OpenRaiser/Intern-Atlas huggingface dataset:
Blind Identification via Lifting
Venue: arXiv cs.SY 2013
Year: 2013

Blind system identification is known to be an ill-posed problem and without further assumptions, no unique solution is at hand. In this contribution, we are concerned with the task of identifying an ARX model from only output measurements. We phrase this as a constrained rank minimization problem and present a relaxed convex formulation to approximate its solution. To make the problem well posed we assume that the sought input lies in some known linear subspace.
A Novel Hierarchical Ant based QoS aware Intelligent Routing Scheme for MANETS
Venue: arXiv cs.NI 2013
Year: 2013

MANET is a collection of mobile devices with no centralized control and no pre-existing infrastructures. Due to the nodal mobility, supporting QoS during routing in this type of networks is a very challenging task. To tackle this type of overhead many routing algorithms with clustering approach have been proposed. Clustering is an effective method for resource management regarding network performance, routing protocol design, QoS etc. Most of the flat network architecture contains homogeneous capacity of nodes but in real time nodes are with heterogeneous capacity and transmission power. Hierarchical routing provides routing through this kind of heterogeneous nodes. Here, routes can be recorded hierarchically, across clusters to increase routing flexibility. Besides this, it increases scalability and robustness of routes. In this paper, a novel ant based QoS aware routing is proposed on a three level hierarchical cluster based topology in MANET which will be more scalable and efficient compared to flat architecture and will give better throughput.
Implementation of CRISP Methodology for ERP Systems
Venue: arXiv cs.DB 2013
Year: 2013

ERP systems contain huge amounts of data related to the actual execution of business processes. These systems have a particular way of recording activities which results in an unclear display of business processes in event logs. Several works have been conducted on ERP systems, most of them focusing on the development of new algorithms for the automatic discovery of business processes. We focused on addressing issues like, how can organizations with ERP systems apply process mining for analyzing their business processes in order to improve them. The data handling aspect of ERP systems contrasts with those of BPMS or workflow based systems, whose systematical storage of events facilitates the application of process mining techniques. CRISP-DM has emerged as the de facto standard for developing data mining and knowledge discovery projects. Successful data mining requires three families of analytical capabilities namely reporting, classification and forecasting. A data miner uses more than one analytical method to get the best results. The objective of this paper is to improve the usability and understandability of process mining techniques, by implementing CRISP-DM methodology for their application in ERP contexts, detailed in terms of specific implementation tools and step by step coordination. Our study confirms that data discovery from ERP system improves strategic and operational decision making.
Hierarchical complexity of 2-clique-colouring weakly chordal graphs and perfect graphs having cliques of size at least 3
Venue: arXiv cs.CC 2013
Year: 2013

A clique of a graph is a maximal set of vertices of size at least 2 that induces a complete graph. A $k$-clique-colouring of a graph is a colouring of the vertices with at most $k$ colours such that no clique is monochromatic. Défossez proved that the 2-clique-colouring of perfect graphs is a $Σ_2^P$-complete problem [J. Graph Theory 62 (2009) 139--156]. We strengthen this result by showing that it is still $Σ_2^P$-complete for weakly chordal graphs. We then determine a hierarchy of nested subclasses of weakly chordal graphs whereby each graph class is in a distinct complexity class, namely $Σ_2^P$-complete, $\mathcal{NP}$-complete, and $\mathcal{P}$. We solve an open problem posed by Kratochvíl and Tuza to determine the complexity of 2-clique-colouring of perfect graphs with all cliques having size at least 3 [J. Algorithms 45 (2002), 40--54], proving that it is a $Σ_2^P$-complete problem. We then determine a hierarchy of nested subclasses of perfect graphs with all cliques having size at least 3 whereby each graph class is in a distinct complexity class, namely $Σ_2^P$-complete, $\mathcal{NP}$-complete, and $\mathcal{P}$.
Towards Structural Natural Language Formalization: Mapping Discourse to Controlled Natural Language
Venue: arXiv cs.CL 2013
Year: 2013

