OpenRaiser/Intern-Atlas huggingface dataset:
Traffic Management Applications for Stateful SDN Data Plane
Venue: arXiv cs.NI 2015
Year: 2015

The successful OpenFlow approach to Software Defined Networking (SDN) allows network programmability through a central controller able to orchestrate a set of dumb switches. However, the simple match/action abstraction of OpenFlow switches constrains the evolution of the forwarding rules to be fully managed by the controller. This can be particularly limiting for a number of applications that are affected by the delay of the slow control path, like traffic management applications. Some recent proposals are pushing toward an evolution of the OpenFlow abstraction to enable the evolution of forwarding policies directly in the data plane based on state machines and local events. In this paper, we present two traffic management applications that exploit a stateful data plane and their prototype implementation based on OpenState, an OpenFlow evolution that we recently proposed.
An Enhanced Apriori Algorithm for Discovering Frequent Patterns with Optimal Number of Scans
Venue: arXiv cs.DB 2015
Year: 2015

Data mining is wide spreading its applications in several areas. There are different tasks in mining which provides solutions for wide variety of problems in order to discover knowledge. Among those tasks association mining plays a pivotal role for identifying frequent patterns. Among the available association mining algorithms Apriori algorithm is one of the most prevalent and dominant algorithm which is used to discover frequent patterns. This algorithm is used to discover frequent patterns from small to large databases. This paper points toward the inadequacy of the tangible Apriori algorithm of wasting time for scanning the whole transactional database for discovering association rules and proposes an enhancement on Apriori algorithm to overcome this problem. This enhancement is obtained by dropping the amount of time used in scanning the transactional database by just limiting the number of transactions while calculating the frequency of an item or item-pairs. This improved version of Apriori algorithm optimizes the time used for scanning the whole transactional database.
Construction of power flow feasibility sets
Venue: arXiv cs.SY 2015
Year: 2015

We develop a new approach for construction of convex analytically simple regions where the AC power flow equations are guaranteed to have a feasible solutions. Construction of these regions is based on efficient semidefinite programming techniques accelerated via sparsity exploiting algorithms. Resulting regions have a simple geometric shape in the space of power injections (polytope or ellipsoid) and can be efficiently used for assessment of system security in the presence of uncertainty. Efficiency and tightness of the approach is validated on a number of test networks.
Capacity Regions of Two-User Broadcast Erasure Channels with Feedback and Hidden Memory
Venue: arXiv cs.IT 2015
Year: 2015

The two-receiver broadcast packet erasure channel with feedback and memory is studied. Memory is modeled using a finite-state Markov chain representing a channel state. The channel state is unknown at the transmitter, but observations of this hidden Markov chain are available at the transmitter through feedback. Matching outer and inner bounds are derived and the capacity region is determined. The capacity region does not have a single-letter characterization and is, in this sense, uncomputable. Approximations of the capacity region are provided and two optimal coding algorithms are outlined. The first algorithm is a probabilistic coding scheme that bases its decisions on the past L feedback sequences. Its achievable rate-region approaches the capacity region exponentially fast in L. The second algorithm is a backpressure-like algorithm that performs optimally in the long run.
Convolution and Product Theorem for the Special Affine Fourier Transform
Venue: arXiv cs.IT 2015
Year: 2015

The Special Affine Fourier Transform or the SAFT generalizes a number of well known unitary transformations as well as signal processing and optics related mathematical operations. Unlike the Fourier transform, the SAFT does not work well with the standard convolution operation. Recently, Q. Xiang and K. Y. Qin introduced a new convolution operation that is more suitable for the SAFT and by which the SAFT of the convolution of two functions is the product of their SAFTs and a phase factor. However, their convolution structure does not work well with the inverse transform in sofar as the inverse transform of the product of two functions is not equal to the convolution of the transforms. In this article we introduce a new convolution operation that works well with both the SAFT and its inverse leading to an analogue of the convolution and product formulas for the Fourier transform. Furthermore, we introduce a second convolution operation that leads to the elimination of the phase factor in the convolution formula obtained by Q. Xiang and K. Y. Qin.
$1$-String $B_1$-VPG Representations of Planar Partial $3$-Trees and Some Subclasses
Venue: arXiv cs.CG 2015
Year: 2015

Planar partial $3$-trees are subgraphs of those planar graphs obtained by repeatedly inserting a vertex of degree $3$ into a face. In this paper, we show that planar partial $3$-trees have $1$-string $B_1$-VPG representations, i.e., representations where every vertex is represented by an orthogonal curve with at most one bend, every two curves intersect at most once, and intersections of curves correspond to edges in the graph. We also that some subclasses of planar partial 3-trees have L-representations, i.e., a $B_1$-VPG representation where every curve has the shape of an L.
Packing coloring of some undirected and oriented coronae graphs
Venue: arXiv cs.DM 2015
Year: 2015

