Performance Study of Localization Techniques in Wireless Body Area Sensor Networks

Abstract: One of the major issues in Wireless Body Area Sensor Networks (WBASNs) is efficient localization. There are various techniques for indoor and outdoor environments to locate a person. This study evaluating and compares performance of optimization schemes in indoor environments for optimal placement of wireless sensors, where patients can perform their daily activities. In indoor environments, the performance comparison between Distance Vector-Hop algorithm, Ring Overlapping Based on Comparison Received Signal Strength Indicator (ROCRSSI), Particle filtering and Kalman filtering based location tracking techniques, in terms of localization accuracy is estimated. Results show that particle filtering outperforms all. GPS and several techniques based on GSMlocation tracking schemes are proposed for outdoor environments. Hidden Markov GSM based location tracking scheme efficiently performs among all, in terms of location accuracy and computational overheads.


A Generic Library for Stencil Computations

Abstract: In this era of diverse and heterogeneous computer architectures, the programmability issues, such as productivity and portable efficiency, are crucial to software development and algorithm design. One way to approach the problem is to step away from traditional sequential programming languages and move toward domain specific programming environments to balance between expressivity and efficiency. In order to demonstrate this principle, we developed a domain specific C++ generic library for stencil computations, like PDE solvers. The library features high level constructs to specify computation and allows the development of parallel stencil computations with very limited effort. The high abstraction constructs (like do_all and do_reduce) make the program shorter and cleaner with increased contextual information for better performance exploitation. The results show good performance from Windows multicores, to HPC clusters and machines with accelerators, like GPUs.


Object Recognition with Multi-Scale Pyramidal Pooling Networks

Abstract: We present a Multi-Scale Pyramidal Pooling Network, featuring a novel pyramidal pooling layer at multiple scales and a novel encoding layer. Thanks to the former the network does not require all images of a given classification task to be of equal size. The encoding layer improves generalisation performance in comparison to similar neural network architectures, especially when training data is scarce. We evaluate and compare our system to convolutional neural networks and state-of-the-art computer vision methods on various benchmark datasets. We also present results on industrial steel defect classification, where existing architectures are not applicable because of the constraint on equally sized input images. The proposed architecture can be seen as a fully supervised hierarchical bag-of-features extension that is trained online and can be fine-tuned for any given task.


Analysis and Modeling Experiment Performance Parameters of Routing Protocols in MANETs and VANETs

Abstract: In this paper, a framework for experimental parameters in which Packet Delivery Ratio (PDR), effect of link duration over End-to-End Delay (E2ED) and Normalized Routing Overhead (NRO) in terms of control packets is analyzed and modeled for Mobile Ad-Hoc NETworks (MANETs) and Vehicular Ad-Hoc NETworks (VANETs) with the assumption that nodes (vehicles) are sparsely moving in two different road. Moreover, this paper contributes the performance comparison of one Proactive Routing Protocol; Destination Sequenced Distance vector (DSDV) and two reactive protocols; DYnamic Source Routing (DSR) and DYnamic MANET On-Demand (DYMO). A novel contribution of this work is enhancements in default versions of selected routing protocols. Three performance parameters; PDR, E2ED and NRO with varying scalabilities are measured to analyze the performance of selected routing protocols with their original and enhanced versions. From extensive simulations, it is observed that DSR outperforms among all three protocols at the cost of delay. NS-2 simulator is used for simulation with TwoRayGround propagation model to evaluate analytical results.


A hybrid Hermitian general eigenvalue solver

Abstract: The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium sized Hermitian generalized eigenvalue problems must be solved many times. The small size of the problems limits the scalability on a distributed memory system, hence they can benefit from the massive computational performance concentrated on a single node, hybrid GPU-CPU system. However, new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multi/many-core CPUs as well are required. Addressing these demands, we implemented a novel Hermitian general eigensolver algorithm. This algorithm is based on a standard eigenvalue solver, and existing algorithms can be used. The resulting eigensolvers are state-of-the-art in HPC, significantly outperforming existing libraries. We analyze their performance impact on applications of interest, when different fractions of eigenvectors are needed by the host electronic structure code.