The author describes a conceptual study towards mapping grounded natural language discourse representation structures to instances of controlled language statements. This can be achieved via a pipeline of preexisting state of the art technologies, namely natural language syntax to semantic discourse mapping, and a reduction of the latter to controlled language discourse, given a set of previously learnt reduction rules. Concludingly a description on evaluation, potential and limitations for ontology-based reasoning is presented.
Parallelization in Extracting Fresh Information from Online Social Network
Venue: arXiv cs.SI 2013
Year: 2013

Online Social Network (OSN) is one of the most hottest services in the past years. It preserves the life of users and provides great potential for journalists, sociologists and business analysts. Crawling data from social network is a basic step for social network information analysis and processing. As the network becomes huge and information on the network updates faster than web pages, crawling is more difficult because of the limitations of band-width, politeness etiquette and computation power. To extract fresh information from social network efficiently and effectively, this paper presents a novel crawling method and discusses parallelization architecture of social network. To discover the feature of social network, we gather data from real social network, analyze them and build a model to describe the discipline of users' behavior. With the modeled behavior, we propose methods to predict users' behavior. According to the prediction, we schedule our crawler more reasonably and extract more fresh information with parallelization technologies. Experimental results demonstrate that our strategies could obtain information from OSN efficiently and effectively.
A Repair Framework for Scalar MDS Codes
Venue: arXiv cs.IT 2013
Year: 2013

Several works have developed vector-linear maximum-distance separable (MDS) storage codes that min- imize the total communication cost required to repair a single coded symbol after an erasure, referred to as repair bandwidth (BW). Vector codes allow communicating fewer sub-symbols per node, instead of the entire content. This allows non trivial savings in repair BW. In sharp contrast, classic codes, like Reed- Solomon (RS), used in current storage systems, are deemed to suffer from naive repair, i.e. downloading the entire stored message to repair one failed node. This mainly happens because they are scalar-linear. In this work, we present a simple framework that treats scalar codes as vector-linear. In some cases, this allows significant savings in repair BW. We show that vectorized scalar codes exhibit properties that simplify the design of repair schemes. Our framework can be seen as a finite field analogue of real interference alignment. Using our simplified framework, we design a scheme that we call clique-repair which provably identifies the best linear repair strategy for any scalar 2-parity MDS code, under some conditions on the sub-field chosen for vectorization. We specify optimal repair schemes for specific (5,3)- and (6,4)-Reed- Solomon (RS) codes. Further, we present a repair strategy for the RS code currently deployed in the Facebook Analytics Hadoop cluster that leads to 20% of repair BW savings over naive repair which is the repair scheme currently used for this code.
Learning about social learning in MOOCs: From statistical analysis to generative model
Venue: arXiv cs.SI 2013
Year: 2013

We study user behavior in the courses offered by a major Massive Online Open Course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education in MOOCs and is done via online discussion forums, our main focus is in understanding forum activities. Two salient features of MOOC forum activities drive our research: 1. High decline rate: for all courses studied, the volume of discussions in the forum declines continuously throughout the duration of the course. 2. High-volume, noisy discussions: at least 30% of the courses produce new discussion threads at rates that are infeasible for students or teaching staff to read through. Furthermore, a substantial portion of the discussions are not directly course-related. We investigate factors that correlate with the decline of activity in the online discussion forums and find effective strategies to classify threads and rank their relevance. Specifically, we use linear regression models to analyze the time series of the count data for the forum activities and make a number of observations, e.g., the teaching staff's active participation in the discussion increases the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and design an effective algorithm for ranking thread relevance. Our ranking algorithm is further compared against two baseline algorithms, using human evaluation from Amazon Mechanical Turk. The authors on this paper are listed in alphabetical order. For media and press coverage, please refer to us collectively, as "researchers from the EDGE Lab at Princeton University, together with collaborators at Boston University and Microsoft Corporation."
On Kinetic Delaunay Triangulations: A Near Quadratic Bound for Unit Speed Motions
Venue: arXiv cs.CG 2013
Year: 2013