The packing chromatic number $\pcn(G)$ of a graph $G$ is the smallest integer $k$ such that its set of vertices $V(G)$ can be partitioned into $k$ disjoint subsets $V\_1$, \ldots, $V\_k$, in such a way that every two distinct vertices in $V\_i$ are at distance greater than $i$ in $G$ for every $i$, $1\le i\le k$. For a given integer $p \ge 1$, the generalized corona $G\odot pK\_1$ of a graph $G$ is the graph obtained from $G$ by adding $p$ degree-one neighbors to every vertex of $G$. In this paper, we determine the packing chromatic number of generalized coronae of paths and cycles. Moreover, by considering digraphs and the (weak) directed distance between vertices, we get a natural extension of the notion of packing coloring to digraphs. We then determine the packing chromatic number of orientations of generalized coronae of paths and cycles.
Algorithmic Aspects of Upper Domination
Venue: arXiv cs.CC 2015
Year: 2015

In this paper we study combinatorial and algorithmic resp. complexity questions of upper domination, i.e., the maximum cardinality of a minimal dominating set in a graph. We give a full classification of the related maximisation and minimisation problems, as well as the related parameterised problems, on general graphs and on graphs of bounded degree, and we also study planar graphs.
Survey on Network Virtualization Hypervisors for Software Defined Networking
Venue: arXiv cs.NI 2015
Year: 2015

Software defined networking (SDN) has emerged as a promising paradigm for making the control of communication networks flexible. SDN separates the data packet forwarding plane, i.e., the data plane, from the control plane and employs a central controller. Network virtualization allows the flexible sharing of physical networking resources by multiple users (tenants). Each tenant runs its own applications over its virtual network, i.e., its slice of the actual physical network. The virtualization of SDN networks promises to allow networks to leverage the combined benefits of SDN networking and network virtualization and has therefore attracted significant research attention in recent years. A critical component for virtualizing SDN networks is an SDN hypervisor that abstracts the underlying physical SDN network into multiple logically isolated virtual SDN networks (vSDNs), each with its own controller. We comprehensively survey hypervisors for SDN networks in this article. We categorize the SDN hypervisors according to their architecture into centralized and distributed hypervisors. We furthermore sub-classify the hypervisors according to their execution platform into hypervisors running exclusively on general-purpose compute platforms, or on a combination of general-purpose compute platforms with general- or special-purpose network elements. We exhaustively compare the network attribute abstraction and isolation features of the existing SDN hypervisors. As part of the future research agenda, we outline the development of a performance evaluation framework for SDN hypervisors.
Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
Venue: arXiv cs.CV 2015
Year: 2015

Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition. Experiments show that this method outperforms other state-of-the-art methods on LFW dataset, achieving 99.77% pair-wise verification accuracy and significantly better accuracy under other two more practical protocols. This paper also discusses the importance of data size and the number of patches, showing a clear path to practical high-performance face recognition systems in real world.
New families of optimal frequency hopping sequence sets
Venue: arXiv cs.IT 2015
Year: 2015

Frequency hopping sequences (FHSs) are employed to mitigate the interferences caused by the hits of frequencies in frequency hopping spread spectrum systems. In this paper, we present some new algebraic and combinatorial constructions for FHS sets, including an algebraic construction via the linear mapping, two direct constructions by using cyclotomic classes and recursive constructions based on cyclic difference matrices. By these constructions, a number of series of new FHS sets are then produced. These FHS sets are optimal with respect to the Peng-Fan bounds.
Speech Controlled Quadruped
Venue: arXiv cs.RO 2015
Year: 2015

The project which we have performed is based on voice recognition and we desire to create a four legged robot that can acknowledge the given instructions which are given through vocal commands and perform the tasks. The main processing unit of the robot will be Arduino Uno. We are using 8 servos for the movement of its legs while two servos will be required for each leg. The interface between a human and the robot is generated through Python programming and Eclipse software and it is implemented by using Bluetooth module HC 06.
The Vector Space of Convex Curves: How to Mix Shapes
Venue: arXiv cs.GR 2015
Year: 2015

We present a novel, log-radius profile representation for convex curves and define a new operation for combining the shape features of curves. Unlike the standard, angle profile-based methods, this operation accurately combines the shape features in a visually intuitive manner. This method have implications in shape analysis as well as in investigating how the brain perceives and generates curved shapes and motions.
Learning Representations from Deep Networks Using Mode Synthesizers
Venue: arXiv cs.NE 2015
Year: 2015

