortest uv-path. The existence of small hub sets, and consequently efficient shortest path processing algorithms, for road networks is an empirical observation. A theoretical explanation for this phenomenon was proposed by Abraham et al. (SODA 2010) through a network parameter they called highway dimension, which captures the size of a hitting set for a collection of shortest paths of length at least r intersecting a given ball of radius 2r. In this work, we revisit this explanation, introducing a more tractable (and directly comparable) parameter based solely on the structure of shortest-path spanning trees, which we call skeleton dimension. We show that skeleton dimension admits an intuitive definition for both directed and undirected graphs, provides a way of computing labels more efficiently than by using highway dimension, and leads to comparable or stronger theoretical bounds on hub set size.
Pairwise, Magnitude, or Stars: What's the Best Way for Crowds to Rate?
Venue: arXiv cs.IR 2016
Year: 2016

We compare three popular techniques of rating content: the ubiquitous five star rating, the less used pairwise comparison, and the recently introduced (in crowdsourcing) magnitude estimation approach. Each system has specific advantages and disadvantages, in terms of required user effort, achievable user preference prediction accuracy and number of ratings required. We design an experiment where the three techniques are compared in an unbiased way. We collected 39'000 ratings on a popular crowdsourcing platform, allowing us to release a dataset that will be useful for many related studies on user rating techniques.
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off
Venue: arXiv cs.CG 2016
Year: 2016

Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges ("local bundles"), or also between the weakly bundled edges occurring globally over the graph ("global bundles"); we allow a user-set global-local tradeoff. We call the technique "peacock bundles". Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.
A heuristic extending the Squarified treemapping algorithm
Venue: arXiv cs.HC 2016
Year: 2016

A heuristic extending the Squarified Treemap technique for the representation of hierarchical information as treemaps is presented. The original technique gives high quality treemap views, since items are laid out with rectangles that approximate squares, allowing easy comparison and selection operations. New key steps, with a low computational impact, have been introduced to yield treemaps with even better aspect ratios and higher homogeneity among items.
Greedy MAXCUT Algorithms and their Information Content
Venue: arXiv cs.DS 2016
Year: 2016

MAXCUT defines a classical NP-hard problem for graph partitioning and it serves as a typical case of the symmetric non-monotone Unconstrained Submodular Maximization (USM) problem. Applications of MAXCUT are abundant in machine learning, computer vision and statistical physics. Greedy algorithms to approximately solve MAXCUT rely on greedy vertex labelling or on an edge contraction strategy. These algorithms have been studied by measuring their approximation ratios in the worst case setting but very little is known to characterize their robustness to noise contaminations of the input data in the average case. Adapting the framework of Approximation Set Coding, we present a method to exactly measure the cardinality of the algorithmic approximation sets of five greedy MAXCUT algorithms. Their information contents are explored for graph instances generated by two different noise models: the edge reversal model and Gaussian edge weights model. The results provide insights into the robustness of different greedy heuristics and techniques for MAXCUT, which can be used for algorithm design of general USM problems.
Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison
Venue: arXiv cs.CV 2016
Year: 2016

Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification.
An Online Universal Classifier for Binary, Multi-class and Multi-label Classification
Venue: arXiv cs.LG 2016
Year: 2016

Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification. In this paper, a novel online universal classifier capable of performing all the three types of classification is proposed. Being a high speed online classifier, the proposed technique can be applied to streaming data applications. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.
A Robust Sparse Fourier Transform in the Continuous Setting
Venue: arXiv cs.DS 2016
Year: 2016

In recent years, a number of works have studied methods for computing the Fourier transform in sublinear time if the output is sparse. Most of these have focused on the discrete setting, even though in many applications the input signal is continuous and naive discretization significantly worsens the sparsity level. We present an algorithm for robustly computing sparse Fourier transforms in the continuous setting. Let $x(t) = x^*(t) + g(t)$, where $x^*$ has a $k$-sparse Fourier transform and $g$ is an arbitrary noise term. Given sample access to $x(t)$ for some duration $T$, we show how to find a $k$-Fourier-sparse reconstruction $x'(t)$ with $$\frac{1}{T}\int_0^T |x'(t) - x(t) |^2 \mathrm{d} t \lesssim \frac{1}{T}\int_0^T | g(t)|^2 \mathrm{d}t.$$ The sample complexity is linear in $k$ and logarithmic in the signal-to-noise ratio and the frequency resolution. Previous results with similar sample complexities could not tolerate an infinitesimal amount of i.i.d. Gaussian noise, and even algorithms with higher sample complexities increased the noise by a polynomial factor. We also give new results for how precisely the individual frequencies of $x^*$ can be recovered.
CNSMO: A Network Services Manager/Orchestrator Tool for Cloud Federated Environments
Venue: arXiv cs.NI 2016
Year: 2016