Let $P$ be a collection of $n$ points in the plane, each moving along some straight line at unit speed. We obtain an almost tight upper bound of $O(n^{2+ε})$, for any $ε>0$, on the maximum number of discrete changes that the Delaunay triangulation $\mathbb{DT}(P)$ of $P$ experiences during this motion. Our analysis is cast in a purely topological setting, where we only assume that (i) any four points can be co-circular at most three times, and (ii) no triple of points can be collinear more than twice; these assumptions hold for unit speed motions.
VoIP Technology: Security Issues Analysis
Venue: arXiv cs.NI 2013
Year: 2013

Voice over IP (VoIP) is the technology allowing voice and multimedia transmissions as data packets over a private or a public IP network. Thanks to the benefits that it may provide, the VoIP technology is increasingly attracting attention and interest in the industry. Actually, VoIP allows significant benefits for customers and communication services providers such as cost savings, rich media service, phone and service portability, mobility, and the integration with other applications. Nevertheless, the deployment of the VoIP technology encounters many challenges such as architecture complexity, interoperability issues, QoS issues, and security concerns. Among these disadvantages, VoIP security issues are becoming more serious because traditional security devices, protocols, and architectures cannot adequately protect VoIP systems from recent intelligent attacks. The aim of this paper is carry out a deep analysis of the security concerns of the VoIP technology. Firstly, we present a brief overview about the VoIP technology. Then, we discuss security attacks and vulnerabilities related to VoIP protocols and devices. After that, we talk about the security profiles of the VoIP protocols, and we present the main security components designed to help the deployment of a reliable and secured VoIP systems.
Algorithms for Joint Phase Estimation and Decoding for MIMO Systems in the Presence of Phase Noise
Venue: arXiv cs.IT 2013
Year: 2013

In this work, we derive the maximum a posteriori (MAP) symbol detector for a multiple-input multiple-output system in the presence of Wiener phase noise due to noisy local oscillators. As in single-antenna systems, the computation of the optimal receiver is an infinite dimensional problem and is thus unimplementable in practice. In this purview, we propose three suboptimal, low-complexity algorithms for approximately implementing the MAP symbol detector, which involve joint phase noise estimation and data detection. Our first algorithm is obtained by means of the sum-product algorithm, where we use the multivariate Tikhonov canonical distribution approach. In our next algorithm, we derive an approximate MAP symbol detector based on the smoother-detector framework, wherein the detector is properly designed by incorporating the phase noise statistics from the smoother. The third algorithm is derived based on the variational Bayesian framework. By simulations, we evaluate the performance of the proposed algorithms for both uncoded and coded data transmissions, and we observe that the proposed techniques significantly outperform the other algorithms proposed in the literature.
E-Governance, International Cooperation and Security - New Millennium Challenges for a Small Country
Venue: arXiv cs.CY 2013
Year: 2013

In the paper we analyze the Information and Communication Technologies (ICT) and their impact on developing countries (DCs), making a criticism of different views on the supposed role of ICT for the future of human society. This criticism is seen from the point of view of a small developing post-communist country as Albania, hoping that the conclusions would throw some light for developing countries in general, especially those in a transition stage. We examine some aspects of international collaboration and security, where the ICT implemented in the public administration may have an important impact. The development policies and practices are examined, including relations between public and private sectors. Following arguments of many authors, we identify or redefine some crucial factors that negatively impact the role of ICT in the development of the country, its relations with the international community, and ways to push forward its development.
Scalable Object Detection using Deep Neural Networks
Venue: arXiv cs.CV 2013
Year: 2013

Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.
Dominant block guided optimal cache size estimation to maximize IPC of embedded software
Venue: arXiv cs.PF 2013
Year: 2013

Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle. Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine environment. The cache size estimated is cross validated by simulating the set of benchmark applications with different cache sizes in simple scalar simulator. The proposed method seems to be superior in terms of estimation accuracy and estimation time as compared to the existing methods for estimation of optimal cache size parameters like cache line size, number of cache lines.
Novel text categorization by amalgamation of augmented k-nearest neighborhood classification and k-medoids clustering
Venue: arXiv cs.IR 2013
Year: 2013

