OpenRaiser/Intern-Atlas huggingface dataset:
A general practitioner or a specialist for your infected smartphone?
Venue: arXiv cs.CR 2015
Year: 2015

With explosive growth in the number of mobile devices, the mobile malware is rapidly spreading as well, and the number of encountered malware families is increasing. Existing solutions, which are mainly based on one malware detector running on the phone or in the cloud, are no longer effective. Main problem lies in the fact that it might be impossible to create a unique mobile malware detector that would be able to detect different malware families with high accuracy, being at the same time lightweight enough not to drain battery quickly and fast enough to give results of detection promptly. The proposed approach to mobile malware detection is analogous to general practitioner versus specialist approach to dealing with a medical problem. Similarly to a general practitioner that, based on indicative symptoms identifies potential illnesses and sends the patient to an appropriate specialist, our detection system distinguishes among symptoms representing different malware families and, once the symptoms are detected, it triggers specific analyses. A system monitoring application operates in the same way as a general practitioner. It is able to distinguish between different symptoms and trigger appropriate detection mechanisms. As an analogy to different specialists, an ensemble of detectors, each of which specifically trained for a particular malware family, is used. The main challenge of the approach is to define representative symptoms of different malware families and train detectors accordingly to them. The main goal of the poster is to foster discussion on the most representative symptoms of different malware families and to discuss initial results in this area obtained by using Malware Genome project dataset.
Streaming Property Testing of Visibly Pushdown Languages
Venue: arXiv cs.DS 2015
Year: 2015

In the context of language recognition, we demonstrate the superiority of streaming property testers against streaming algorithms and property testers, when they are not combined. Initiated by Feigenbaum et al., a streaming property tester is a streaming algorithm recognizing a language under the property testing approximation: it must distinguish inputs of the language from those that are $\varepsilon$-far from it, while using the smallest possible memory (rather than limiting its number of input queries). Our main result is a streaming $\varepsilon$-property tester for visibly pushdown languages (VPL) with one-sided error using memory space $\mathrm{poly}((\log n) / \varepsilon)$. This constructions relies on a (non-streaming) property tester for weighted regular languages based on a previous tester by Alon et al. We provide a simple application of this tester for streaming testing special cases of instances of VPL that are already hard for both streaming algorithms and property testers. Our main algorithm is a combination of an original simulation of visibly pushdown automata using a stack with small height but possible items of linear size. In a second step, those items are replaced by small sketches. Those sketches relies on a notion of suffix-sampling we introduce. This sampling is the key idea connecting our streaming tester algorithm to property testers.
Relaxed Disk Packing
Venue: arXiv cs.CG 2015
Year: 2015

Motivated by biological questions, we study configurations of equal-sized disks in the Euclidean plane that neither pack nor cover. Measuring the quality by the probability that a random point lies in exactly one disk, we show that the regular hexagonal grid gives the maximum among lattice configurations.
Twin-Load: Building a Scalable Memory System over the Non-Scalable Interface
Venue: arXiv cs.AR 2015
Year: 2015

Commodity memory interfaces have difficulty in scaling memory capacity to meet the needs of modern multicore and big data systems. DRAM device density and maximum device count are constrained by technology, package, and signal in- tegrity issues that limit total memory capacity. Synchronous DRAM protocols require data to be returned within a fixed latency, and thus memory extension methods over commodity DDRx interfaces fail to support scalable topologies. Current extension approaches either use slow PCIe interfaces, or require expensive changes to the memory interface, which limits commercial adoptability. Here we propose twin-load, a lightweight asynchronous memory access mechanism over the synchronous DDRx interface. Twin-load uses two special loads to accomplish one access request to extended memory, the first serves as a prefetch command to the DRAM system, and the second asynchronously gets the required data. Twin-load requires no hardware changes on the processor side and only slight soft- ware modifications. We emulate this system on a prototype to demonstrate the feasibility of our approach. Twin-load has comparable performance to NUMA extended memory and outperforms a page-swapping PCIe-based system by several orders of magnitude. Twin-load thus enables instant capacity increases on commodity platforms, but more importantly, our architecture opens opportunities for the design of novel, efficient, scalable, cost-effective memory subsystems.
Modified Hausdorff Fractal Dimension (MHFD)
Venue: arXiv cs.CV 2015
Year: 2015