Application service providers (ASPs) now develop, deploy, and maintain complex computing platforms within multiple cloud infrastructures to improve resilience, responsiveness and elasticity of their applications. On the other hand, complex applications have little control and visibility over network resources, and need to use low-level hacks to extract network properties and prioritize traffic. This biased view, limits tenants flexibility while deploying their applications and prevents them from implementing part of the application logic in the network. In this paper, we propose the CNSMO (CYCLONE Network Services Manager/Orchestrator) tool to bring the innovation at federated cloud environments by bridging these network service capabilities to cloud based services as part of the overall CYCLONE solution. The integration of networking aspects with purely federated clouds, will allow users to request specific infrastructures and manage their dedicated set of coordinated network and IT resources in an easy and transparent way while operating dynamic deployments of distributed applications.
Multi-Cell Massive MIMO Performance with Double Scattering Channels
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates the spectral efficiency (SE) of multi-cell Massive Multi-Input Multi-Output (MIMO) using different channel models. Prior works have derived closed-form SE bounds and approximations for Gaussian distributed channels, while we consider the double scattering model-a prime example of a non-Gaussian channel for which it is intractable to obtain closed form SE expressions. The channels are estimated using limited resources, which gives rise to pilot contamination, and the estimates are used for linear detection and to compute the SE numerically. Analytical and numerical examples are used to describe the key behaviors of the double scattering models, which differ from conventional Massive MIMO models. Finally, we provide multi-cell simulation results that compare the double scattering model with uncorrelated Rayleigh fading and explain under what conditions we can expect to achieve similar SEs.
Proposal of Data Processing Platform for Direct Marketing Data
Venue: arXiv cs.CY 2016
Year: 2016

Data mining has been widely used to identify potential customers for a new product or service. In this article is done a study of previous work relating to the application of data mining methodologies for software projects, specifically for direct marketing projects. Several data sets of demographic and historical customer purchases data available for evaluation of algorithms in this area, some of them very new and current are described. The main contribution of this paper is the proposal of a platform for distributed data stream processing for the processes of targeting customers and building predictive models required response; thus facilitating several of the functional requirements for development environments.
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learning appears as a prominent solution. In this report we aim to clarify how transfer learning schemes may influence classification results. We are particularly focused in the automated melanoma screening problem, a case of medical imaging in which transfer learning is still not widely used. We explored transfer with and without fine-tuning, sequential transfers and usage of pre-trained models in general and specific datasets. Although some issues remain open, our findings may drive future researches.
Implementation of Electrical Feedback Technologies in 5 Households in Ankara Turkey
Venue: arXiv cs.HC 2016
Year: 2016

Most people do not know how much energy they are spending for different purposes and also they are unaware of potential electrical consumption reduction level they could make by changing their consumption behaviors or investing in new efficient technology products. Providing electrical consumption information to household and make the consumption information tangible is very effective strategy in reducing electrical consumption in households. In Home Displays provide consumers real time information on electrical consumption level and related expenditure, with that strategy turning an intangible electric bill into transparent and controllable is possible. We interviewed the participants before the start to our study in order to have knowledge of their income, environmental attitudes and the knowledge level of their electrical consumption conserving activities. Monitoring over 3 to 5 months maximal of five households electrical consumption and comparing that consumption level with the previous year had been realized. Households received advanced real time appliance level feedback. Behavioral change associated electrical consumption reduction levels were identified. It is also realized that setting goals via an electrical energy consumption display have high potential on reducing the total electricity consumption in households. We found 0, 0, 13.21, 3.42, 1.59 % electricity savings on house 1, house 2, house 3, house 4 and house 5 correspondingly. It is suggested that further attention need to be given on potential reduction effect of applying different type of monitor designs and implication of special designed stimulants that can be used all over the house that can alert households with their current consumption level. Also, it is suggested to concentrate on time periods in order to analyze and try to reveal specific time periods for specific consumption behaviors.
Consensus of Dependent Opinions
Venue: arXiv cs.HC 2016
Year: 2016

Providing opinions through labeling of images, tweets, etc. have drawn immense interest in crowdsourcing markets. This invokes a major challenge of aggregating multiple opinions received from different crowd workers for deriving the final judgment. Generally, opinion aggregation models deal with independent opinions, which are given unanimously and are not visible to all. However, in many real-life cases, it is required to make the opinions public as soon as they are received. This makes the opinions dependent and might incorporate some bias. In this paper, we address a novel problem, hereafter denoted as dependent judgment analysis, and discuss the requirements for developing an appropriate model to deal with this problem. The challenge remains to be improving the consensus by revealing true opinions.
A Framework for Scientific Paper Retrieval and Recommender Systems
Venue: arXiv cs.IR 2016
Year: 2016

