OpenRaiser/Intern-Atlas huggingface dataset:
Application of Multi factor authentication in Internet of Things domain
Venue: arXiv cs.CR 2015
Year: 2015

Authentication forms the gateway to any secure system. Together with integrity, confidentiality and authorization it helps in preventing any sort of intrusions into the system. Up until a few years back password based authentication was the most common form of authentication to any secure network. But with the advent of more sophisticated technologies this form of authentication although still widely used has become insecure. Furthermore, with the rise of 'Internet of Things' where the number of devices would grow manifold it would be infeasible for user to remember innumerable passwords. Therefore, it's important to address this concern by devising ways in which multiple forms of authentication would be required to gain access to any smart devices and at the same time its usability would be high. In this paper, a methodology is discussed as to what kind of authentication mechanisms could be deployed in internet of things (IOT).
Entity-Specific Sentiment Classification of Yahoo News Comments
Venue: arXiv cs.CL 2015
Year: 2015

Sentiment classification is widely used for product reviews and in online social media such as forums, Twitter, and blogs. However, the problem of classifying the sentiment of user comments on news sites has not been addressed yet. News sites cover a wide range of domains including politics, sports, technology, and entertainment, in contrast to other online social sites such as forums and review sites, which are specific to a particular domain. A user associated with a news site is likely to post comments on diverse topics (e.g., politics, smartphones, and sports) or diverse entities (e.g., Obama, iPhone, or Google). Classifying the sentiment of users tied to various entities may help obtain a holistic view of their personality, which could be useful in applications such as online advertising, content personalization, and political campaign planning. In this paper, we formulate the problem of entity-specific sentiment classification of comments posted on news articles in Yahoo News and propose novel features that are specific to news comments. Experimental results show that our models outperform state-of-the-art baselines.
Complete relations on fuzzy complete lattices
Venue: arXiv cs.LO 2015
Year: 2015

We generalize the notion of complete binary relation on complete lattice to residuated lattice valued ordered sets and show its properties. Then we focus on complete fuzzy tolerances on fuzzy complete lattices and prove they are in one-to-one correspondence with extensive isotone Galois connections. Finally, we prove that fuzzy complete lattice, factorized by a complete fuzzy tolerance, is again a fuzzy complete lattice.
Efficient algorithm for computing large scale systems of differential algebraic equations
Venue: arXiv cs.NA 2015
Year: 2015

In many mathematical models of physical phenomenons and engineering fields, such as electrical circuits or mechanical multibody systems, which generate the differential algebraic equations (DAEs) systems naturally. In general, the feature of DAEs is a sparse large scale system of fully nonlinear and high index. To make use of its sparsity, this paper provides a simple and efficient algorithm for computing the large scale DAEs system. We exploit the shortest augmenting path algorithm for finding maximum value transversal (MVT) as well as block triangular forms (BTF). We also present the extended signature matrix method with the block fixed point iteration and its complexity results. Furthermore, a range of nontrivial problems are demonstrated by our algorithm.
No SQL, No Injection? Examining NoSQL Security
Venue: arXiv cs.CR 2015
Year: 2015

NoSQL data storage systems have become very popular due to their scalability and ease of use. This paper examines the maturity of security measures for NoSQL databases, addressing their new query and access mechanisms. For example the emergence of new query formats makes the old SQL injection techniques irrelevant, but are NoSQL databases immune to injection in general? The answer is NO. Here we present a few techniques for attacking NoSQL databases such as injections and CSRF. We analyze the source of these vulnerabilities and present methodologies to mitigate the attacks. We show that this new vibrant technological area lacks the security measures and awareness which have developed over the years in traditional RDBMS SQL systems.
The Case for a General and Interaction-based Third-party Cookie Policy
Venue: arXiv cs.CR 2015
Year: 2015

The privacy implications of third-party tracking is a well-studied problem. Recent research has shown that besides data aggregators and behavioral advertisers, online social networks also act as trackers via social widgets. Existing cookie policies are not enough to solve these problems, pushing users to employ blacklist-based browser extensions to prevent such tracking. Unfortunately, such approaches require maintaining and distributing blacklists, which are often too general and adversely affect non-tracking services for advertisements and analytics. In this paper, we propose and advocate for a general third-party cookie policy that prevents third-party tracking with cookies and preserves the functionality of social widgets without requiring a blacklist and adversely affecting non-tracking services. We implemented a proof-of-concept of our policy as browser extensions for Mozilla Firefox and Google Chrome. To date, our extensions have been downloaded about 11.8K times and have over 2.8K daily users combined.
Stickler: Defending Against Malicious CDNs in an Unmodified Browser
Venue: arXiv cs.CR 2015
Year: 2015