DSDV, DYMO, OLSR: Link Duration and Path Stability

Abstract: In this paper, we evaluate and compare the impact of link duration and path stability of routing protocols; Destination Sequence Distance vector (DSDV), Dynamic MANET On- Demand (DYMO) and Optimized Link State Routing (OLSR) at different number of connections and node density. In order to improve the efficiency of selected protocols; we enhance DYMO and OLSR. Simulation and comparison of both default and enhanced routing protocols is carried out under the performance parameters; Packet Delivery Ratio (PDR), Average End-to End Delay (AE2ED) and Normalized Routing Overhead (NRO). From the results, we observe that DYMO performs better than DSDV, MOD-OLSR and OLSR in terms of PDR, AE2ED, link duration and path stability at the cost of high value of NRO.


Improved Bounds for Online Preemptive Matching

Abstract: When designing a preemptive online algorithm for the maximum matching problem, we wish to maintain a valid matching M while edges of the underlying graph are presented one after the other. When presented with an edge e, the algorithm should decide whether to augment the matching M by adding e (in which case e may be removed later on) or to keep M in its current form without adding e (in which case e is lost for good). The objective is to eventually hold a matching M with maximum weight. The main contribution of this paper is to establish new lower and upper bounds on the competitive ratio achievable by preemptive online algorithms: 1. We provide a lower bound of 1+ln 2~1.693 on the competitive ratio of any randomized algorithm for the maximum cardinality matching problem, thus improving on the currently best known bound of e/(e-1)~1.581 due to Karp, Vazirani, and Vazirani [STOC'90]. 2. We devise a randomized algorithm that achieves an expected competitive ratio of 5.356 for maximum weight matching. This finding demonstrates the power of randomization in this context, showing how to beat the tight bound of 3 +2\sqrt{2}~5.828 for deterministic algorithms, obtained by combining the 5.828 upper bound of McGregor [APPROX'05] and the recent 5.828 lower bound of Varadaraja [ICALP'11].


The SeqBin Constraint Revisited

Abstract: We revisit the SeqBin constraint. This meta-constraint subsumes a number of important global constraints like Change, Smooth and IncreasingNValue. We show that the previously proposed filtering algorithm for SeqBin has two drawbacks even under strong restrictions: it does not detect bounds disentailment and it is not idempotent. We identify the cause for these problems, and propose a new propagator that overcomes both issues. Our algorithm is based on a connection to the problem of finding a path of a given cost in a restricted $n$-partite graph. Our propagator enforces domain consistency in O(nd^2) and, for special cases of SeqBin that include Change, Smooth and IncreasingNValue, in O(nd) time.


Introspective Pushdown Analysis of Higher-Order Programs

Abstract: In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection skirt just inside the boundaries of soundness and decidability. Alone, each method reduces analysis times and boosts precision by orders of magnitude. This work illuminates and conquers the theoretical challenges that stand in the way of combining the power of these techniques. The challenge in marrying these techniques is not subtle: computing the reachable control states of a pushdown system relies on limiting access during transition to the top of the stack; abstract garbage collection, on the other hand, needs full access to the entire stack to compute a root set, just as concrete collection does. \emph{Introspective} pushdown systems resolve this conflict. Introspective pushdown systems provide enough access to the stack to allow abstract garbage collection, but they remain restricted enough to compute control-state reachability, thereby enabling the sound and precise product of pushdown analysis and abstract garbage collection. Experiments reveal synergistic interplay between the techniques, and the fusion demonstrates "better-than-both-worlds" precision.


Footprint Tracker: reviewing lifelogs and reconstructing daily experiences

Abstract: With the increasing emphasis on how mobile technologies are experienced in everyday life, researchers are increasingly emphasizing the use of in-situ methods such as Experience Sampling and Day Reconstruction. In our line of research we explore the concept of Technology-Assisted Reconstruction, in which passively logged behavior data assist in the later reconstruction of daily experiences. In this paper we introduce Footprint tracker, a web application that supports participants in reviewing lifelogs and reconstructing their daily experiences. We focus on three kinds of data: visual (as captured through Microsoft's sensecam), location, and context (i.e., SMS and calls received and made). We describe how Footprint Tracker supports the user in reviewing these lifelogs and outline a field study that attempts to inquire into whether and how this data support reconstruction from memory.


