Towards Optimal Approximate Streaming Pattern Matching by Matching Multiple Patterns in Multiple Streams

We consider computing a longest palindrome in the streaming model, where the symbols arrive one-by-one and we do not have random access to the input. While computing the answer exactly using sublinear space is not possible in such a setting, one can still hope for a good approximation guarantee. Our contribution is twofold. First, we provide lower bounds on the space requirements for randomized approximation algorithms processing inputs of length n. We rule out Las Vegas algorithms, as they cannot achieve sublinear space complexity. For Monte Carlo algorithms, we prove a lower bounds of Omega(M log min {|Sigma|, M}) bits of memory; here M=n/E for approximating the answer with additive error E, and M= log n / log (1 + epsilon) for approximating the answer with multiplicative error (1 + epsilon). Second, we design three real-time algorithms for this problem. Our Monte Carlo approximation algorithms for both additive and multiplicative versions of the problem use O(M) words of memory. Thus the obtained lower bounds are asymptotically tight up to a logarithmic factor. The third algorithm is deterministic and finds a longest palindrome exactly if it is short. This algorithm can be run in parallel with a Monte Carlo algorithm to obtain better results in practice. Overall, both the time and space complexity of finding a longest palindrome in a stream are essentially settled.

Estimating the size of the maximum matching is a canonical problem in graph analysis, and one that has attracted extensive study over a range of different computational models. We present improved streaming algorithms for approximating the size of maximum matching with sparse (bounded arboricity) graphs. 
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\n* (Insert-Only Streams) We present a one-pass algorithm that takes O(alpha log n) space and approximates the size of the maximum matching in graphs with arboricity alpha within a factor of O(alpha). This improves significantly upon the state-of-the-art tilde{O}(alpha n^{2/3})-space streaming algorithms, and is the first poly-logarithmic space algorithm for this problem. 
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\n* (Dynamic Streams) Given a dynamic graph stream (i.e., inserts and deletes) of edges of an underlying alpha-bounded arboricity graph, we present an one-pass algorithm that uses space tilde{O}(alpha^{10/3}n^{2/3}) and returns an O(alpha)-estimator for the size of the maximum matching on the condition that the number edge deletions in the stream is bounded by O(alpha n). For this class of inputs, our algorithm improves the state-of-the-art tilde{O}(\\alpha n^{4/5})-space algorithms, where the \\tilde{O}(.) notation hides logarithmic in n dependencies.
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\nIn contrast to prior work, our results take more advantage of the streaming access to the input and characterize the matching size based on the ordering of the edges in the stream in addition to the degree distributions and structural properties of the sparse graphs.

In this paper, we study streaming and online algorithms in the context of randomness in the input. For several problems, a random order of the input sequence - as opposed to the worst-case order - appears to be a necessary evil in order to prove satisfying guarantees. However, algorithmic techniques that work under this assumption tend to be vulnerable to even small changes in the distribution. For this reason, we propose a new adversarial injections model, in which the input is ordered randomly, but an adversary may inject misleading elements at arbitrary positions. We believe that studying algorithms under this much weaker assumption can lead to new insights and, in particular, more robust algorithms. We investigate two classical combinatorial-optimization problems in this model: Maximum matching and cardinality constrained monotone submodular function maximization. Our main technical contribution is a novel streaming algorithm for the latter that computes a 0.55-approximation. While the algorithm itself is clean and simple, an involved analysis shows that it emulates a subdivision of the input stream which can be used to greatly limit the power of the adversary.

We present the first streaming algorithm for the longest path problem in undirected graphs. The input graph is given as a stream of edges and RAM is limited to only a linear number of edges at a time (linear in the number of vertices n ). We prove a per-edge processing time of O ( n ) , where a naive solution would have required Ω( n 2 ) . Moreover, we give a concrete linear upper bound on the number of bits of RAM that are required. On a set of graphs with various structure, we experimentally compare our algorithm with three leading RAM algorithms: Warnsdorf (1823), Pohl-Warnsdorf (1967), and Pongracz (2012). Although conducting only a small constant number of passes over the input, our algorithm delivers competitive results: with the exception of preferential attachment graphs, we deliver at least 71% of the solution of the best RAM algorithm. The same minimum relative performance of 71% is observed over all graph classes after removing the 10% worst cases. This comparison has strong meaning, since for each instance class there is one algorithm that on average delivers at least 84% of a Hamilton path. In some cases we deliver even better results than any of the RAM algorithms.

