OpenRaiser/Intern-Atlas huggingface dataset:
Paying for Likes? Understanding Facebook Like Fraud Using Honeypots
Venue: arXiv cs.SI 2014
Year: 2014

Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior.
Extremum Seeking-based Iterative Learning Linear MPC
Venue: arXiv cs.SY 2014
Year: 2014

In this work we study the problem of adaptive MPC for linear time-invariant uncertain models. We assume linear models with parametric uncertainties, and propose an iterative multi-variable extremum seeking (MES)-based learning MPC algorithm to learn on-line the uncertain parameters and update the MPC model. We show the effectiveness of this algorithm on a DC servo motor control example.
Variability Modeling for Customizable SaaS Applications
Venue: arXiv cs.SE 2014
Year: 2014

Most of current Software-as-a-Service (SaaS) applications are developed as customizable service-oriented applications that serve a large number of tenants (users) by one application instance. The current rapid evolution of SaaS applications increases the demand to study the commonality and variability in software product lines that produce customizable SaaS applications. During runtime, Customizability is required to achieve different tenants' requirements. During the development process, defining and realizing commonalty and variability in SaaS applications' families is required to develop reusable, flexible, and customizable SaaS applications at lower costs, in shorter time, and with higher quality. In this paper, Orthogonal Variability Model (OVM) is used to model variability in a separated model, which is used to generate simple and understandable customization model. Additionally, Service oriented architecture Modeling Language (SoaML) is extended to define and realize commonalty and variability during the development of SaaS applications.
Mining Users' Significant Driving Routes with Low-power Sensors
Venue: arXiv cs.CY 2014
Year: 2014

While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, opens doors to the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.
Semantically Configurable Consistency Analysis for Class and Object Diagrams
Venue: arXiv cs.SE 2014
Year: 2014

Checking consistency between an object diagram (OD) and a class diagram (CD) is an important analysis problem. However, several variations in the semantics of CDs and ODs, as used in different contexts and for different purposes, create a challenge for analysis tools. To address this challenge in this paper we investigate semantically configurable model analysis. We formalize the variability in the languages semantics using a feature model: each configuration that the model permits induces a different semantics. Moreover, we develop a parametrized analysis that can be instantiated to comply with every legal configuration of the feature model. Thus, the analysis is semantically congured and its results change according to the semantics induced by the selected feature configuration. The ideas are implemented using a parametrized transformation to Alloy. The work can be viewed as a case study example for a formal and automated approach to handling semantic variability in modeling languages.
Metamodelling: State of the Art and Research Challenges
Venue: arXiv cs.SE 2014
Year: 2014

This chapter discusses the current state of the art, and emerging research challenges, for metamodelling. In the state-of-the-art review on metamodelling, we review approaches, abstractions, and tools for metamodelling, evaluate them with respect to their expressivity, investigate what role(s) metamodels may play at run-time and how semantics can be assigned to metamodels and the domain specific modeling languages they could define. In the emerging challenges section on metamodelling we highlight research issues regarding the management of complexity, consistency, and evolution of metamodels, and how the semantics of metamodels impacts each of these.
Investigation of Partition Cells as a Structural Basis Suitable for Assessments of Individual Scientists
Venue: arXiv cs.DL 2014
Year: 2014

Individual, excellent scientists have become increasingly important in the research funding landscape. Accurate bibliometric measures of an individual's performance could help identify excellent scientists, but still present a challenge. One crucial aspect in this respect is an adequate delineation of the sets of publications that determine the reference values to which a scientist's publication record and its citation impact should be compared. The structure of partition cells formed by intersecting fixed subject categories in a database has been proposed to approximate a scientist's specialty more closely than can be done with the broader subject categories. This paper investigates this cell structure's suitability as an underlying basis for methodologies to assess individual scientists, from two perspectives: (1) Proximity to the actual structure of publication records of individual scientists: The distribution and concentration of publications over the highly fragmented structure of partition cells are examined for a sample of ERC grantees; (2) Proximity to customary levels of accuracy: Differences in commonly used reference values (mean expected number of citations per publication, and threshold number of citations for highly cited publications) between adjacent partition cells are compared to differences in two other dimensions: successive publication years and successive citation window lengths. Findings from both perspectives are in support of partition cells rather than the larger subject categories as a journal based structure on which to construct and apply methodologies for the assessment of highly specialized publication records such as those of individual scientists.
Towards Architectural Programming of Embedded Systems
Venue: arXiv cs.SE 2014
Year: 2014

