OpenRaiser/Intern-Atlas huggingface dataset:
Establishing Role-based Access Control in Viewpoint-oriented Variability Management
Venue: arXiv cs.SE 2017
Year: 2017

Process roles are used to structure complex engineering processes in single sys-tems development for many years. Typically, each role has specific responsi-bilities from which certain information demands originate. In the engineering of variable software, role-specific information demands affect variability in-formation. To control the access to the variability information, we suggest us-ing the concepts of an explicit access control model. We integrate an access control model and a variability modeling language on a conceptual level. Ad-ditionally, we extend the variability modeling language by formally defined operations. Based on this extension, we propose a formal integration of an ac-cess control model and the variability modeling language. Our solution allows to explicitly define access control to variability information on a formal basis.
Introduction to a Temporal Graph Benchmark
Venue: arXiv cs.SI 2017
Year: 2017

A temporal graph is a data structure, consisting of nodes and edges in which the edges are associated with time labels. To analyze the temporal graph, the first step is to find a proper graph dataset/benchmark. While many temporal graph datasets exist online, none could be found that used the interval labels in which each edge is associated with a starting and ending time. Therefore we create a temporal graph data based on Wikipedia reference graph for temporal analysis. This report aims to provide more details of this graph benchmark to those who are interested in using it.
A Study of MAC Address Randomization in Mobile Devices and When it Fails
Venue: arXiv cs.CR 2017
Year: 2017

MAC address randomization is a privacy technique whereby mobile devices rotate through random hardware addresses in order to prevent observers from singling out their traffic or physical location from other nearby devices. Adoption of this technology, however, has been sporadic and varied across device manufacturers. In this paper, we present the first wide-scale study of MAC address randomization in the wild, including a detailed breakdown of different randomization techniques by operating system, manufacturer, and model of device. We then identify multiple flaws in these implementations which can be exploited to defeat randomization as performed by existing devices. First, we show that devices commonly make improper use of randomization by sending wireless frames with the true, global address when they should be using a randomized address. We move on to extend the passive identification techniques of Vanhoef et al. to effectively defeat randomization in ~96% of Android phones. Finally, we show a method that can be used to track 100% of devices using randomization, regardless of manufacturer, by exploiting a previously unknown flaw in the way existing wireless chipsets handle low-level control frames.
Large-scale image analysis using docker sandboxing
Venue: arXiv cs.CV 2017
Year: 2017

With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. Designing scalable software architecture that co-evolves with such specialized hardware is a challenge in the commercial setting. In this paper, we describe one such architecture (\textit{Cortexica}) that leverages scalability of GPUs and sandboxing offered by docker containers. This allows for the flexibility of mixing different computer architectures as well as computational algorithms with the security of a trusted environment. We illustrate the utility of this framework in a commercial setting i.e., searching for multiple products in an image by combining image localisation and retrieval.
Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons
Venue: arXiv cs.CV 2017
Year: 2017

HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel double-stage classification approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and 3D skeleton joint data, able to reach high performances maintaining all advantages listed above. The approach allows both to quickly classify pre-segmented gestures (offline classification), and to perform temporal segmentation on streams of gestures (online classification) faster than real time. We test our system on three public datasets, MSRAction3D, UTKinect-Action and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created for Human Computer Interaction (HCI). We obtain state of the art performances on all of them.
Simple synchronization protocols for heterogeneous networks: beyond passivity (extended version)
Venue: arXiv cs.SY 2017
Year: 2017

Synchronization among autonomous agents via local interactions is one of the benchmark problems in multi-agent control. Whereas synchronization algorithms for identical agents have been thoroughly studied, synchronization of heterogeneous networks still remains a challenging problem. The existing algorithms primarily use the internal model principle, assigning to each agent a local copy of some dynamical system (internal model). Synchronization of heterogeneous agents thus reduces to global synchronization of identical generators and local synchronization between the agents and their internal models. The internal model approach imposes a number of restrictions and leads to sophisticated dynamical (and, in general, nonlinear) controllers. At the same time, passive heterogeneous agents can be synchronized by a very simple linear protocol, which is used for consensus of first-order integrators. A natural question arises whether analogous algorithms are applicable to synchronization of agents that do not satisfy the passivity condition. In this paper, we study the synchronization problem for heterogeneous agents that are not passive but satisfy a weaker input feedforward passivity (IFP) condition. We show that such agents can also be synchronized by a simple linear protocol, provided that the interaction graph is strongly connected and the couplings are sufficiently weak. We demonstrate how stability of cooperative adaptive cruise control algorithms and some microscopic traffic flow models reduce to synchronization of heterogeneous IFP agents.
"Tell me and I forget, teach me and I may remember, involve me and I learn": changing the approach of teaching Computer Organization
Venue: arXiv cs.CY 2017
Year: 2017

