OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Approach to Implement Message Level Security in RESTful Web Services
Venue: arXiv cs.NI 2016
Year: 2016

The world is rapidly adopting RESTful web services for most of its tasks. The once popular SOAP-based web services are fast losing ground owing to this. RESTful web services are light weight services without strict message formats. RESTful web services, unlike SOAP, are capable of message transfer in any format be it XML, JSON, plain text. However, in spite of these positives, ensuring message level security in REST is a challenge. Security in RESTful web services is still largely dependent upon transport layer security. There has been some work recently towards message level security in such environments wherein the transfer of message level security metadata is done through utilising new HTTP headers. We feel, however, that any method that compromises the generality of the HTTP protocol should be avoided. In this paper, therefore, we propose two new ways of encryption that promise to ensure message level security in RESTful web services without the need for special HTTP headers. This approach works seamlessly on most famous content-types of RESTful web services: XML, JSON, HTML, plain-text and various ASCII printable content types. Further, the proposed approach removes the need for content negotiation in cases where the content comprises XML, JSON, HTML, plain-text, and ASCII printable content types and also removes the need for XML or JSON canonicalization.
Discriminative Scale Space Tracking
Venue: arXiv cs.CV 2016
Year: 2016

Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5% in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50% higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.
An Efficient Method of Partitioning High Volumes of Multidimensional Data for Parallel Clustering Algorithms
Venue: arXiv cs.AI 2016
Year: 2016

An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging. Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s. To address these issues we propose two new partitioning techniques using existing mathematical models & study their feasibility, performance (bias and partitioning speed) & possible variants in choosing initial seeds. First method uses an n dimensional hashed grid based approach which is based on mapping the points in space to a set of cubes which hashes the points. Second method uses a tree of voronoi planes where each plane corresponds to a partition. We found that grid based approach was computationally impractical, while using a tree of voronoi planes (using scalable K-Means++ initial seeds) drastically outperformed the Kd-tree tree method as dimensionality increased.
Generating Politically-Relevant Event Data
Venue: arXiv cs.CL 2016
Year: 2016

Automatically generated political event data is an important part of the social science data ecosystem. The approaches for generating this data, though, have remained largely the same for two decades. During this time, the field of computational linguistics has progressed tremendously. This paper presents an overview of political event data, including methods and ontologies, and a set of experiments to determine the applicability of deep neural networks to the extraction of political events from news text.
Finding Model-Checkable Needles in Large Source Code Haystacks: Modular Bug-Finding via Static Analysis and Dynamic Invariant Discovery
Venue: arXiv cs.SE 2016
Year: 2016

In this paper, we present a novel marriage of static and dynamic analysis. Given a large code base with many functions and a mature test suite, we propose using static analysis to find functions 1) with assertions or other evident correctness properties (e.g., array bounds requirements or pointer access) and 2) with simple enough control flow and data use to be amenable to predicate-abstraction based or bounded model checking without human intervention. Because most such functions in realistic software systems in fact rely on many input preconditions not specified by the language's type system (or annotated in any way), we propose using dynamically discovered invariants based on a program's test suite to characterize likely preconditions, in order to reduce the problem of false positives. While providing little in the way of verification, this approach may provide an additional quick and highly scalable bug-finding method for programs that are usually considered "too large to model check." We present a simple example showing that the technique can be useful for a more typically "model-checkable" code base, even in the presence of a poorly designed test suite and bad invariants.
Large-Scale Strategic Games and Adversarial Machine Learning
Venue: arXiv cs.GT 2016
Year: 2016

Decision making in modern large-scale and complex systems such as communication networks, smart electricity grids, and cyber-physical systems motivate novel game-theoretic approaches. This paper investigates big strategic (non-cooperative) games where a finite number of individual players each have a large number of continuous decision variables and input data points. Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables. In addition to these computational challenges, real-world players often have limited information about their preference parameters due to the prohibitive cost of identifying them or due to operating in dynamic online settings. The challenge of limited information is exacerbated in high dimensions and big data sets. Motivated by both computational and information limitations that constrain the direct solution of big strategic games, our investigation centers around reductions using linear transformations such as random projection methods and their effect on Nash equilibrium solutions. Specific analytical results are presented for quadratic games and approximations. In addition, an adversarial learning game is presented where random projection and sampling schemes are investigated.
Insider Threats in Emerging Mobility-as-a-Service Scenarios
Venue: arXiv cs.CR 2016
Year: 2016