Sense me: Supporting awareness in parent-child relationships through mobile sensing

Abstract: We introduce Sense{\mu} (pronounced "sense me"), a mobile application that aims at supporting awareness in parent- child relationships through the sensing capabilities of mobile devices. We discuss the relevance of three types of awareness information: physical activity inferred from accelerometers, verbal activity during class hours inferred from microphones, and social activity inferred from Bluetooth pair-wise proximity sensing. We describe how we attempt to contextualize these sensing data with the goal of supporting parents' awareness of the educational performance and social wellbeing of their children, as well as motivating and sustaining a two-way communication between parents and teachers over the long term.


Beyond Experience Sampling: Evaluating Personal Informatics with Technology-Assisted Reconstruction

Abstract: Experience Sampling has been considered the golden standard of in-situ measurement, yet, at the expense of high burden to participants. In this paper we propose Technology-Assisted Reconstruction (TAR), a methodological approach that combines passive logging of users' behaviors with use of these data in assisting the reconstruction of behaviors and experiences. Through a number of recent and ongoing projects we will discuss how TAR may be employed for the evaluation of personal informatics systems, but also, conversely, how ideas from the field of personal informatics may contribute towards the development of new methodologies for in-situ evaluation.


Optimal Euclidean spanners: really short, thin and lanky

Abstract: In a seminal STOC'95 paper, titled "Euclidean spanners: short, thin and lanky", Arya et al. devised a construction of Euclidean $(1+\eps)$-spanners that achieves constant degree, diameter $O(\log n)$, and weight $O(\log^2 n) \cdot \omega(MST)$, and has running time $O(n \cdot \log n)$. This construction applies to $n$-point constant-dimensional Euclidean spaces. Moreover, Arya et al. conjectured that the weight bound can be improved by a logarithmic factor, without increasing the degree and the diameter of the spanner, and within the same running time. This conjecture of Arya et al. became a central open problem in the area of Euclidean spanners. In this paper we resolve the long-standing conjecture of Arya et al. in the affirmative. Specifically, we present a construction of spanners with the same stretch, degree, diameter, and running time, as in Arya et al.'s result, but with optimal weight $O(\log n) \cdot \omega(MST)$. Moreover, our result is more general in three ways. First, we demonstrate that the conjecture holds true not only in constant-dimensional Euclidean spaces, but also in doubling metrics. Second, we provide a general tradeoff between the three involved parameters, which is tight in the entire range. Third, we devise a transformation that decreases the lightness of spanners in general metrics, while keeping all their other parameters in check. Our main result is obtained as a corollary of this transformation.


Minimal Proof Search for Modal Logic K Model Checking

Abstract: Most modal logics such as S5, LTL, or ATL are extensions of Modal Logic K. While the model checking problems for LTL and to a lesser extent ATL have been very active research areas for the past decades, the model checking problem for the more basic Multi-agent Modal Logic K (MMLK) has important applications as a formal framework for perfect information multi-player games on its own. We present Minimal Proof Search (MPS), an effort number based algorithm solving the model checking problem for MMLK. We prove two important properties for MPS beyond its correctness. The (dis)proof exhibited by MPS is of minimal cost for a general definition of cost, and MPS is an optimal algorithm for finding (dis)proofs of minimal cost. Optimality means that any comparable algorithm either needs to explore a bigger or equal state space than MPS, or is not guaranteed to find a (dis)proof of minimal cost on every input. As such, our work relates to A* and AO* in heuristic search, to Proof Number Search and DFPN+ in two-player games, and to counterexample minimization in software model checking.


Towards Tight Bounds for Local Broadcasting

Abstract: We consider the local broadcasting problem in the SINR model, which is a basic primitive for gathering initial information among $n$ wireless nodes. Assuming that nodes can measure received power, we achieve an essentially optimal constant approximate algorithm (with a $\log^2 n$ additive term). This improves upon the previous best $O(\log n)$-approximate algorithm. Without power measurement, our algorithm achieves $O(\log n)$-approximation, matching the previous best result, but with a simpler approach that works under harsher conditions, such as arbitrary node failures. We give complementary lower bounds under reasonable assumptions.