Machine learning for text classification is the underpinning of document cataloging, news filtering, document steering and exemplification. In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k-Nearest Neighborhood (kNN) has a major pitfall in calculating the similarity between all the objects in training and testing sets, there by leads to exaggeration of both computational complexity of the algorithm and massive consumption of main memory. To diminish these shortcomings in viewpoint of a data-mining practitioner an amalgamative technique is proposed in this paper using a novel restructured version of kNN called AugmentedkNN(AkNN) and k-Medoids(kMdd) clustering.The proposed work comprises preprocesses on the initial training set by imposing attribute feature selection for reduction of high dimensionality, also it detects and excludes the high-fliers samples in the initial training set and restructures a constrictedtraining set. The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category and restructures the constricted training set with centroids. This technique is amalgamated with AkNNclassifier that was prearranged with text mining similarity measures. Eventually, significantweights and ranks were assigned to each object in the new training set based upon their accessory towards the object in testing set. Experiments conducted on Reuters-21578 a UCI benchmark text mining data set, and comparisons with traditional kNNclassifier designates the referredmethod yieldspreeminentrecitalin both clustering and classification.
CEAI: CCM based Email Authorship Identification Model
Venue: arXiv cs.LG 2013
Year: 2013

In this paper we present a model for email authorship identification (EAI) by employing a Cluster-based Classification (CCM) technique. Traditionally, stylometric features have been successfully employed in various authorship analysis tasks; we extend the traditional feature-set to include some more interesting and effective features for email authorship identification (e.g. the last punctuation mark used in an email, the tendency of an author to use capitalization at the start of an email, or the punctuation after a greeting or farewell). We also included Info Gain feature selection based content features. It is observed that the use of such features in the authorship identification process has a positive impact on the accuracy of the authorship identification task. We performed experiments to justify our arguments and compared the results with other base line models. Experimental results reveal that the proposed CCM-based email authorship identification model, along with the proposed feature set, outperforms the state-of-the-art support vector machine (SVM)-based models, as well as the models proposed by Iqbal et al. [1, 2]. The proposed model attains an accuracy rate of 94% for 10 authors, 89% for 25 authors, and 81% for 50 authors, respectively on Enron dataset, while 89.5% accuracy has been achieved on authors' constructed real email dataset. The results on Enron dataset have been achieved on quite a large number of authors as compared to the models proposed by Iqbal et al. [1, 2].
An Analog Baseband Approach for Designing Full-Duplex Radios
Venue: arXiv cs.NI 2013
Year: 2013

Recent wireless testbed implementations have proven that full-duplex communication is in fact possible and can outperform half-duplex systems. Many of these implementations modify existing half-duplex systems to operate in full-duplex. To realize the full potential of full-duplex, radios need to be designed with self-interference in mind. In our work, we use an experimental setup with a patch antenna prototype to characterize the self-interference channel between two radios. In doing so, we form an analytical model to design analog baseband cancellation techniques. We show that our cancellation scheme can provide up to 10 dB improved signal strength, 2.5 bps/Hz increase in rate, and a 10,000 improvement in BER as compared to the RF only cancellation provided by the patch antenna.
Data Structures for Task-based Priority Scheduling
Venue: arXiv cs.DC 2013
Year: 2013

Many task-parallel applications can benefit from attempting to execute tasks in a specific order, as for instance indicated by priorities associated with the tasks. We present three lock-free data structures for priority scheduling with different trade-offs on scalability and ordering guarantees. First we propose a basic extension to work-stealing that provides good scalability, but cannot provide any guarantees for task-ordering in-between threads. Next, we present a centralized priority data structure based on $k$-fifo queues, which provides strong (but still relaxed with regard to a sequential specification) guarantees. The parameter $k$ allows to dynamically configure the trade-off between scalability and the required ordering guarantee. Third, and finally, we combine both data structures into a hybrid, $k$-priority data structure, which provides scalability similar to the work-stealing based approach for larger $k$, while giving strong ordering guarantees for smaller $k$. We argue for using the hybrid data structure as the best compromise for generic, priority-based task-scheduling. We analyze the behavior and trade-offs of our data structures in the context of a simple parallelization of Dijkstra's single-source shortest path algorithm. Our theoretical analysis and simulations show that both the centralized and the hybrid $k$-priority based data structures can give strong guarantees on the useful work performed by the parallel Dijkstra algorithm. We support our results with experimental evidence on an 80-core Intel Xeon system.