Integrating architectural elements with a modern programming language is essential to ensure a smooth combination of architectural design and programming. In this position statement, we motivate a combination of architectural description for distributed, asynchronously communicating systems and Java as an example for such an integration. The result is an ordinary programming language, that exhibits architecture, data structure and behavior within one view. Mappings or tracing between different views is unnecessary. A prototypical implementation of a compiler demonstrates the possibilities and challenges of architectural programming.
MontiWeb - Modular Development of Web Information Systems
Venue: arXiv cs.SE 2014
Year: 2014

The development process of web information systems is often tedious, error prone and usually involves redundant steps of work. Therefore, it is rather e_x000E_fficient to employ a modeldriven approach for the systematic aspects that comprise such a system. This involves models for the data structure that shall be handled by the system (here: class diagrams), various editable and read-only presentations (views) on combinations and extractions of the underlying data (here: a special view language) and ways to connect these views and define data flow between them (here: activity diagrams). In this paper, we present the MontiWeb approach to model and generate these aspects in a modular manner by incorperating the MontiCore framework. Therefor we shortly introduce the infrastructure that helps to develop modular systems. This involves the whole development process from defining the modeling languages to final code generation as well as all steps in between. We present the text-based class and activity diagram languages as well as a view language that are used to model our system.
Black-box Integration of Heterogeneous Modeling Languages for Cyber-Physical Systems
Venue: arXiv cs.SE 2014
Year: 2014

Robots belong to a class of Cyber-Physical Systems where complex software as a mobile device has to full tasks in a complex environment. Modeling robotics applications for analysis and code generation requires modeling languages for the logical software architecture and the system behavior. The MontiArcAutomaton modeling framework integrates six independently developed modeling languages to model robotics applications: a component & connector architecture description language, automata, I/O tables, class diagrams, OCL, and a Java DSL. We describe how we integrated these languages into MontiArcAutomaton a-posteriori in a black-box integration fashion.
Sketching Cuts in Graphs and Hypergraphs
Venue: arXiv cs.DS 2014
Year: 2014

Sketching and streaming algorithms are in the forefront of current research directions for cut problems in graphs. In the streaming model, we show that $(1-ε)$-approximation for Max-Cut must use $n^{1-O(ε)}$ space; moreover, beating $4/5$-approximation requires polynomial space. For the sketching model, we show that $r$-uniform hypergraphs admit a $(1+ε)$-cut-sparsifier (i.e., a weighted subhypergraph that approximately preserves all the cuts) with $O(ε^{-2} n (r+\log n))$ edges. We also make first steps towards sketching general CSPs (Constraint Satisfaction Problems).
Image processing
Venue: arXiv cs.CV 2014
Year: 2014

Gabor filters can extract multi-orientation and multiscale features from face images. Researchers have designed different ways to use the magnitude of the filtered results for face recognition: Gabor Fisher classifier exploited only the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor Binary Pattern uses only the gradient information. In this paper, we regard GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a face representation method called Gabor Surface Feature (GSF). First, we compute the magnitude, 1st and 2nd derivatives of GMPs, then binarize them and transform them into decimal values. Finally we construct joint histograms and use subspace methods for classification. Experiments on FERET, ORL and FRGC 1.0.4 database show the effectiveness of GSF.
Simulate High Traffic and Effective Cost an Academic Kurdistan Network Based on DWDM using OPNET
Venue: arXiv cs.NI 2014
Year: 2014

Till now the academic institutes in Kurdistan region not connected via network with each other and with research centers worldwide, this paper suggests design an optical IP-Network for academic Kurdistan region based on DWDM (Dense wavelength Division Multiplexer) backbone. The design has been developed using minimum spanning tree between the main realistic locations for campuses in each province were calculated using Prim's algorithm in province level. This proposed network simulated using OPENT IT GURU, and then information was collected using GPS and GIS about the campuses (to obtain realistic locations) of each university in university level, finally unique design model was assumed for each campus in campus level. Simulation the proposed design shows results of delay, traffic sends, traffic receives; utility, performance (packet/second) and throughput are measured in the case of heavy load. Addition to that the hub based network has longer response time than the switch based network. Key words: IP-Network, OPNET, DWDM, Prim's algorithm, Intranet, GPS and GIS.
A Local Logic for Realizability in Web Service Choreographies
Venue: arXiv cs.LO 2014
Year: 2014