Proceedings 2nd Workshop on Formal Methods in the Development of Software

Abstract: This volume contains the proceedings of the 2nd WorkShop on Formal Methods in the Development of Software (WS-FMDS 2012). The workshop was held in Paris, France on August 30th, 2012 as a satellite event to the 18th International Symposium on Formal Methods (FM-2012). The aim of WS-FMDS 2012 is to provide a forum for researchers who are interested in the application of formal methods on systems which are being developing with a software methodology. In particular, this workshop is intended to bring together scientists and practitioners who are active in the area of formal methods and interested in exchanging their experiences in the industrial usage of these methods. This workshop also strives to promote research and development for the improvement of formal methods and tools for industrial applications.


Differential Spectrum of Some Power Functions With Low Differential Uniformity

Abstract: In this paper, for an odd prime $p$, the differential spectrum of the power function $x^{\frac{p^k+1}{2}}$ in $\mathbb{F}_{p^n}$ is calculated. For an odd prime $p$ such that $p\equiv 3\bmod 4$ and odd $n$ with $k|n$, the differential spectrum of the power function $x^{\frac{p^n+1}{p^k+1}+\frac{p^n-1}{2}}$ in $\mathbb{F}_{p^n}$ is also derived. From their differential spectrums, the differential uniformities of these two power functions are determined. We also find some new power functions having low differential uniformity.


Finding Structure in Text, Genome and Other Symbolic Sequences

Abstract: The statistical methods derived and described in this thesis provide new ways to elucidate the structural properties of text and other symbolic sequences. Generically, these methods allow detection of a difference in the frequency of a single feature, the detection of a difference between the frequencies of an ensemble of features and the attribution of the source of a text. These three abstract tasks suffice to solve problems in a wide variety of settings. Furthermore, the techniques described in this thesis can be extended to provide a wide range of additional tests beyond the ones described here. A variety of applications for these methods are examined in detail. These applications are drawn from the area of text analysis and genetic sequence analysis. The textually oriented tasks include finding interesting collocations and cooccurent phrases, language identification, and information retrieval. The biologically oriented tasks include species identification and the discovery of previously unreported long range structure in genes. In the applications reported here where direct comparison is possible, the performance of these new methods substantially exceeds the state of the art. Overall, the methods described here provide new and effective ways to analyse text and other symbolic sequences. Their particular strength is that they deal well with situations where relatively little data are available. Since these methods are abstract in nature, they can be applied in novel situations with relative ease.


Optimal Deterministic Routing and Sorting on the Congested Clique

Abstract: Consider a clique of n nodes, where in each synchronous round each pair of nodes can exchange O(log n) bits. We provide deterministic constant-time solutions for two problems in this model. The first is a routing problem where each node is source and destination of n messages of size O(log n). The second is a sorting problem where each node i is given n keys of size O(log n) and needs to receive the ith batch of n keys according to the global order of the keys. The latter result also implies deterministic constant-round solutions for related problems such as selection or determining modes.


Reconstruction Conjecture for Graphs Isomorphic to Cube of a Tree

Abstract: This paper proves the reconstruction conjecture for graphs which are isomorphic to the cube of a tree. The proof uses the reconstructibility of trees from their peripheral vertex deleted subgraphs. The main result follows from (i) characterization of the cube of a tree (ii) recognizability of the cube of a tree (iii) uniqueness of tree as a cube root of a graph G, except when G is a complete graph (iv) reconstructibility of trees from their peripheral vertex deleted subgraphs.


Embedding of Virtual Network Requests over Static Wireless Multihop Networks

Abstract: Network virtualization is a technology of running multiple heterogeneous network architecture on a shared substrate network. One of the crucial components in network virtualization is virtual network embedding, which provides a way to allocate physical network resources (CPU and link bandwidth) to virtual network requests. Despite significant research efforts on virtual network embedding in wired and cellular networks, little attention has been paid to that in wireless multi-hop networks, which is becoming more important due to its rapid growth and the need to share these networks among different business sectors and users. In this paper, we first study the root causes of new challenges of virtual network embedding in wireless multi-hop networks, and propose a new embedding algorithm that efficiently uses the resources of the physical substrate network. We examine our algorithm's performance through extensive simulations under various scenarios. Due to lack of competitive algorithms, we compare the proposed algorithm to five other algorithms, mainly borrowed from wired embedding or artificially made by us, partially with or without the key algorithmic ideas to assess their impacts.