The Hausdorff fractal dimension has been a fast-to-calculate method to estimate complexity of fractal shapes. In this work, a modified version of this fractal dimension is presented in order to make it more robust when applied in estimating complexity of non-fractal images. The modified Hausdorff fractal dimension stands on two features that weaken the requirement of presence of a shape and also reduce the impact of the noise possibly presented in the input image. The new algorithm has been evaluated on a set of images of different character with promising performance.
Pricing Agreement between Service and Content Providers: A Net Neutrality Issue
Venue: arXiv cs.CY 2015
Year: 2015

The Net Neutrality issue has been at the center of debate worldwide lately. Some countries have established laws so that principles of Net Neutrality are respected, the Netherlands being the latest country in Europe. Among the questions that have been discussed in these debates are whether to allow agreements between service and content providers, i.e. to allow some preferential treatment by an operator to traffic from some subset of providers. Our goal in this paper is to analyze the impact of non neutral pricing and agreements on the Internet users and on the content providers. Each one of several Internet users have to decide in which way to split their demand among several content providers. The cost for an Internet user depends on whether the content providers have an agreement with the Internet Service Provider in which the Internet user is connected to. In addition, the requests coming from users depend on the preference of the consumer in the different CP. We model the choice of how to split the demands and the pricing aspects faced by the content providers as a hierarchical game model composed of a congestion game at the lower level and a noncooperative pricing game at the upper level. We show that agreement between providers have a positive impact on the equilibrium performance of the Internet users. We further show that at equilibrium, the content provider on the contrary, does not benefit from the agreement.
Splitting Root-Locus Plot into Algebraic Plane Curves
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we show how to split the Root Locus plot for an irreducible rational transfer function into several individual algebraic plane curves, like lines, circles, conics, etc. To achieve this goal we use results of a previous paper of the author to represent the Root Locus as an algebraic variety generated by an ideal over a polynomial ring, and whose primary decomposion allow us to isolate the planes curves that composes the Root Locus. As a by-product, using the concept of duality in projective algebraic geometry, we show how to obtain the dual curve of each plane curve that composes the Root Locus and unite them to obtain what we denominate the "Algebraic Dual Root Locus".
Timed Consistent Network Updates
Venue: arXiv cs.NI 2015
Year: 2015

Network updates such as policy and routing changes occur frequently in Software Defined Networks (SDN). Updates should be performed consistently, preventing temporary disruptions, and should require as little overhead as possible. Scalability is increasingly becoming an essential requirement in SDN. In this paper we propose to use time-triggered network updates to achieve consistent updates. Our proposed solution requires lower overhead than existing update approaches, without compromising the consistency during the update. We demonstrate that accurate time enables far more scalable consistent updates in SDN than previously available. In addition, it provides the SDN programmer with fine-grained control over the tradeoff between consistency and scalability.
Distant Supervision for Entity Linking
Venue: arXiv cs.CL 2015
Year: 2015

Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages). In this way, a large scale of weakly labeled data can be generated without manual annotation and fed to a classifier for linking more newly discovered entities. Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia. Specifically, the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities. Experiments conducted on a dataset of 140,000 items and 60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze the feature performance and improve the F1-measure to 0.545.
Parametric Regression on the Grassmannian
Venue: arXiv cs.CV 2015
Year: 2015

We address the problem of fitting parametric curves on the Grassmann manifold for the purpose of intrinsic parametric regression. As customary in the literature, we start from the energy minimization formulation of linear least-squares in Euclidean spaces and generalize this concept to general nonflat Riemannian manifolds, following an optimal-control point of view. We then specialize this idea to the Grassmann manifold and demonstrate that it yields a simple, extensible and easy-to-implement solution to the parametric regression problem. In fact, it allows us to extend the basic geodesic model to (1) a time-warped variant and (2) cubic splines. We demonstrate the utility of the proposed solution on different vision problems, such as shape regression as a function of age, traffic-speed estimation and crowd-counting from surveillance video clips. Most notably, these problems can be conveniently solved within the same framework without any specifically-tailored steps along the processing pipeline.
Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions
Venue: arXiv cs.DC 2015
Year: 2015