Millennials are arriving to university sometimes uncomfortable with the methods of some courses. Ideas that worked with previous generations of students begin to fail when digital natives receive paper and pencil as tools. Courses must update from old paper-based methods to hands-on and computerized versions. The present work discusses about this update and comments on one implementation in the course Computer Organization of the Computer Science curriculum at Universidad de Buenos Aires. It also includes some metrics that show the effectiveness of the changes in attracting and engaging the digital generation.
A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to arbitrary measurement matrix $\boldsymbol{A}$ than the standard damped GAMP algorithm while being much lower complexity than the standard SBL algorithm. We then extend the approach from the single measurement vector (SMV) case to the temporally correlated multiple measurement vector (MMV) case, leading to the GGAMP-TSBL algorithm. We verify the robustness and computational advantages of the proposed algorithms through numerical experiments.
Orthogonal Transform Multiplexing with Memoryless Nonlinearity: a Possible Alternative to Traditional Coded-Modulation Schemes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a novel joint coding-modulation technique based on serial concatenation of orthogonal linear transform, such as discrete Fourier transform (DFT) or Walsh-Hadamard transform (WHT), with memoryless nonlinearity. We demonstrate that such a simple signal construction may exhibit properties of a random code ensemble, as a result approaching channel capacity. Our computer simulations confirm that if the decoder relies on a modified approximate message passing algorithm, the proposed modulation technique exhibits performance on par with state-of-the-art coded modulation schemes that use capacity-approaching component codes. The proposed modulation scheme could be used directly or as a pre-coder for a conventional orthogonal frequency division multiplexing (OFDM) transmitter, resulting in a system possessing all benefits of OFDM along with reduced peak-to-average power ratio (PAPR).
Detecting Sockpuppets in Deceptive Opinion Spam
Venue: arXiv cs.CL 2017
Year: 2017

This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches. Two methods are explored. The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features. The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors. Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.
Prior-based Hierarchical Segmentation Highlighting Structures of Interest
Venue: arXiv cs.CV 2017
Year: 2017

Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.
Turkish PoS Tagging by Reducing Sparsity with Morpheme Tags in Small Datasets
Venue: arXiv cs.CL 2017
Year: 2017

Sparsity is one of the major problems in natural language processing. The problem becomes even more severe in agglutinating languages that are highly prone to be inflected. We deal with sparsity in Turkish by adopting morphological features for part-of-speech tagging. We learn inflectional and derivational morpheme tags in Turkish by using conditional random fields (CRF) and we employ the morpheme tags in part-of-speech (PoS) tagging by using hidden Markov models (HMMs) to mitigate sparsity. Results show that using morpheme tags in PoS tagging helps alleviate the sparsity in emission probabilities. Our model outperforms other hidden Markov model based PoS tagging models for small training datasets in Turkish. We obtain an accuracy of 94.1% in morpheme tagging and 89.2% in PoS tagging on a 5K training dataset.
$Σ^μ_2$ is decidable for $Π^μ_2$
Venue: arXiv cs.LO 2017
Year: 2017

Given a $Π^μ_2$ formula of the modal $μ$ calculus, it is decidable whether it is equivalent to a $Σ^μ_2$ formula.
PMU-Based Estimation of Dynamic State Jacobian Matrix
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, a hybrid measurement- and model-based method is proposed which can estimate the dynamic state Jacobian matrix in near real-time. The proposed method is computationally efficient and robust to the variation of network topology. A numerical example is given to show that the proposed method is able to provide good estimation for the dynamic state Jacobian matrix and is superior to the model-based method under undetectable network topology change. The proposed method may also help identify big discrepancy in the assumed network model.
Multi-sensor authentication to improve smartphone security
Venue: arXiv cs.CR 2017
Year: 2017

The widespread use of smartphones gives rise to new security and privacy concerns. Smartphone thefts account for the largest percentage of thefts in recent crime statistics. Using a victim's smartphone, the attacker can launch impersonation attacks, which threaten the security of the victim and other users in the network. Our threat model includes the attacker taking over the phone after the user has logged on with his password or pin. Our goal is to design a mechanism for smartphones to better authenticate the current user, continuously and implicitly, and raise alerts when necessary. In this paper, we propose a multi-sensors-based system to achieve continuous and implicit authentication for smartphone users. The system continuously learns the owner's behavior patterns and environment characteristics, and then authenticates the current user without interrupting user-smartphone interactions. Our method can adaptively update a user's model considering the temporal change of user's patterns. Experimental results show that our method is efficient, requiring less than 10 seconds to train the model and 20 seconds to detect the abnormal user, while achieving high accuracy (more than 90%). Also the combination of more sensors provide better accuracy. Furthermore, our method enables adjusting the security level by changing the sampling rate.
Identifying User Survival Types via Clustering of Censored Social Network Data
Venue: arXiv cs.SI 2017
Year: 2017