Website publishers can derive enormous performance benefits and cost savings by directing traffic to their sites through content distribution networks (CDNs). However, publishers who use CDNs today must trust their CDN not to modify the site's JavaScript, CSS, images or other media en route to end users. A CDN that violates this trust could inject ads into websites, downsample media to save bandwidth or, worse, inject malicious JavaScript code to steal user secrets it could not otherwise access. We present Stickler, a system for website publishers that guarantees the end-to-end authenticity of content served to end users while simultaneously allowing publishers to reap the benefits of CDNs. Crucially, Stickler achieves these guarantees without requiring modifications to the browser.
Owning Your Home Network: Router Security Revisited
Venue: arXiv cs.CR 2015
Year: 2015

In this paper we investigate the Web interfaces of several DSL home routers that can be used to manage their settings via a Web browser. Our goal is to change these settings by using primary XSS and UI redressing attacks. This study evaluates routers from 10 different manufacturers (TP-Link, Netgear, Huawei, D-Link, Linksys, LogiLink, Belkin, Buffalo, Fritz!Box, and Asus). We were able to circumvent the security of all of them. To demonstrate how all devices are able to be attacked, we show how to do fast fingerprinting attacks. Furthermore, we provide countermeasures to make administration interfaces and therefore the use of routers more secure.
Applying Memory Forensics to Rootkit Detection
Venue: arXiv cs.CR 2015
Year: 2015

Volatile memory dump and its analysis is an essential part of digital forensics. Among a number of various software and hardware approaches for memory dumping there are authors who point out that some of these approaches are not resilient to various anti-forensic techniques, and others that require a reboot or are highly platform dependent. New resilient tools have certain disadvantages such as low speed or vulnerability to rootkits which directly manipulate kernel structures e.g. page tables. A new memory forensic system - Malware Analysis System for Hidden Knotty Anomalies (MASHKA) is described in this paper. It is resilient to popular anti-forensic techniques. The system can be used for doing a wide range of memory forensics tasks. This paper describes how to apply the system for research and detection of kernel mode rootkits and also presents analysis of the most popular anti-rootkit tools.
Bayesian Budget Feasibility with Posted Pricing
Venue: arXiv cs.GT 2015
Year: 2015

We consider the problem of budget feasible mechanism design proposed by Singer (2010), but in a Bayesian setting. A principal has a public value for hiring a subset of the agents and a budget, while the agents have private costs for being hired. We consider both additive and submodular value functions of the principal. We show that there are simple, practical, ex post budget balanced posted pricing mechanisms that approximate the value obtained by the Bayesian optimal mechanism that is budget balanced only in expectation. A main motivating application for this work is the crowdsourcing large projects, e.g., on Mechanical Turk, where workers are drawn from a large population and posted pricing is standard. Our analysis methods relate to contention resolution schemes in submodular optimization of Vondrak et al. (2011) and the correlation gap analysis of Yan (2011).
Gradual Certified Programming in Coq
Venue: arXiv cs.PL 2015
Year: 2015

Expressive static typing disciplines are a powerful way to achieve high-quality software. However, the adoption cost of such techniques should not be under-estimated. Just like gradual typing allows for a smooth transition from dynamically-typed to statically-typed programs, it seems desirable to support a gradual path to certified programming. We explore gradual certified programming in Coq, providing the possibility to postpone the proofs of selected properties, and to check "at runtime" whether the properties actually hold. Casts can be integrated with the implicit coercion mechanism of Coq to support implicit cast insertion a la gradual typing. Additionally, when extracting Coq functions to mainstream languages, our encoding of casts supports lifting assumed properties into runtime checks. Much to our surprise, it is not necessary to extend Coq in any way to support gradual certified programming. A simple mix of type classes and axioms makes it possible to bring gradual certified programming to Coq in a straightforward manner.
Evaluation of the Accuracy of the BGLemmatizer
Venue: arXiv cs.CL 2015
Year: 2015

This paper reveals the results of an analysis of the accuracy of developed software for automatic lemmatization for the Bulgarian language. This lemmatization software is written entirely in Java and is distributed as a GATE plugin. Certain statistical methods are used to define the accuracy of this software. The results of the analysis show 95% lemmatization accuracy.
Pseudorandomness via the discrete Fourier transform
Venue: arXiv cs.CC 2015
Year: 2015