We describe a technique for drawing values from discrete distributions, such as sampling from the random variables of a mixture model, that avoids computing a complete table of partial sums of the relative probabilities. A table of alternate ("butterfly-patterned") form is faster to compute, making better use of coalesced memory accesses. From this table, complete partial sums are computed on the fly during a binary search. Measurements using an NVIDIA Titan Black GPU show that for a sufficiently large number of clusters or topics (K > 200), this technique alone more than doubles the speed of a latent Dirichlet allocation (LDA) application already highly tuned for GPU execution.
Closed-Form Bounds for the Rice $Ie$-Function
Venue: arXiv cs.IT 2015
Year: 2015

This work is devoted in the derivation of novel upper and lower bounds for the Rice $Ie$-function. These bounds are expressed in closed-form and are shown to be quite tight. This is particularly evident by the fact that for a certain range of parameter values, the derived lower bound virtually behaves as a remarkably accurate approximation. As a result, the offered expressions can be considered useful mathematical tools that can be efficiently employed in various analytical studies related to natural sciences and engineering. To this effect, they can be sufficiently applied in the area of digital communications over fading channels for the derivation of explicit representations for vital performance measures such as bit and symbol error probability, among others.
Textual Spatial Cosine Similarity
Venue: arXiv cs.IR 2015
Year: 2015

When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprise-wide search environments. To address these real-time constraints, we developed a new measure of document similarity called Textual Spatial Cosine Similarity, which is able to detect similitude at the semantic level using word placement information contained in the document. We will see in this paper that two degenerate cases exist for this model, which coincide with Cosine Similarity on one side and with a paraphrasing detection model to the other.
Logics of Strong Noncontingency
Venue: arXiv cs.LO 2015
Year: 2015

Inspired by Hintikka's treatment of question embedding verbs in [8] and the variations of noncontingency operator, we propose a logic with strong noncontingency operator $\blacktriangle$ as the only primitive modality. A proposition is strongly noncontingent, if no matter whether it is true or false, it does it necessarily; otherwise, it is weakly contingent. This logic is not a normal modal logic, since $\blacktriangle(ϕ\toψ)\to(\blacktriangleϕ\to\blacktriangleψ)$ is invalid. We compare the relative expressivity of this logic and other logics, such as standard modal logic, noncontingency logic, and logic of essence and accident, and investigate its frame definability. Apart from these results, we also propose a suitable notion of bisimulation for the logic of strong noncontingency, based on which we characterize this logic within modal logic and within first-order logic. We also axiomatize the logic of strong noncontingency over various frame classes. Our work is also related to the treatment of agreement operator in [10].
Computationally efficient formulation of relay operator for Preisach hysteresis modeling
Venue: arXiv cs.SY 2015
Year: 2015

An algebraic expression for the Preisach hysteron, which is a non-ideal (delayed) relay operator, is formulated for a computationally efficient real-time implementation. This allows representing the classical scalar Preisach hysteresis model as a summation of a large number of weighted hysterons which computation can be accomplished in parallel. The latter makes possible an efficient FPGA or ASIC realization of the scalar Preisach hysteresis model that can be useful for multiple applications. The signal flow which specifies the model implementation is provided in form of the block diagram. The proposed computation of Preisach hysterons, aggregated to the entire Preisach hysteresis model, is evaluated numerically and on a real-time hardware platform.
Location Prediction of Social Images via Generative Model
Venue: arXiv cs.IR 2015
Year: 2015

The vast amount of geo-tagged social images has attracted great attention in research of predicting location using the plentiful content of images, such as visual content and textual description. Most of the existing researches use the text-based or vision-based method to predict location. There still exists a problem: how to effectively exploit the correlation between different types of content as well as their geographical distributions for location prediction. In this paper, we propose to predict image location by learning the latent relation between geographical location and multiple types of image content. In particularly, we propose a geographical topic model GTMI (geographical topic model of social image) to integrate multiple types of image content as well as the geographical distributions, In GTMI, image topic is modeled on both text vocabulary and visual feature. Each region has its own distribution over topics and hence has its own language model and vision pattern. The location of a new image is estimated based on the joint probability of image content and similarity measure on topic distribution between images. Experiment results demonstrate the performance of location prediction based on GTMI.
Solutions to the Incomplete Toronto Function and Incomplete Lipschitz-Hankel Integrals
Venue: arXiv cs.IT 2015
Year: 2015