Deep learning Networks play a crucial role in the evolution of a vast number of current machine learning models for solving a variety of real world non-trivial tasks. Such networks use big data which is generally unlabeled unsupervised and multi-layered requiring no form of supervision for training and learning data and has been used to successfully build automatic supervisory neural networks. However the question still remains how well the learned data represents interestingness, and their effectiveness i.e. efficiency in deep learning models or applications. If the output of a network of deep learning models can be beamed unto a scene of observables, we could learn the variational frequencies of these stacked networks in a parallel and distributive way.This paper seeks to discover and represent interesting patterns in an efficient and less complex way by incorporating the concept of Mode synthesizers in the deep learning process models
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Venue: arXiv cs.LG 2015
Year: 2015

Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node distributed systems. Splash consists of a programming interface and an execution engine. Using the programming interface, the user develops sequential stochastic algorithms without concerning any detail about distributed computing. The algorithm is then automatically parallelized by a communication-efficient execution engine. We provide theoretical justifications on the optimal rate of convergence for parallelizing stochastic gradient descent. Splash is built on top of Apache Spark. The real-data experiments on logistic regression, collaborative filtering and topic modeling verify that Splash yields order-of-magnitude speedup over single-thread stochastic algorithms and over state-of-the-art implementations on Spark.
Towards Resistance Sparsifiers
Venue: arXiv cs.DS 2015
Year: 2015

We study resistance sparsification of graphs, in which the goal is to find a sparse subgraph (with reweighted edges) that approximately preserves the effective resistances between every pair of nodes. We show that every dense regular expander admits a $(1+ε)$-resistance sparsifier of size $\tilde O(n/ε)$, and conjecture this bound holds for all graphs on $n$ nodes. In comparison, spectral sparsification is a strictly stronger notion and requires $Ω(n/ε^2)$ edges even on the complete graph. Our approach leads to the following structural question on graphs: Does every dense regular expander contain a sparse regular expander as a subgraph? Our main technical contribution, which may of independent interest, is a positive answer to this question in a certain setting of parameters. Combining this with a recent result of von Luxburg, Radl, and Hein~(JMLR, 2014) leads to the aforementioned resistance sparsifiers.
CRAFT: ClusteR-specific Assorted Feature selecTion
Venue: arXiv cs.LG 2015
Year: 2015

We present a framework for clustering with cluster-specific feature selection. The framework, CRAFT, is derived from asymptotic log posterior formulations of nonparametric MAP-based clustering models. CRAFT handles assorted data, i.e., both numeric and categorical data, and the underlying objective functions are intuitively appealing. The resulting algorithm is simple to implement and scales nicely, requires minimal parameter tuning, obviates the need to specify the number of clusters a priori, and compares favorably with other methods on real datasets.
Sensor Selection Scheme in Temperature Wireless Sensor Network
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we propose a novel energy efficient environment monitoring scheme for wireless sensor networks, based on data mining formulation. The proposed adapting routing scheme for sensors for achieving energy efficiency from temperature wireless sensor network data set. The experimental validation of the proposed approach using publicly available Intel Berkeley lab Wireless Sensor Network dataset shows that it is possible to achieve energy efficient environment monitoring for wireless sensor networks, with a trade-off between accuracy and life time extension factor of sensors, using the proposed approach.
Network Aggregative Games and Distributed Mean Field Control via Consensus Theory
Venue: arXiv cs.SY 2015
Year: 2015

We consider network aggregative games to model and study multi-agent populations in which each rational agent is influenced by the aggregate behavior of its neighbors, as specified by an underlying network. Specifically, we examine systems where each agent minimizes a quadratic cost function, that depends on its own strategy and on a convex combination of the strategies of its neighbors, and is subject to personalized convex constraints. We analyze the best response dynamics and we propose alternative distributed algorithms to steer the strategies of the rational agents to a Nash equilibrium configuration. The convergence of these schemes is guaranteed under different sufficient conditions, depending on the matrices defining the cost and on the network. Additionally, we propose an extension to the network aggregative game setting that allows for multiple rounds of communications among the agents, and we illustrate how it can be combined with consensus theory to recover a solution to the mean field control problem in a distributed fashion, that is, without requiring the presence of a central coordinator. Finally, we apply our theoretical findings to study a novel multi-dimensional, convex-constrained model of opinion dynamics and a hierarchical demand-response scheme for energy management in smart buildings, extending literature results.
Auditable Restoration of Distributed Programs
Venue: arXiv cs.DC 2015
Year: 2015

We focus on a protocol for auditable restoration of distributed systems. The need for such protocol arises due to conflicting requirements (e.g., access to the system should be restricted but emergency access should be provided). One can design such systems with a tamper detection approach (based on the intuition of "break the glass door"). However, in a distributed system, such tampering, which are denoted as auditable events, is visible only for a single node. This is unacceptable since the actions they take in these situations can be different than those in the normal mode. Moreover, eventually, the auditable event needs to be cleared so that system resumes the normal operation. With this motivation, in this paper, we present a protocol for auditable restoration, where any process can potentially identify an auditable event. Whenever a new auditable event occurs, the system must reach an "auditable state" where every process is aware of the auditable event. Only after the system reaches an auditable state, it can begin the operation of restoration. Although any process can observe an auditable event, we require that only "authorized" processes can begin the task of restoration. Moreover, these processes can begin the restoration only when the system is in an auditable state. Our protocol is self-stabilizing and has bounded state space. It can effectively handle the case where faults or auditable events occur during the restoration protocol. Moreover, it can be used to provide auditable restoration to other distributed protocol.
A Study on the Effect of Exit Widths and Crowd Sizes in the Formation of Arch in Clogged Crowds
Venue: arXiv cs.MA 2015
Year: 2015

The arching phenomenon is an emergent pattern formed by a $c$-sized crowd of intelligent, goal-oriented, autonomous, heterogeneous individuals moving towards a $w$-wide exit along a long $W$-wide corridor, where $W>w$. We collected empirical data from microsimulations to identify the combination effects of~$c$ and~$w$ to the time~$T$ of the onset of and the size~$S$ of the formation of the arch. The arch takes on the form of the perimeter of a half ellipse halved along the minor axis. We measured the~$S$ with respect to the lengths of the major~$M$ and minor~$m$ axes of the ellipse, respectively. The mathematical description of the formation of this phenomenon will be an important information in the design of walkways to control and easily direct the flow of large crowds, especially during panic egress conditions.
Trigger detection for adaptive scientific workflows using percentile sampling
Venue: arXiv cs.CE 2015
Year: 2015

Increasing complexity of scientific simulations and HPC architectures are driving the need for adaptive workflows, where the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state of the simulation itself. Consider for example, the frequency of data storage. Critical phases of the simulation should be captured with high frequency and with high fidelity for post-analysis, however we cannot afford to retain the same frequency for the full simulation due to the high cost of data movement. We can instead look for triggers, indicators that the simulation will be entering a critical phase and adapt the workflow accordingly. We present a method for detecting triggers and demonstrate its use in direct numerical simulations of turbulent combustion using S3D. We show that chemical explosive mode analysis (CEMA) can be used to devise a noise-tolerant indicator for rapid increase in heat release. However, exhaustive computation of CEMA values dominates the total simulation, thus is prohibitively expensive. To overcome this bottleneck, we propose a quantile-sampling approach. Our algorithm comes with provable error/confidence bounds, as a function of the number of samples. Most importantly, the number of samples is independent of the problem size, thus our proposed algorithm offers perfect scalability. Our experiments on homogeneous charge compression ignition (HCCI) and reactivity controlled compression ignition (RCCI) simulations show that the proposed method can detect rapid increases in heat release, and its computational overhead is negligible. Our results will be used for dynamic workflow decisions about data storage and mesh resolution in future combustion simulations. Proposed framework is generalizable and we detail how it could be applied to a broad class of scientific simulation workflows.
Optimizing Data Forwarding from Body Area Networks in the Presence of Body Shadowing with Dual Wireless Technology Nodes
Venue: arXiv cs.NI 2015
Year: 2015

In this paper we are concerned with the problem of data forwarding from a wireless body area network (WBAN) to a gateway when body shadowing affects the ability of WBAN nodes to communicate with the gateway. To solve this problem we present a new WBAN architecture that uses two communication technologies. One network is formed between on-body nodes, and is realized with capacitive body-coupled communication (BCC), while an IEEE 802.15.4 radio frequency (RF) network is used for forwarding data to the gateway. WBAN nodes that have blocked RF links due to body shadowing forward their data through the BCC link to a node that acts as a relay and has an active RF connection. For this architecture we design a network layer protocol that manages the two communication technologies and is responsible for relay selection and data forwarding. Next, we develop analytical performance models of the medium access control (MAC) protocols of the two independent communication links in order to be used for driving the decisions of the previous algorithms. Finally, the analytical models are used for further optimizing energy and delay efficiency. We test our system under different configurations first by performing simulations and next by using real RF traces.