Mobility as a Service (MaaS) applies the everything-as-a-service paradigm of Cloud Computing to transportation: a MaaS provider offers to its users the dynamic composition of solutions of different travel agencies into a single, consistent interface. Traditionally, transits and data on mobility belong to a scattered plethora of operators. Thus, we argue that the economic model of MaaS is that of federations of providers, each trading its resources to coordinate multi-modal solutions for mobility. Such flexibility comes with many security and privacy concerns, of which insider threat is one of the most prominent. In this paper, we follow a tiered structure --- from individual operators to markets of federated MaaS providers --- to classify the potential threats of each tier and propose the appropriate countermeasures, in an effort to mitigate the problems.
On Buffer-Aided Multiple-Access Relay Channel
Venue: arXiv cs.IT 2016
Year: 2016

The paper treats uplink scenario where M user equipments (UEs) send to a Base Station (BS), possibly via a common Relay Station (RS) that is equipped with a buffer. This is a multiple-access relay channel (MARC) aided by a buffer. We devise a protocol in which the transmission mode is selected adaptively, using the buffer at the RS in order to maximize the average system throughput. We consider the general case in which the RS and the BS can have limits on the maximal number of transmitters that can be received over the multiple access channel. In each slot there are three type possible actions: (A1) multiple UEs transmit at rates that enable BS to decode them (A2) multiple UEs transmit, the BS can only decode the messages partially, while the RS completely; (A3) RS forwards the side information to BS about the partially decoded messages, which are going to be combined and decoded entirely at the BS, while simultaneously a number of UEs sends new messages to the BS. The results show that the adaptive selection of direct and buffer-aided relay transmissions leads to significant average throughput gains.
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redun- dant information from the pre-trained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully- connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization shows that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu-CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.
Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge
Venue: arXiv cs.CV 2016
Year: 2016

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. Finally, given the recent surge of interest in this task, a competition was organized in 2015 using the newly released COCO dataset. We describe and analyze the various improvements we applied to our own baseline and show the resulting performance in the competition, which we won ex-aequo with a team from Microsoft Research, and provide an open source implementation in TensorFlow.
Land Use Classification using Convolutional Neural Networks Applied to Ground-Level Images
Venue: arXiv cs.CV 2016
Year: 2016

Land use mapping is a fundamental yet challenging task in geographic science. In contrast to land cover mapping, it is generally not possible using overhead imagery. The recent, explosive growth of online geo-referenced photo collections suggests an alternate approach to geographic knowledge discovery. In this work, we present a general framework that uses ground-level images from Flickr for land use mapping. Our approach benefits from several novel aspects. First, we address the nosiness of the online photo collections, such as imprecise geolocation and uneven spatial distribution, by performing location and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our indoor/outdoor classifier achieves state-of-the-art performance on several bench- mark datasets and approaches human-level accuracy. Second, we utilize high-level semantic image features extracted using deep learning, specifically convolutional neural net- works, which allow us to achieve upwards of 76% accuracy on a challenging eight class land use mapping problem.
Convex Program Duality, Fisher Markets, and Nash Social Welfare
Venue: arXiv cs.GT 2016
Year: 2016

We study Fisher markets and the problem of maximizing the Nash social welfare (NSW), and show several closely related new results. In particular, we obtain: -- A new integer program for the NSW maximization problem whose fractional relaxation has a bounded integrality gap. In contrast, the natural integer program has an unbounded integrality gap. -- An improved, and tight, factor 2 analysis of the algorithm of [7]; in turn showing that the integrality gap of the above relaxation is at most 2. The approximation factor shown by [7] was $2e^{1/e} \approx 2.89$. -- A lower bound of $e^{1/e}\approx 1.44$ on the integrality gap of this relaxation. -- New convex programs for natural generalizations of linear Fisher markets and proofs that these markets admit rational equilibria. These results were obtained by establishing connections between previously known disparate results, and they help uncover their mathematical underpinnings. We show a formal connection between the convex programs of Eisenberg and Gale and that of Shmyrev, namely that their duals are equivalent up to a change of variables. Both programs capture equilibria of linear Fisher markets. By adding suitable constraints to Shmyrev's program, we obtain a convex program that captures equilibria of the spending-restricted market model defined by [7] in the context of the NSW maximization problem. Further, adding certain integral constraints to this program we get the integer program for the NSW mentioned above. The basic tool we use is convex programming duality. In the special case of convex programs with linear constraints (but convex objectives), we show a particularly simple way of obtaining dual programs, putting it almost at par with linear program duality. This simple way of finding duals has been used subsequently for many other applications.
On Efficient Computation of Shortest Dubins Paths Through Three Consecutive Points
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we address the problem of computing optimal paths through three consecutive points for the curvature-constrained forward moving Dubins vehicle. Given initial and final configurations of the Dubins vehicle, and a midpoint with an unconstrained heading, the objective is to compute the midpoint heading that minimizes the total Dubins path length. We provide a novel geometrical analysis of the optimal path, and establish new properties of the optimal Dubins' path through three points. We then show how our method can be used to quickly refine Dubins TSP tours produced using state-of-the-art techniques. We also provide extensive simulation results showing the improvement of the proposed approach in both runtime and solution quality over the conventional method of uniform discretization of the heading at the mid-point, followed by solving the minimum Dubins path for each discrete heading.
Bibliographic Analysis with the Citation Network Topic Model
Venue: arXiv cs.DL 2016
Year: 2016