Web service choreographies specify conditions on observable interactions among the services. An important question in this regard is realizability: given a choreography C, does there exist a set of service implementations I that conform to C ? Further, if C is realizable, is there an algorithm to construct implementations in I ? We propose a local temporal logic in which choreographies can be specified, and for specifications in the logic, we solve the realizability problem by constructing service implementations (when they exist) as communicating automata. These are nondeterministic finite state automata with a coupling relation. We also report on an implementation of the realizability algorithm and discuss experimental results.
Simple and Near-Optimal Mechanisms For Market Intermediation
Venue: arXiv cs.GT 2014
Year: 2014

A prevalent market structure in the Internet economy consists of buyers and sellers connected by a platform (such as Amazon or eBay) that acts as an intermediary and keeps a share of the revenue of each transaction. While the optimal mechanism that maximizes the intermediary's profit in such a setting may be quite complicated, the mechanisms observed in reality are generally much simpler, e.g., applying an affine function to the price of the transaction as the intermediary's fee. Loertscher and Niedermayer [2007] initiated the study of such fee-setting mechanisms in two-sided markets, and we continue this investigation by addressing the question of when an affine fee schedule is approximately optimal for worst-case seller distribution. On one hand our work supplies non-trivial sufficient conditions on the buyer side (i.e. linearity of marginal revenue function, or MHR property of value and value minus cost distributions) under which an affine fee schedule can obtain a constant fraction of the intermediary's optimal profit for all seller distributions. On the other hand we complement our result by showing that proper affine fee-setting mechanisms (e.g. those used in eBay and Amazon selling plans) are unable to extract a constant fraction of optimal profit in the worst-case seller distribution. As subsidiary results we also show there exists a constant gap between maximum surplus and maximum revenue under the aforementioned conditions. Most of the mechanisms that we propose are also prior-independent with respect to the seller, which signifies the practical implications of our result.
Linear-time Algorithms for Proportional Apportionment
Venue: arXiv cs.DS 2014
Year: 2014

The apportionment problem deals with the fair distribution of a discrete set of $k$ indivisible resources (such as legislative seats) to $n$ entities (such as parties or geographic subdivisions). Highest averages methods are a frequently used class of methods for solving this problem. We present an $O(n)$-time algorithm for performing apportionment under a large class of highest averages methods. Our algorithm works for all highest averages methods used in practice.
Learning Machines Implemented on Non-Deterministic Hardware
Venue: arXiv cs.LG 2014
Year: 2014

This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementations. The hardware/software co-design methodology advocated here hinges on the deployment of compute-intensive machine learning kernels onto compute platforms that trade-off determinism in the computation for improvement in speed and/or energy efficiency. To achieve this, we revisit digital stochastic circuits for approximating matrix computations that are ubiquitous in machine learning algorithms. Theoretical and empirical evaluation is undertaken to assess the impact of the hardware-induced computational noise on algorithm performance. As a proof-of-concept, a stochastic hardware simulator is employed for training deep neural networks for image recognition problems.
A Crowdsourcing Procedure for the Discovery of Non-Obvious Attributes of Social Image
Venue: arXiv cs.IR 2014
Year: 2014

Research on mid-level image representations has conventionally concentrated relatively obvious attributes and overlooked non-obvious attributes, i.e., characteristics that are not readily observable when images are viewed independently of their context or function. Non-obvious attributes are not necessarily easily nameable, but nonetheless they play a systematic role in people`s interpretation of images. Clusters of related non-obvious attributes, called interpretation dimensions, emerge when people are asked to compare images, and provide important insight on aspects of social images that are considered relevant. In contrast to aesthetic or affective approaches to image analysis, non-obvious attributes are not related to the personal perspective of the viewer. Instead, they encode a conventional understanding of the world, which is tacit, rather than explicitly expressed. This paper introduces a procedure for discovering non-obvious attributes using crowdsourcing. We discuss this procedure using a concrete example of a crowdsourcing task on Amazon Mechanical Turk carried out in the domain of fashion. An analysis comparing discovered non-obvious attributes with user tags demonstrated the added value delivered by our procedure.
The Hildreth's Algorithm with Applications to Soft Constraints for User Interface Layout
Venue: arXiv cs.NA 2014
Year: 2014