The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily in the medical domain with relatively small datasets, but the need for such a clustering methodology could arise in other domains with large datasets, such as social networks. Concretely, we wish to identify different survival classes in a social network by clustering the users based on their lifespan in the network. In this paper, we propose a decision tree based algorithm that uses a global normalization of $p$-values to identify clusters with significantly different survival distributions. We evaluate the clusters from our model with the help of a simple survival prediction task and show that our model outperforms other competing methods.
Temporal evolution of notoriety of Wikipedia pages with origin in social networks
Venue: arXiv cs.SI 2017
Year: 2017

The Wikipedia is a web portal created by users and its simplicity, references and also the inclusion as insets introductory paragraphs for their pages in Google search results have made it the go-to place to find out about current events or people featured in them. Besides, its open application programming interface (API) allows any user to know about the number of visits some particular page has. In this paper, after certain events that made Copernicus a viral meme in Spain, we study the intensity and duration of the increment of visits to his page and other pages related to the event. Using pages related to other persons as a comparison, we try to establish a typical duration of notoriety achieved through social networks, mainly comparing with visits in previous years in the same dates. We conclude that a 7 day duration is the statistical mode and that this duration is relatively independent of the initial increase in the number of visits.
An Accurate and Efficient Method to Calculate the Error Statistics of Block-based Approximate Adders
Venue: arXiv cs.ET 2017
Year: 2017

Adders are key building blocks of many error-tolerant applications. Leveraging the application-level error tolerance, a number of approximate adders were proposed recently. Many of them belong to the category of block-based approximate adders. For approximate circuits, besides normal metrics such as area and delay, another important metric is the error measurement. Given the popularity of block-based approximate adders, in this work, we propose an accurate and efficient method to obtain the error statistics of these adders. We first show how to calculate the error rates. Then, we demonstrate an approach to get the exact error distribution, which can be used to calculate other error characteristics, such as mean error distance and mean square error.
The xDotGrid Native, Cross-Platform, High-Performance xDFS File Transfer Framework
Venue: arXiv cs.DC 2017
Year: 2017

In this paper we introduce and describe the highly concurrent xDFS file transfer protocol and examine its cross-platform and cross-language implementation in native code for both Linux and Windows in 32 or 64-bit multi-core processor architectures. The implemented xDFS protocol based on xDotGrid.NET framework is fully compared with the Globus GridFTP protocol. We finally propose the xDFS protocol as a new paradigm of distributed systems for Internet services, and data-intensive Grid and Cloud applications. Also, we incrementally consider different developmental methods of the optimum file transfer systems, and their advantages and disadvantages. The vision of this paper tries as possible to minimize the overhead concerned with the file transfer protocol itself and to examine optimal software design patterns of that protocol. In all disk-to-disk tests for transferring a 2GB file with or without parallelism, the xDFS throughput at minimum 30% and at most 53% was superior to the GridFTP.
Performance Analysis of Mixed-ADC Massive MIMO Systems over Rician Fading Channels
Venue: arXiv cs.IT 2017
Year: 2017

The practical deployment of massive multiple-input multiple-output (MIMO) in future fifth generation (5G) wireless communication systems is challenging due to its high hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things (IoT). Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician $K$-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the trade-off between the achievable rate and energy efficiency with respect to key system parameters including the quantization bits, number of BS antennas, Rician $K$-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate trade-off compared with the ideal infinite-resolution and low-resolution ADC architectures.
Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach
Venue: arXiv cs.SI 2017
Year: 2017

Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition.
Survey on Essential and Accidental Real-Time Issues in Software Engineering
Venue: arXiv cs.SE 2017
Year: 2017

Software product lines have recently been presented as one of the best promising improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the problems of producing a perfect software scheme. Traditional approaches or recycling software are not effective to solve the problems concerning software competence. Since fast developments with software engineering in the past few years, studies show that some approaches are getting extensive attention in both industries and universities. This method is categorized as the software product line improvement; that supports reusing of software in big organizations. Different industries are adopting product lines to enhance efficiency and reduce operational expenses by way of emerging product developments. This research paper is formed to offer in-depth study regarding the software engineering issues such as complexity, conformity, changeability, invisibility, time constraints, budget constraints, and security. We have conducted various research surveys by visiting different professional software development organizations and took feedback from the professional software engineers to analyze the real-time problems that they are facing during the development process of software systems. Survey results proved that complexity is a most occurring issue that most software developers face while developing software applications. Moreover, invisibility is the problem that rarely happens according to the survey.
A Genetic Algorithm-based Beamforming Approach for Delay-constrained Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the performance of initial access beamforming schemes in the cases with large but finite number of transmit antennas and users. Particularly, we develop an efficient beamforming scheme using genetic algorithms. Moreover, taking the millimeter wave communication characteristics and different metrics into account, we investigate the effect of various parameters such as number of antennas/receivers, beamforming resolution as well as hardware impairments on the system performance. As shown, our proposed algorithm is generic in the sense that it can be effectively applied with different channel models, metrics and beamforming methods. Also, our results indicate that the proposed scheme can reach (almost) the same end-to-end throughput as the exhaustive search-based optimal approach with considerably less implementation complexity.