Live Road Surveillance video streaming is a new type of safety data that can be transmitted in Vehicular Ad Hoc Networks (VANETs). Video streaming is a high-bandwidth-demanded service and causes congestion if there are many vehicles that stream video in the wireless network at the same time. Clustering is an efficient technique to relieve network congestion because it can reduce the number of vehicles to transmit data. Based on the effective-vision of the camera in vehicles, an Effective-Vision-Area-based Clustering (EVAC) algorithm for LRS service is proposed in this paper. EVAC can construct clusters such that a driver X can join a suitable cluster to get the LRS service from the appropriate vehicle ahead of X to have additional vision. In this paper, we compare the proposed EVAC algorithm for LRS service with other LRS service provider selection schemes that do not adopt the clustering mechanism in term of different performance metrics to show the effectiveness of the proposed work.

The H.264 video coding standard has been widely applied to multimedia data transmission. To prevent the contents of video from being recognized by someone unauthorized, providing sufficient security to H.264 has become more and more important. In this thesis, an improved H.264/AVC video encryption method is proposed. As compared with the Shahid’s method, more data are encrypted. And this makes the outcome more desirable. In the proposed scheme, selective encryption (SE) is performed in the motion vector difference (MVD), delta quantization parameter (delta QP), and the context-based adaptive variable length coding (CAVLC) modules. Owing that the encrypted stream should be decoded correctly before decryption, a format-compliant method is presented. Furthermore, in order to keep the bitrate unchanged, keeping the same stream length is another important characteristic of our proposed scheme. Since there is no escalation in bitrate, the proposed encryption algorithm is suitable for real-time multimedia streaming over heterogeneous networks. The encrypting key is generated by using the advanced encryption standard (AES) algorithm, and every frame has its own unique key. Four benchmark video sequences with different combinations of motion, texture and objects were used for experimental evaluation of the proposed scheme. The structural similarity (SSIM) index, a reference value to image quality, of the proposed scheme is 0.202 which is lower than 0.233 of the Shahid’s scheme. The key security is shown reliable under the test of only 1 bit change. The extra computation needed by our scheme is less than 0.5%, so it is feasible for real-time transmission. The above performance on security and sensitivity demonstrates that our proposed scheme is a good candidate for practical applications.

In this paper, we address the problem of QoS (Quality of Service) provisioning for multi-user video streaming over multiple heterogeneous wireless networks based on the distributed, cross-layer design framework. By jointly considering the rate allocation and the Joint Source-Channel Coding (JSCC), our proposal aims at maximizing the QoS provisioning under the given resource constraint. At first, we develop and evaluate a framework for optimal video rate allocation over multiple networks based on the observed Available Bit Rate (ABR) and the Round Trip Time (RTT) over each access network, as well as the video rate-distortion characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the sum of all video streams expected distortion. Then, we propose an analytical JSCC scheme for error-resilient scalable encoded video, and integrate the JSCC with the specific rate allocation algorithm to improve the constructed video quality by optimally applying the appropriate channel coding rate given the constraints imposed by the transmission rate and the prevailing channel conditions. Objective and subjective simulation results are provided which demonstrate the effectiveness of our proposed joint scheme.