The Hildreth's algorithm is a row action method for solving large systems of inequalities. This algorithm is efficient for problems with sparse matrices, as opposed to direct methods such as Gaussian elimination or QR-factorization. We apply the Hildreth's algorithm, as well as a randomized version, along with prioritized selection of the inequalities, to efficiently detect the highest priority feasible subsystem of equations. We prove convergence results and feasibility criteria for both cyclic and randomized Hildreth's algorithm, as well as a mixed algorithm which uses Hildreth's algorithm for inequalities and Kaczmarz algorithm for equalities. These prioritized, sparse systems of inequalities commonly appear in constraint-based user interface (UI) layout specifications. The performance and convergence of these proposed algorithms are evaluated empirically using randomly generated UI layout specifications of various sizes. The results show that these methods offer improvements in performance over standard methods like Matlab's LINPROG, a well-known efficient linear programming solver, and the recent developed Kaczmarz algorithm with prioritized IIS detection.
Efficient Algorithms for Discrepancy Minimization in Convex Sets
Venue: arXiv cs.DS 2014
Year: 2014

A result of Spencer states that every collection of $n$ sets over a universe of size $n$ has a coloring of the ground set with $\{-1,+1\}$ of discrepancy $O(\sqrt{n})$. A geometric generalization of this result was given by Gluskin (see also Giannopoulos) who showed that every symmetric convex body $K\subseteq R^n$ with Gaussian measure at least $e^{-εn}$, for a small $ε>0$, contains a point $y\in K$ where a constant fraction of coordinates of $y$ are in $\{-1,1\}$. This is often called a partial coloring result. While both these results were inherently non-algorithmic, recently Bansal (see also Lovett-Meka) gave a polynomial time algorithm for Spencer's setting and Rothvoßgave a randomized polynomial time algorithm obtaining the same guarantee as the result of Gluskin and Giannopoulos. This paper has several related results. First we prove another constructive version of the result of Gluskin and Giannopoulos via an optimization of a linear function. This implies a linear programming based algorithm for combinatorial discrepancy obtaining the same result as Spencer. Our second result gives a new approach to obtains partial colorings and shows that every convex body $K\subseteq R^n$, possibly non-symmetric, with Gaussian measure at least $e^{-εn}$, for a small $ε>0$, contains a point $y\in K$ where a constant fraction of coordinates of $y$ are in $\{-1,1\}$. Finally, we give a simple proof that shows that for any $δ>0$ there exists a constant $c>0$ such that given a body $K$ with $γ_n(K)\geq δ$, a uniformly random $x$ from $\{-1,1\}^n$ is in $cK$ with constant probability. This gives an algorithmic version of a special case of the result of Banaszczyk.
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data
Venue: arXiv cs.CY 2014
Year: 2014

In this paper, we present a novel approach to predict crime in a geographic space from multiple data sources, in particular mobile phone and demographic data. The main contribution of the proposed approach lies in using aggregated and anonymized human behavioral data derived from mobile network activity to tackle the crime prediction problem. While previous research efforts have used either background historical knowledge or offenders' profiling, our findings support the hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime. In our experimental results with real crime data from London we obtain an accuracy of almost 70% when predicting whether a specific area in the city will be a crime hotspot or not. Moreover, we provide a discussion of the implications of our findings for data-driven crime analysis.
A Study of Association Measures and their Combination for Arabic MWT Extraction
Venue: arXiv cs.CL 2014
Year: 2014

Automatic Multi-Word Term (MWT) extraction is a very important issue to many applications, such as information retrieval, question answering, and text categorization. Although many methods have been used for MWT extraction in English and other European languages, few studies have been applied to Arabic. In this paper, we propose a novel, hybrid method which combines linguistic and statistical approaches for Arabic Multi-Word Term extraction. The main contribution of our method is to consider contextual information and both termhood and unithood for association measures at the statistical filtering step. In addition, our technique takes into account the problem of MWT variation in the linguistic filtering step. The performance of the proposed statistical measure (NLC-value) is evaluated using an Arabic environment corpus by comparing it with some existing competitors. Experimental results show that our NLC-value measure outperforms the other ones in term of precision for both bi-grams and tri-grams.