We present a new approach to constructing unconditional pseudorandom generators against classes of functions that involve computing a linear function of the inputs. We give an explicit construction of a pseudorandom generator that fools the discrete Fourier transforms of linear functions with seed-length that is nearly logarithmic (up to polyloglog factors) in the input size and the desired error parameter. Our result gives a single pseudorandom generator that fools several important classes of tests computable in logspace that have been considered in the literature, including halfspaces (over general domains), modular tests and combinatorial shapes. For all these classes, our generator is the first that achieves near logarithmic seed-length in both the input length and the error parameter. Getting such a seed-length is a natural challenge in its own right, which needs to be overcome in order to derandomize RL - a central question in complexity theory. Our construction combines ideas from a large body of prior work, ranging from a classical construction of [NN93] to the recent gradually increasing independence paradigm of [KMN11, CRSW13, GMRTV12], while also introducing some novel analytic machinery which might find other applications.
Drawing Large Graphs by Multilevel Maxent-Stress Optimization
Venue: arXiv cs.DS 2015
Year: 2015

Drawing large graphs appropriately is an important step for the visual analysis of data from real-world networks. Here we present a novel multilevel algorithm to compute a graph layout with respect to a recently proposed metric that combines layout stress and entropy. As opposed to previous work, we do not solve the linear systems of the maxent-stress metric with a typical numerical solver. Instead we use a simple local iterative scheme within a multilevel approach. To accelerate local optimization, we approximate long-range forces and use shared-memory parallelism. Our experiments validate the high potential of our approach, which is particularly appealing for dynamic graphs. In comparison to the previously best maxent-stress optimizer, which is sequential, our parallel implementation is on average 30 times faster already for static graphs (and still faster if executed on one thread) while producing a comparable solution quality.
Performance evaluation of different optimization techniques for coverage and connectivity control in backbone based wireless networks
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, performance evaluation of Newton-Raphson and Conjugate Gradient method has been studied in comparison to Steepest Decent method for coverage and connectivity control in backbone based wireless networks. In order to design such wireless networks, the main challenge is to ensure network requirements such as network coverage and connectivity. To optimize coverage and connectivity, backbone nodes will be repositioned by the use of mobility control based on above mentioned methods. Thus the network get self organized which autonomously achieve energy minimizing configuration. Furthermore by simulation using MATLAB R2010a, methods are compared on the basis of optimized cost, number of iterations and elapsed time i.e. total time taken to execute the algorithm.
A Fast Incremental Gaussian Mixture Model
Venue: arXiv cs.LG 2015
Year: 2015

This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in a single-pass by improving its model after analyzing each data point and discarding it thereafter. Nevertheless, it suffers from the scalability point-of-view, due to its asymptotic time complexity of $\operatorname{O}\bigl(NKD^3\bigr)$ for $N$ data points, $K$ Gaussian components and $D$ dimensions, rendering it inadequate for high-dimensional data. In this paper, we manage to reduce this complexity to $\operatorname{O}\bigl(NKD^2\bigr)$ by deriving formulas for working directly with precision matrices instead of covariance matrices. The final result is a much faster and scalable algorithm which can be applied to high dimensional tasks. This is confirmed by applying the modified algorithm to high-dimensional classification datasets.
Error Tree: A Tree Structure for Hamming & Edit Distances & Wildcards Matching
Venue: arXiv cs.DS 2015
Year: 2015

Error Tree is a novel tree structure that is mainly oriented to solve the approximate pattern matching problems, Hamming and edit distances, as well as the wildcards matching problem. The input is a text of length $n$ over a fixed alphabet of length $Σ$, a pattern of length $m$, and $k$. The output is to find all positions that have $\leq$ $k$ Hamming distance, edit distance, or wildcards matching with $P$. The algorithm proposes for Hamming distance and wildcards matching a tree structure that needs $O(n\frac{log_Σ^{k}n}{k!})$ words and takes $O(\frac {m^k}{k!} + occ$)($O(m + \frac {log_Σ^kn}{k!} + occ$) in the average case) of query time for any online/offline pattern, where $occ$ is the number of outputs. As well, a tree structure of $O(2^{k}n\frac{log_Σ^{k}n}{k!})$ words and $O(\frac {m^k}{k!} + 3^{k}occ$)($O(m + \frac {log_Σ^kn}{k!} + 3^{k}occ$) in the average case) query time for edit distance for any online/offline pattern.
Flow Segmentation in Dense Crowds
Venue: arXiv cs.CV 2015
Year: 2015