Life finds a way: the recovery of frog populations from a chytridiomycosis outbreak

Peer-2-Peer (P2P) technologies are rapidly advancing and have already proven successful in the form of various bandwidth demanding applications such as file-transfer and video streaming. The major advantage of the P2P technologies is its built-in scalability feature, relative to traditional client- server architecture. Therefore, keeping in view the large number of users involved in live video-streaming, P2P technology can be considered as a valuable tool aiming to broadcast live-video contents to a large number of users over the internet. Although, there is lot of research going on in analyzing the performance of P2P video streaming in heterogeneous networking scenarios, but to the best of our knowledge no work is done in analyzing the impact of Forward Error Correction (FEC) on the performance of P2P live-video streaming. In this paper we analyze the performance and characteristics of P2P live-video streaming on end-user quality of experience while employing FEC on the coded video packets. Additionally, in-order to carry out the performance analysis with reference to non-error protected benchmarker system; we utilized a realistic realtime French distributed Grid facility known as Grid'5000. More specifically, the performance of the system is evaluated while considering various realistic P2P video streaming constraints, such as limited playout delay, limited upload bandwidth and video rates. We concluded that the FEC will result in noticeable performance improvement in a scenario where each peer is connected with limited number of other peers that have the required contents for download. In such a situation the portion of the stream corrupted due to channel errors may not be recovered with the help of other accessible peers. However, if each peer has more connected peers with various alternative options to download the corrupted contents, then the employment of FEC may not have very noticeable advantage on the performance of the P2P video streaming system.

Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The correct extraction of the component for posterior analysis is a critical step of the AOI process. Nowadays, the Pattern Matching Algorithm is commonly used, although this algorithm requires extensive calculations and is time consuming. This paper will present an improved algorithm for the component localization process, with the capability of implementation in a parallel execution system.

Behavioral analysis of real-time systems with interdependent tasks

Nowadays signature attacks are termed as very big problem because it leads to software vulnerability.Malware writers confuse their malicious code to malicious code detectors such as Signature-based detection. However,it fails to detect new malware. This research article addresses the signature based intrusion detection from IntrusionDetection (IDS) systems. The proposed hybrid techniques for Generation of Signature are done using GeneticAlgorithm (GA) and Simulated Annealing (SA) approaches. For this, signature-set in execution statements are selected by using simulated annealing and genetic algorithm, which produce the optimal solution of selection. Then thegenerated signatures are matched with IDS by using the two pattern matching techniques, namely (i). Finite stateautomaton based search for Single Pattern matching technique and (ii) Rabin Karp string search algorithm for multiple pattern matching technique. These techniques are used to match the signature as in an effective manner. In addition tothis the Fuzzy Logic classification is used to find the degrees of truth of vulnerability for classification. The aim ofthe proposed work is to improve the final resultant accuracy in compared to existing techniques. The proposed RabinKarp- fuzzy logic system returns the higher performance metrics namely precision is 88% and Recall is 80% and inopen source dataset it contains 30 vulnerabilities this proposed worked well in detecting 28 vulnerabilities/ defect, theaccuracy of this proposed is 94.27%.

Data stream mining has attracted much research attention from the data mining community. With the advance of wireless networks and mobile devices, the concept of ubiquitous data mining has been proposed. However, mobile devices are resource-constrained, which makes data stream mining a greater challenge. In this paper, we propose the RA-HCluster algorithm that can be used in mobile devices for clustering stream data. It adapts algorithm settings and compresses stream data based on currently available resources, so that mobile devices can continue with clustering at acceptable accuracy even under low memory resources. Experimental results show that not only is RA-HCluster more accurate than RA-VFKM, it is able to maintain a low and stable memory usage.

Our work is motivated by the large number of data stream sources that define mesoscale meteorology where asynchronous streams are commonplace. Techniques for performing filtering, aggregation, and transformation on multiple streams must be effective for the case of asynchronous streams. Rate Sizing algorithm (RS-Algo) links the number of events waiting to participate in a join to the rate of the streams responsible for their delivery. In this poster, we show the results of performance evaluation of the RS-Algo. The gains in memory utilization are largest under asynchronous streams.

Research on artificial neural networks (ANNs) has been carried out for more than five decades. A renewed interest appeared in the 80's with the finding of powerful models like J. Hopfield's recurrent networks, T. Kohonen's self-organizing feature maps, and the back-propagation rule. At that time, there was no platform that was at the same time versatile enough for any ANN model to be implemented and fast enough to solve large problems. Super-computers were the sole exception to this rule, but were prohibitively expensive for most applications. However, both research scientists and application engineers clearly identified the need for such a computing power. This triggered many projects in the field. In parallel, research on multi-processor systems started during the 60's. Systolic arrays have been proposed in 1979 as a means to fully exploit the possibilities of VLSI. Two previous theses, by F. Blayo and C. Lehmann, have studied the use of bi-dimensional systolic arrays for neural computation. At first, the presented system, called GENES, has been designed for the Hopfield model. Extensions to other ANNs have also been proposed. The goal of the present thesis is to study, design, build, and analyze an efficient accelerator for neural computation. In a first step, the GENES architecture has been extended towards generality and efficiency. This includes a thorough analysis of ANN models, of other neural computers, and of previous GENES implementations. The result of this work is the GENES IV integrated circuit, whose architecture has been co-designed by P. Ienne and the author. The main part of this thesis discusses the architecture, the design and the analysis of the MANTRA I machine, a neural computer based on a GENES IV array with up to 40 × 40 processing elements (PEs). The delta rule (and hence the Perceptron and Adaline rules), the back-propagation rule, the Hopfield model, and the Kohonen model can be implemented on this system. Although not a generic system, such a machine may be regarded as a multi-model neural computer. A prototype has been running for a year and is used daily by software designers. Several novel features distinguish the MANTRA I machine from other neural systems. First, it belongs to the few existing neural computers, contrary to the majority of implementations, which are specific to an application or to an algorithm. The machine does not hard-wire any algorithm, but provides the necessary primitives to implement the target models. This is a key feature for research, since several algorithms or versions of an algorithm can be tested on a problem. It is an important aspect for applications as well, because different ANN models are often cascaded to solve a problem. The GENES IV array — that is, the computing core of the MANTRA I machine — features synapse-level parallelism (i.e., one real or virtual PE is allocated per synapse or neural connection), while most other systems exploit only neuron-level parallelism (i.e., one PE per neuron). Hence, this system aims at a much finer parallelism grain and is well suited for massively parallel architectures. The problem size that can be computed by a neural accelerator should not be limited by the hardware (except for memory size). Therefore, it is essential to support time-sharing of PEs. On the MANTRA I machine, this is achieved by the concept of virtual arrays. Matrices are divided into sub-matrices that can be mapped onto the physical array, which is then time-shared among them. An efficient mechanism has been implemented to swap sub-matrices in background, while some other computation is performed. Since systolic arrays are pipelined systems, it is important to avoid emptying and re-filling them too often, in order to keep the hardware utilization rate high. Therefore, a systolic instruction flow has been implemented, so that each instruction follows the data for which it has been issued. Like any SIMD system, the MANTRA I machine is composed of a parallel or SIMD module and a control module. The SIMD module consists of the GENES IV array and a set of dedicated units designed for the computation that scales with the number of neurons and would poorly fit on a bi-dimensional array. A complex system of FIFOs and memories sustains the required input/output streams for the systolic array. The control module is a complete SISD system. Its tasks are (1) to control the SIMD module by dispatching instructions, (2) to manage data input and output, (3) to communicate with the external world, and (4) to perform data pre- and post-processing. The SIMD instructions are of the very long instruction word (VLIW) type. Synchronization between the two modules is achieved by an instruction FIFO. The performance of the MANTRA I machine has been analyzed using the delta rule. Measurements show that the sustained performance is very close to its peak value, as long as the problem fits in the memory banks connected to the GENES IV array. Experiments have also been run to investigate the impact of the constraints imposed by the hardware on the convergence of algorithms. Finally, the use of systolic arrays as neural accelerators is discussed in the light of the experience acquired with the GENES IV array and the MANTRA I machine. The weaknesses of the machine are analyzed, and several solutions are proposed to avoid them in a future design. A general discussion of the future of neural computers concludes this thesis.