Bibliographic analysis considers author's research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We propose a novel and efficient inference algorithm for the model to explore subsets of research publications from CiteSeerX. Our model demonstrates improved performance in both model fitting and a clustering task compared to several baselines.
Minkowski games
Venue: arXiv cs.GT 2016
Year: 2016

We introduce and study Minkowski games. These are two player games, where the players take turns to chose positions in $\mathbb{R}^d$ based on some rules. Variants include boundedness games, where one player wants to keep the positions bounded, and the other wants to escape to infinity; as well as safety games, where one player wants to stay within a prescribed set, while the other wants to leave it. We provide some general characterizations of which player can win such games, and explore the computational complexity of the associated decision problems. A natural representation of boundedness games yields coNP-completeness, whereas the safety games are undecidable.
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
Venue: arXiv cs.CL 2016
Year: 2016

Textual information is considered as significant supplement to knowledge representation learning (KRL). There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically select those informative sentences of the corresponding entities for KRL. In this paper, we propose the Sequential Text-embodied Knowledge Representation Learning to build knowledge representations from multiple sentences. Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the entity. Then we further design an attention model to measure the informativeness of each sentence, and build text-based representations of entities. We evaluate our method on two tasks, including triple classification and link prediction. Experimental results demonstrate that our method outperforms other baselines on both tasks, which indicates that our method is capable of selecting informative sentences and encoding the textual information well into knowledge representations.
On the (im)possibility of fairness
Venue: arXiv cs.CY 2016
Year: 2016

What does it mean for an algorithm to be fair? Different papers use different notions of algorithmic fairness, and although these appear internally consistent, they also seem mutually incompatible. We present a mathematical setting in which the distinctions in previous papers can be made formal. In addition to characterizing the spaces of inputs (the "observed" space) and outputs (the "decision" space), we introduce the notion of a construct space: a space that captures unobservable, but meaningful variables for the prediction. We show that in order to prove desirable properties of the entire decision-making process, different mechanisms for fairness require different assumptions about the nature of the mapping from construct space to decision space. The results in this paper imply that future treatments of algorithmic fairness should more explicitly state assumptions about the relationship between constructs and observations.
A short review and primer on eye tracking in human computer interaction applications
Venue: arXiv cs.HC 2016
Year: 2016

The application of psychophysiologicy in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. Eye tracking is a widely used method for tracking user attention with gaze location, but also provides information on the internal cognitive and contextual state, intention, and the locus of the user's visual attention in interactive settings through a number of eye and eyelid movement related parameters. This paper presents a short review on the application of eye tracking in human-computer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.
Alternative Form of Predictor Based Identification of LPV-SS Models with Innovation Noise
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we present an approach to identify linear parameter-varying (LPV) systems with a state-space (SS) model structure in an innovation form where the coefficient functions have static and affine dependency on the scheduling signal. With this scheme, the curse of dimensionality problem is reduced, compared to existing predictor based LPV subspace identification schemes. The investigated LPV-SS model is reformulated into an equivalent impulse response form, which turns out to be a moving average with exogenous inputs (MAX) system. The Markov coefficient functions of the LPV-MAX representation are multi-linear in the scheduling signal and its time-shifts, contrary to the predictor based schemes where the corresponding LPV auto-regressive with exogenous inputs system is multi-quadratic in the scheduling signal and its time-shifts. In this paper, we will prove that under certain conditions on the input and scheduling signals, the $\ell_2$ loss function of the one-step-ahead prediction error for the LPV-MAX model has only one unique minimum, corresponding to the original underlying system. Hence, identifying the LPV-MAX model in the prediction error minimization framework will be consistent and unbiased. The LPV-SS model is realized by applying an efficient basis reduced Ho-Kalman realization on the identified LPV-MAX model. The performance of the proposed scheme is assessed on a Monte Carlo simulation study.
Modeling and simulation of non-linear and hysteresis behavior of magneto-rheological dampers in the example of quarter-car model
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents reviews of several models and numerical simulation models of non-linear and hysteresis behaviors of magneto-rheological liquid dampers in MATLAB/Simulink in the example of quarter-car model of vehicle suspension simulation, such as, Bingham, Dahl, LuGre and Bouc-Wen models. In addition, it demonstrates numerical simulation models built in MATLAB/Simulink and discusses results from numerical simulation models for two different input excitations from terrain.