Information retrieval (IR) and recommender systems (RS) have been employed for addressing search tasks executed during literature review and the overall scholarly communication lifecycle. Majority of the studies have concentrated on algorithm design for improving the accuracy and usefulness of these systems. Contextual elements related to the scholarly tasks have been largely ignored. In this paper, we propose a framework called the Scientific Paper Recommender and Retrieval Framework (SPRRF) that combines aspects of user role modeling and user-interface features with IR/RS components. The framework is based on eight emergent themes identified from participants feedback in a user evaluation study conducted with a prototype assistive system. 119 researchers participated in the study for evaluating the prototype system that provides recommendations for two literature review and one manuscript writing tasks. This holistic framework is meant to guide future studies in this area.
Hub Identification of the Metro Manila Road Network Using PageRank
Venue: arXiv cs.SI 2016
Year: 2016

We attempt to identify the different node hubs of a road network using PageRank for preparation for possible random terrorist attacks. The robustness of a road network against such attack is crucial to be studied because it may cripple its connectivity by simply shutting down these hubs. We show the important hubs in a road network based on network structure and propose a model for robustness analysis. By identifying important hubs in a road network, possible preparation schemes may be done earlier to mitigate random terrorist attacks, including defense reinforcement and transportation security. A case study of the Metro Manila road network is also presented. The case study shows that the most important hubs in the Metro Manila road network are near airports, piers, major highways and expressways
Multi Exit Configuration of Mesoscopic Pedestrian Simulation
Venue: arXiv cs.MA 2016
Year: 2016

A mesoscopic approach to modeling pedestrian simulation with multiple exits is proposed in this paper. A floor field based on Qlearning Algorithm is used. Attractiveness of exits to pedestrian typically is based on shortest path. However, several factors may influence pedestrian choice of exits. Scenarios with multiple exits are presented and effect of Q-learning rewards system on navigation is investigated
Low-rank Bandits with Latent Mixtures
Venue: arXiv cs.LG 2016
Year: 2016

We study the task of maximizing rewards from recommending items (actions) to users sequentially interacting with a recommender system. Users are modeled as latent mixtures of C many representative user classes, where each class specifies a mean reward profile across actions. Both the user features (mixture distribution over classes) and the item features (mean reward vector per class) are unknown a priori. The user identity is the only contextual information available to the learner while interacting. This induces a low-rank structure on the matrix of expected rewards r a,b from recommending item a to user b. The problem reduces to the well-known linear bandit when either user or item-side features are perfectly known. In the setting where each user, with its stochastically sampled taste profile, interacts only for a small number of sessions, we develop a bandit algorithm for the two-sided uncertainty. It combines the Robust Tensor Power Method of Anandkumar et al. (2014b) with the OFUL linear bandit algorithm of Abbasi-Yadkori et al. (2011). We provide the first rigorous regret analysis of this combination, showing that its regret after T user interactions is $\tilde O(C\sqrt{BT})$, with B the number of users. An ingredient towards this result is a novel robustness property of OFUL, of independent interest.
A Hardware-Efficient Approach to Computing the Rotation Matrix from a Quaternion
Venue: arXiv cs.AR 2016
Year: 2016

In this paper, we have proposed a novel VLSI-oriented approach to computing the rotation matrix entries from the quaternion coefficients. The advantage of this approach is the complete elimination of multiplications and replacing them by less costly squarings. Our approach uses Logan's identity, which proposes to replace the calculation of the product of two numbers on summing the squares via the Binomial Theorem. Replacing multiplications by squarings implies reducing power consumption as well as decreases hardware circuit complexity.
Modeling The Adaption Rule in Context-aware Systems
Venue: arXiv cs.HC 2016
Year: 2016

Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing systems. Each context-aware application has its own set of behaviors to react to context modifications. This paper is concerned with the context modeling and the development methodology for context-aware systems. We proposed a rule-based approach and use the adaption tree to model the adaption rule of context-aware systems. We illustrate this idea in an arithmetic game application.
Private Link Exchange over Social Graphs
Venue: arXiv cs.SI 2016
Year: 2016

Currently, most of the online social networks (OSN) keep their data secret and in centralized manner. Researchers are allowed to crawl the underlying social graphs (and data) but with limited rates, leading to only partial views of the true social graphs. To overcome this constraint, we may start from user perspective, the contributors of the OSNs. More precisely, if users cautiously collaborate with one another, they can use the very infrastructure of the OSNs to exchange noisy friend lists with their neighbors in several rounds. In the end, they can build local subgraphs, also called local views of the true social graph. In this paper, we propose such protocols for the problem of \textit{private link exchange} over social graphs. The problem is unique in the sense that the disseminated data over the links are the links themselves. However, there exist fundamental questions about the feasibility of this model. The first question is how to define simple and effective privacy concepts for the link exchange processes. The second question comes from the high volume of link lists in exchange which may increase exponentially round after round. While storage and computation complexity may be affordable for desktop PCs, communication costs are non-trivial. We address both questions by a simple $(α,β)$-exchange using Bloom filters.
Robust Computation of a Minimum Area Convex Polygon Stabber of a Set of Isothetic Line Segments
Venue: arXiv cs.CG 2016
Year: 2016

In this paper, we discuss the algorithm engineering aspects of an O(n^2)-time algorithm [6] for computing a minimum-area convex polygon that intersects a set of n isothetic line segments.