A framework is proposed in this paper that is used to segment flow of dense crowds. The flow field that is generated by the movement in the crowd is treated just like an aperiodic dynamic system. On this flow field a grid of particles is put over for particle advection by the use of a numerical integration scheme. Then flow maps are generated which associates the initial position of the particles with final position. The gradient of the flow maps gives the amount of divergence of the neighboring particles. For forward integration and analysis forward Finite time Lyapunov Exponent is calculated and backward Finite time Lyapunov Exponent is also calculated it gives the Lagrangian coherent structures of the flow in crowd. Lagrangian Coherent Structures basically divides the flow in crowd into regions and these regions have different dynamics. These regions are then used to get the boundary in the different flow segments by using water shed algorithm. The experiment is conducted on the crowd dataset of UCF (University of central Florida).
Coding Side-Information for Implementing Cubic Transformation
Venue: arXiv cs.CR 2015
Year: 2015

This paper considers the implementation of the cubic public-key transformation, a public-key cryptographic scheme that requires sending of additional side-information. A coding scheme for the side-information, based on the residue number system, is presented. In the conventional one-to-one encryption mapping also, such coding can be used to send additional like that of watermarking, which could be used to detect man-in-the-middle attacks or used for authentication.
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Venue: arXiv cs.CV 2015
Year: 2015

Different from face verification, face identification is much more demanding. To reach comparable performance, an identifier needs to be roughly N times better than a verifier. To expect a breakthrough in face identification, we need a fresh look at the fundamental building blocks of face recognition. In this paper we focus on the selection of a suitable signal representation and better matching strategy for face identification. We demonstrate how Gabor phase could be leveraged to improve the performance of face identification by using the Block Matching method. Compared to the existing approaches, the proposed method features much lower algorithmic complexity: face images are only filtered by a single-scale Gabor filter pair and the matching is performed between any pairs of face images at hand without involving any training process. Benchmark evaluations show that the proposed approach is totally comparable to and even better than state-of-the-art algorithms, which are typically based on more features extracted from a large set of Gabor faces and/or rely on heavy training processes.
Interpreting communities based on the evolution of a dynamic attributed network
Venue: arXiv cs.SI 2015
Year: 2015

Many methods have been proposed to detect communities, not only in plain, but also in attributed, directed or even dynamic complex networks. From the modeling point of view, to be of some utility, the community structure must be characterized relatively to the properties of the studied system. However, most of the existing works focus on the detection of communities, and only very few try to tackle this interpretation problem. Moreover, the existing approaches are limited either by the type of data they handle, or by the nature of the results they output. In this work, we see the interpretation of communities as a problem independent from the detection process, consisting in identifying the most characteristic features of communities. We give a formal definition of this problem and propose a method to solve it. To this aim, we first define a sequence-based representation of networks, combining temporal information, community structure, topological measures, and nodal attributes. We then describe how to identify the most emerging sequential patterns of this dataset, and use them to characterize the communities. We study the performance of our method on artificially generated dynamic attributed networks. We also empirically validate our framework on real-world systems: a DBLP network of scientific collaborations, and a LastFM network of social and musical interactions.
Multi-path Convolutional Neural Networks for Complex Image Classification
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional Neural Networks demonstrate high performance on ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the published results only show the overall performance for all image classes. There is no further analysis why certain images get worse results and how they could be improved. In this paper, we provide deep performance analysis based on different types of images and point out the weaknesses of convolutional neural networks through experiment. We design a novel multiple paths convolutional neural network, which feeds different versions of images into separated paths to learn more comprehensive features. This model has better presentation for image than the traditional single path model. We acquire better classification results on complex validation set on both top 1 and top 5 scores than the best ILSVRC 2013 classification model.
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
Venue: arXiv cs.LG 2015
Year: 2015

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $Ω(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14].
Green WSN- Optimization of Energy Use Through Reduction in Communication Workload
Venue: arXiv cs.NI 2015
Year: 2015

Applications of Wireless Sensor Networks (WSNs) are growing day by day due to the ease of deployment, reduction in costs to affordable levels and versatility of these networks. Besides developing advanced micro fabrication technologies means are being devised to reduce energy consumption to bring the network setup and operational costs down. With increasing applications amount of energy consumed in these networks is enormous. Even a small savings in energy consumption will result in huge benefits in energy consumption globally. Bulk of the energy is consumed in communication activity of these networks. It is our endeavor to optimize this activity to make these networks energy efficient and thereby reducing their impact on the overall environment in line with the principle Go Green.
End-to-end people detection in crowded scenes
Venue: arXiv cs.CV 2015
Year: 2015

Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes.