OpenRaiser/Intern-Atlas huggingface dataset:
Symblicit algorithms for optimal strategy synthesis in monotonic Markov decision processes
Venue: arXiv cs.LO 2014
Year: 2014

When treating Markov decision processes (MDPs) with large state spaces, using explicit representations quickly becomes unfeasible. Lately, Wimmer et al. have proposed a so-called symblicit algorithm for the synthesis of optimal strategies in MDPs, in the quantitative setting of expected mean-payoff. This algorithm, based on the strategy iteration algorithm of Howard and Veinott, efficiently combines symbolic and explicit data structures, and uses binary decision diagrams as symbolic representation. The aim of this paper is to show that the new data structure of pseudo-antichains (an extension of antichains) provides another interesting alternative, especially for the class of monotonic MDPs. We design efficient pseudo-antichain based symblicit algorithms (with open source implementations) for two quantitative settings: the expected mean-payoff and the stochastic shortest path. For two practical applications coming from automated planning and LTL synthesis, we report promising experimental results w.r.t. both the run time and the memory consumption.
TFPaaS : Test-first Performance as a Service to Cloud for Software Testing Environment
Venue: arXiv cs.SE 2014
Year: 2014

Performance Testing is critical for applications like web services and e-commerce platforms to ensure enhanced end user experience. In such cases, starting to test the system's performance early should significantly reduce the overall development cost. Test-first Performance (TFP) is one such paradigm that allows performance testing right from the early stage of development. Given such potential benefit, this paper proposes the design of a testing framework IVRIDIO which introduces TFP as a Service (TFPaaS). IVRIDIO incorporates the Plugin for TFP in the Cloud (PTFPC) aiming to provide instant feedbacks - a prime requirement of TFP to immediately fix critical performance issues. Furthermore, the Convention over Configuration (CoC) design paradigm has been applied by introducing a configurable project template to maintain TFP test cases. The prototyping details of the framework are given and the variation of response time to the inclusion of PTFPC has also been discussed. The Summated Usability Metric (SUM) score has been provided so that it can later be used for comparing the PTFPC plugin's usability.
Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid
Venue: arXiv cs.SY 2014
Year: 2014

This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.
Automated Inference of Past Action Instances in Digital Investigations
Venue: arXiv cs.CR 2014
Year: 2014

As the amount of digital devices suspected of containing digital evidence increases, case backlogs for digital investigations are also increasing in many organizations. To ensure timely investigation of requests, this work proposes the use of signature-based methods for automated action instance approximation to automatically reconstruct past user activities within a compromised or suspect system. This work specifically explores how multiple instances of a user action may be detected using signature-based methods during a post-mortem digital forensic analysis. A system is formally defined as a set of objects, where a subset of objects may be altered on the occurrence of an action. A novel action-trace update time threshold is proposed that enables objects to be categorized by their respective update patterns over time. By integrating time into event reconstruction, the most recent action instance approximation as well as limited past instances of the action may be differentiated and their time values approximated. After the formal theory if signature-based event reconstruction is defined, a case study is given to evaluate the practicality of the proposed method.
Distributed Cross-layer Dynamic Route Selection in Wireless Multiuser Multihop Networks
Venue: arXiv cs.NI 2014
Year: 2014

In wireless ad-hoc networks, forwarding data through intermediate relays extends the coverage area and enhances the network throughput. We consider a general wireless multiuser multihop transmission, where each data flow is subject to a constraint on the end-to-end buffering delay and the associated packet drop rate as a quality of service (QoS) requirement. The objective is to maximize the weighted sum-rate between source destination pairs, while the corresponding QoS requirements are satisfied. We introduce two new distributed cross-layer dynamic route selection schemes in this setting that are designed involving physical, MAC, and network layers. In the proposed opportunistic cross-layer dynamic route selection scheme, routes are assigned dynamically based on the state of network nodes' buffers and the instantaneous state of fading channels. In the same setting, the proposed time division cross layer dynamic route selection scheme utilizes the average quality of channels instead for more efficient implementation. Detailed results and comparisons are provided, which demonstrate the superior performance of the proposed cross-layer dynamic route selection schemes.
Preference Networks: Probabilistic Models for Recommendation Systems
Venue: arXiv cs.IR 2014
Year: 2014

Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Preference Network (PN) that jointly models various types of domain knowledge for the task of recommendation. The PN is a probabilistic model that systematically combines both content-based filtering and collaborative filtering into a single conditional Markov random field. Once estimated, it serves as a probabilistic database that supports various useful queries such as rating prediction and top-$N$ recommendation. To handle the challenging problem of learning large networks of users and items, we employ a simple but effective pseudo-likelihood with regularisation. Experiments on the movie rating data demonstrate the merits of the PN.
Multi-agents adaptive estimation and coverage control using Gaussian regression
Venue: arXiv cs.MA 2014
Year: 2014

We consider a scenario where the aim of a group of agents is to perform the optimal coverage of a region according to a sensory function. In particular, centroidal Voronoi partitions have to be computed. The difficulty of the task is that the sensory function is unknown and has to be reconstructed on line from noisy measurements. Hence, estimation and coverage needs to be performed at the same time. We cast the problem in a Bayesian regression framework, where the sensory function is seen as a Gaussian random field. Then, we design a set of control inputs which try to well balance coverage and estimation, also discussing convergence properties of the algorithm. Numerical experiments show the effectivness of the new approach.
Approximate Regularization Path for Nuclear Norm Based H2 Model Reduction
Venue: arXiv cs.SY 2014
Year: 2014

This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity. This results in a convex optimization problem where this trade-off is determined by one crucial design parameter. The main contribution is a methodology to approximately calculate all solutions up to a certain tolerance to the model reduction problem as a function of the design parameter. This is called the regularization path in sparse estimation and is a very important tool in order to find the appropriate balance between fit and complexity. We extend this to the more complicated nuclear norm case. The key idea is to determine when to exactly calculate the optimal solution using an upper bound based on the so-called duality gap. Hence, by solving a fixed number of optimization problems the whole regularization path up to a given tolerance can be efficiently computed. We illustrate this approach on some numerical examples.
Domain-partitioned element management systems employing mobile agents for distributed network management
Venue: arXiv cs.NI 2014
Year: 2014

Network management systems based on mobile agents are efficiently a better alternative than typical client/server based architectures. Centralized management models like SNMP or CMIP based management models suffer from scalability and flexibility issues which are addressed to great extent by flat bed or static mid-level manager models based on mobile agents, yet the use of mobile agents to distribute and delegate management tasks for above stated agent-based management frameworks like initial flat bed models and static mid-level managers cannot efficiently meet the demands of current networks which are growing in size and complexity. In view of the above mentioned limitations, we proposed a domain partitioned network management model based-on mobile agent & Element Management Systems in order to minimize management data flow to a centralized server. Intelligent agent allocated to specific EMS performs local network management and reports the results to the superior manager and finally the global manager performs global network management using those submitted management results. Experimental results of various scenarios of the proposed model have been presented to support the arguments given in favor of the prototype system based on mobile agents.
AbsSynthe: abstract synthesis from succinct safety specifications
Venue: arXiv cs.LO 2014
Year: 2014

In this paper, we describe a synthesis algorithm for safety specifications described as circuits. Our algorithm is based on fixpoint computations, abstraction and refinement, it uses binary decision diagrams as symbolic data structure. We evaluate our tool on the benchmarks provided by the organizers of the synthesis competition organized within the SYNT'14 workshop.
Block Bayesian Sparse Learning Algorithms With Application to Estimating Channels in OFDM Systems
Venue: arXiv cs.IT 2014
Year: 2014

Cluster-sparse channels often exist in frequencyselective fading broadband communication systems. The main reason is received scattered waveform exhibits cluster structure which is caused by a few reflectors near the receiver. Conventional sparse channel estimation methods have been proposed for general sparse channel model which without considering the potential cluster-sparse structure information. In this paper, we investigate the cluster-sparse channel estimation (CS-CE) problems in the state of the art orthogonal frequencydivision multiplexing (OFDM) systems. Novel Bayesian clustersparse channel estimation (BCS-CE) methods are proposed to exploit the cluster-sparse structure by using block sparse Bayesian learning (BSBL) algorithm. The proposed methods take advantage of the cluster correlation in training matrix so that they can improve estimation performance. In addition, different from our previous method using uniform block partition information, the proposed methods can work well when the prior block partition information of channels is unknown. Computer simulations show that the proposed method has a superior performance when compared with the previous methods.
On the String Consensus Problem and the Manhattan Sequence Consensus Problem
Venue: arXiv cs.DS 2014
Year: 2014

In the Manhattan Sequence Consensus problem (MSC problem) we are given $k$ integer sequences, each of length $l$, and we are to find an integer sequence $x$ of length $l$ (called a consensus sequence), such that the maximum Manhattan distance of $x$ from each of the input sequences is minimized. For binary sequences Manhattan distance coincides with Hamming distance, hence in this case the string consensus problem (also called string center problem or closest string problem) is a special case of MSC. Our main result is a practically efficient $O(l)$-time algorithm solving MSC for $k\le 5$ sequences. Practicality of our algorithms has been verified experimentally. It improves upon the quadratic algorithm by Amir et al.\ (SPIRE 2012) for string consensus problem for $k=5$ binary strings. Similarly as in Amir's algorithm we use a column-based framework. We replace the implied general integer linear programming by its easy special cases, due to combinatorial properties of the MSC for $k\le 5$. We also show that for a general parameter $k$ any instance can be reduced in linear time to a kernel of size $k!$, so the problem is fixed-parameter tractable. Nevertheless, for $k\ge 4$ this is still too large for any naive solution to be feasible in practice.
Simulation and optimization of container terminal operations: a case study
Venue: arXiv cs.OH 2014
Year: 2014

Container terminals are facing a set of interrelated problems. Container handling problems at container terminals are NP-hard problems. The docking time of container ships at the port must be optimized. In this paper we have built a simulation model that integrates all the activities of a container terminal. The proposed approach is applied on a real case study data of container terminal at El-Dekheilla port. The results show that the proposed approach reduced the ship turnaround time in port where 51% reduction in ship service time (loading/unloading) in port is achieved.
Wireless Sensor Networks Attacks and Solutions
Venue: arXiv cs.CR 2014
Year: 2014

A few years ago, wireless sensor networks (WSNs) used by only military. Now, we have seen many of organizations use WSNs for some purposes such as weather, pollution, traffic control, and healthcare. Security is becoming on these days a major concern for wireless sensor network. In this paper I focus on the security types of attacks and their detection. This paper anatomizes the security requirements and security attacks in wireless sensor networks. Also, indicate to the benchmarks for the security in WSNs.
Quadratically constrained quadratic programming for classification using particle swarms and applications
Venue: arXiv cs.AI 2014
Year: 2014

Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. The approach of particle swarms is an example for interior point methods in optimization as an iterative technique. This approach is novel and deals with classification problems without the use of a traditional classifier. Our method determines the optimal hyperplane or classification boundary for a data set. In a binary classification problem, we constrain each class as a cluster, which is enclosed by an ellipsoid. The estimation of the optimal hyperplane between the two clusters is posed as a quadratically constrained quadratic problem. The optimization problem is solved in distributed format using modified particle swarms. Our method has the advantage of using the direction towards optimal solution rather than searching the entire feasible region. Our results on the Iris, Pima, Wine, and Thyroid datasets show that the proposed method works better than a neural network and the performance is close to that of SVM.
Constrained Monotone Function Maximization and the Supermodular Degree
Venue: arXiv cs.DS 2014
Year: 2014

The problem of maximizing a constrained monotone set function has many practical applications and generalizes many combinatorial problems. Unfortunately, it is generally not possible to maximize a monotone set function up to an acceptable approximation ratio, even subject to simple constraints. One highly studied approach to cope with this hardness is to restrict the set function. An outstanding disadvantage of imposing such a restriction on the set function is that no result is implied for set functions deviating from the restriction, even slightly. A more flexible approach, studied by Feige and Izsak, is to design an approximation algorithm whose approximation ratio depends on the complexity of the instance, as measured by some complexity measure. Specifically, they introduced a complexity measure called supermodular degree, measuring deviation from submodularity, and designed an algorithm for the welfare maximization problem with an approximation ratio that depends on this measure. In this work, we give the first (to the best of our knowledge) algorithm for maximizing an arbitrary monotone set function, subject to a k-extendible system. This class of constraints captures, for example, the intersection of k-matroids (note that a single matroid constraint is sufficient to capture the welfare maximization problem). Our approximation ratio deteriorates gracefully with the complexity of the set function and k. Our work can be seen as generalizing both the classic result of Fisher, Nemhauser and Wolsey, for maximizing a submodular set function subject to a k-extendible system, and the result of Feige and Izsak for the welfare maximization problem. Moreover, when our algorithm is applied to each one of these simpler cases, it obtains the same approximation ratio as of the respective original work.
RTL2RTL Formal Equivalence: Boosting the Design Confidence
Venue: arXiv cs.SE 2014
Year: 2014

Increasing design complexity driven by feature and performance requirements and the Time to Market (TTM) constraints force a faster design and validation closure. This in turn enforces novel ways of identifying and debugging behavioral inconsistencies early in the design cycle. Addition of incremental features and timing fixes may alter the legacy design behavior and would inadvertently result in undesirable bugs. The most common method of verifying the correctness of the changed design is to run a dynamic regression test suite before and after the intended changes and compare the results, a method which is not exhaustive. Modern Formal Verification (FV) techniques involving new methods of proving Sequential Hardware Equivalence enabled a new set of solutions for the given problem, with complete coverage guarantee. Formal Equivalence can be applied for proving functional integrity after design changes resulting from a wide variety of reasons, ranging from simple pipeline optimizations to complex logic redistributions. We present here our experience of successfully applying the RTL to RTL (RTL2RTL) Formal Verification across a wide spectrum of problems on a Graphics design. The RTL2RTL FV enabled checking the design sanity in a very short time, thus enabling faster and safer design churn. The techniques presented in this paper are applicable to any complex hardware design.
Performance evaluation of wavelet scattering network in image texture classification in various color spaces
Venue: arXiv cs.CV 2014
Year: 2014

Texture plays an important role in many image analysis applications. In this paper, we give a performance evaluation of color texture classification by performing wavelet scattering network in various color spaces. Experimental results on the KTH_TIPS_COL database show that opponent RGB based wavelet scattering network outperforms other color spaces. Therefore, when dealing with the problem of color texture classification, opponent RGB based wavelet scattering network is recommended.
Providing better confidentiality and authentication on the Internet using Namecoin and MinimaLT
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, we introduce a duo of improvements for the Internet that would lead to better security. The authentication model on the Internet is broken and TLS connections have a considerable overhead. We try to address those issues with changes in both the application layer, discussing a replacement for the DNS system, and in the transport layer, a drop-in replacement for TCP built on top of UDP so that it can run on today's internet infrastructure.
An Asymptotic Bound on Estimation and Prediction of Mobile MIMO-OFDM Wireless Channels
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we derive an asymptotic closed--form expression for the error bound on extrapolation of doubly selective mobile MIMO wireless channels. The bound shows the relationship between the prediction error and system design parameters such as bandwidth, number of antenna elements, and number of frequency and temporal pilots, thereby providing useful insights into the effects of these parameters on prediction performance. Numerical simulations show that the asymptotic bound provides a good approximation to previously derived bounds while eliminating the need for repeated computation and dependence on channel parameters such as angles of arrival and departure, delays and Doppler shifts.
Security Penetration Test Framework for the Diameter Protocol
Venue: arXiv cs.CR 2014
Year: 2014

This paper outlines the infrastructure required for a penetration testing suite centered around the cellular call control protocol called Diameter. A brief description of Diameter is given along with the basic equipment and design requirements to conduct the testing.
Combining subspace codes with classical linear error-correcting codes
Venue: arXiv cs.IT 2014
Year: 2014

We discuss how subspace codes can be used to simultaneously correct errors and erasures when the network performs random linear network coding and the edges are noisy channels. This is done by combining the subspace code with a classical linear error-correcting code. The classical code then takes care of the errors and the subspace codes takes care of the erasures.
Parallelize Bubble Sort Algorithm Using OpenMP
Venue: arXiv cs.DC 2014
Year: 2014

Sorting has been a profound area for the algorithmic researchers and many resources are invested to suggest more works for sorting algorithms. For this purpose, many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. In this paper we implemented the bubble sort algorithm using multithreading (OpenMP). The proposed work tested on two standard datasets (text file) with different size . The main idea of the proposed algorithm is distributing the elements of the input datasets into many additional temporary sub-arrays according to a number of characters in each word. The sizes of each of these sub-arrays are decided depending on a number of elements with the same number of characters in the input array. We implemented OpenMP using Intel core i7-3610QM ,(8 CPUs),using two approaches (vectors of string and array 3D) . Finally, we get the data structure effects on the performance of the algorithm for that we choice the second approach.