This paper provides novel analytic expressions for the incomplete Toronto function, $T_{B}(m,n,r)$, and the incomplete Lipschitz-Hankel Integrals of the modified Bessel function of the first kind, $Ie_{m,n}(a,z)$. These expressions are expressed in closed-form and are valid for the case that $n$ is an odd multiple of $1/2$, i.e. $n \pm 0.5\in\mathbb{N}$. Capitalizing on these, tight upper and lower bounds are subsequently proposed for both $T_{B}(m,n,r)$ function and $Ie_{m,n}(a,z)$ integrals. Importantly, all new representations are expressed in closed-form whilst the proposed bounds are shown to be rather tight. To this effect, they can be effectively exploited in various analytical studies related to wireless communication theory. Indicative applications include, among others, the performance evaluation of digital communications over fading channels and the information-theoretic analysis of multiple-input multiple-output systems.
An Examination of the Effectiveness of Teaching Data Modelling Concepts
Venue: arXiv cs.CY 2015
Year: 2015

The effective teaching of data modelling concepts is very important; it constitutes the fundament of database planning methods and the handling of databases with the help of database management lan-guages, typically SQL. We examined three courses. The students of two courses prepared for the exam by solving tests, while the students of the third course prepared by solving tasks from a printed exercise book. The number of task for the second course was 2.5 times more than the number of task for the first course. The main purpose of our examination was to determine the effectiveness of the teaching of data modelling concepts, and to decide if there is a significant difference between the results of the three courses. According to our examination, with increasing the number of test tasks and with the use of exercise book, the results became significantly better.
A Real Time Facial Expression Classification System Using Local Binary Patterns
Venue: arXiv cs.CV 2015
Year: 2015

Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns (LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
MCODE: Multivariate Conditional Outlier Detection
Venue: arXiv cs.AI 2015
Year: 2015

Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of multiple outcome variable values. In this paper, we develop and study a new conditional outlier detection approach for multivariate outcome spaces that works by (1) transforming the conditional detection to the outlier detection problem in a new (unconditional) space and (2) defining outlier scores by analyzing the data in the new space. Our approach relies on the classifier chain decomposition of the multi-dimensional classification problem that lets us transform the output space into a probability vector, one probability for each dimension of the output space. Outlier scores applied to these transformed vectors are then used to detect the outliers. Experiments on multiple multi-dimensional classification problems with the different outlier injection rates show that our methodology is robust and able to successfully identify outliers when outliers are either sparse (manifested in one or very few dimensions) or dense (affecting multiple dimensions).
An informational study of the evolution of codes and of emerging concepts in populations of agents
Venue: arXiv cs.MA 2015
Year: 2015

We consider the problem of the evolution of a code within a structured population of agents. The agents try to maximise their information about their environment by acquiring information from the outputs of other agents in the population. A naive use of information-theoretic methods would assume that every agent knows how to "interpret" the information offered by other agents. However, this assumes that one "knows" which other agents one observes, and thus which code they use. In our model, however, we wish to preclude that: it is not clear which other agents an agent is observing, and the resulting usable information is therefore influenced by the universality of the code used and by which agents an agent is "listening" to. We further investigate whether an agent who does not directly perceive the environment can distinguish states by observing other agents' outputs. For this purpose, we consider a population of different types of agents "talking" about different concepts, and try to extract new ones by considering their outputs only.
On the $κ$-$μ$/Gamma Generalized Multipath/ Shadowing Fading Distribution
Venue: arXiv cs.IT 2015
Year: 2015

This work is devoted to the formulation and derivation of the $κ-μ$/gamma distribution which corresponds to A physical fading model. This distribution is composite and is based on the well known $κ-μ$ generalized multipath model and the gamma shadowing model. A special case of the derived model constitutes the $κ-μ$ Extreme/gamma model which accounts for severe multipath and shadowing effects. These models provide accurate characterisation of the simultaneous occurrence of multipath fading and shadowing effects. This is achieved thanks to the remarkable flexibility of their named parameters which render them capable of providing good fittings to experimental data associated with realistic communication scenarios. This is additionally justified by the fact that they include as special cases the widely known composite fading models such as Rice/gamma, Nakagami-m/gamma and Rayleigh/gamma. Novel analytic expressions are derived for the envelope and power probability density function of these distributions which are expressed in a relatively simple algebraic form which is convenient to handle both analytically and numerically. As a result, they can be meaningfully utilized in the derivation of numerous vital measures in investigations related to the analytic performance evaluation of digital communications over composite multipath/shadowing fading channels.