Tight Cell-Probe Bounds for Online Hamming Distance Computation

Abstract: We show tight bounds for online Hamming distance computation in the cell-probe model with word size w. The task is to output the Hamming distance between a fixed string of length n and the last n symbols of a stream. We give a lower bound of Omega((d/w)*log n) time on average per output, where d is the number of bits needed to represent an input symbol. We argue that this bound is tight within the model. The lower bound holds under randomisation and amortisation.


Spreadsheets and Long Term Corporate Survival

Abstract: We have conducted an empirical investigation into the long term survival rates of some small but representative samples of the 30,000 largest UK limited companies. These companies were either a control or known to have used, or been interested in the use of, spreadsheets, spreadsheet based monte carlo simulation software, other spreadsheet and decision analysis software and/or related management training. We show that there is a material and statistically significant increase in the long term survival rate of all of these groups of companies compared to the control.


Spatial And Spectral Quality Evaluation Based On Edges Regions Of Satellite Image Fusion

Abstract: The Quality of image fusion is an essential determinant of the value of processing images fusion for many applications. Spatial and spectral qualities are the two important indexes that used to evaluate the quality of any fused image. However, the jury is still out of fused image's benefits if it compared with its original images. In addition, there is a lack of measures for assessing the objective quality of the spatial resolution for the fusion methods. Therefore, an objective quality of the spatial resolution assessment for fusion images is required. Most important details of the image are in edges regions, but most standards of image estimation do not depend upon specifying the edges in the image and measuring their edges. However, they depend upon the general estimation or estimating the uniform region, so this study deals with new method proposed to estimate the spatial resolution by Contrast Statistical Analysis (CSA) depending upon calculating the contrast of the edge, non edge regions and the rate for the edges regions. Specifying the edges in the image is made by using Soble operator with different threshold values. In addition, estimating the color distortion added by image fusion based on Histogram Analysis of the edge brightness values of all RGB-color bands and Lcomponent.


A Hybrid Forecast of Exchange Rate based on ARFIMA,Discrete Grey-Markov, and Fractal Kalman Model

Abstract: We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combined with degree of grey incidence are better than grey Markov and ARFIMA model or Kalman methods.


Universal Witnesses for State Complexity of Boolean Operations and Concatenation Combined with Star

Abstract: We study the state complexity of boolean operations and product (concatenation, catenation) combined with star. We derive tight upper bounds for the symmetric differences and differences of two languages, one or both of which are starred, and for the product of two starred languages. We prove that the previously discovered bounds for the union and the intersection of languages with one or two starred arguments, for the product of two languages one of which is starred, and for the star of the product of two languages can all be met by the recently introduced universal witnesses and their variants.


Hycon2 Benchmark: Power Network System

Abstract: As a benchmark exercise for testing software and methods developed in Hycon2 for decentralized and distributed control, we address the problem of designing the Automatic Generation Control (AGC) layer in power network systems. In particular, we present three different scenarios and discuss performance levels that can be reached using Centralized Model Predictive Control (MPC). These results can be used as a milestone for comparing the performance of alternative control schemes. Matlab software for simulating the scenarios is also provided in an accompanying file.


dup -- Explicit un-sharing in Haskell

Abstract: We propose two operations to prevent sharing in Haskell that do not require modifying the data generating code, demonstrate their use and usefulness, and compare them to other approaches to preventing sharing. Our claims are supported by a formal semantics and a prototype implementation.


Seamless Infrastructure independent Multi Homed NEMO Handoff Using Effective and Timely IEEE 802.21 MIH triggers

Abstract: Handoff performance of NEMO BS protocol with existent improvement proposals is still not sufficient for real time and QoS-sensitive applications and further optimizations are needed. When dealing with single homed NEMO, handoff latency and packet loss become irreducible all optimizations included, so that it is impossible to meet requirements of the above applications. Then, How to combine the different Fast handoff approaches remains an open research issue and needs more investigation. In this paper, we propose a new Infrastructure independent handoff approach combining multihoming and intelligent Make-Before-Break Handoff. Based on required Handoff time estimation, L2 and L3 handoffs are initiated using effective and timely MIH triggers, reducing so the anticipation time and increasing the probability of prediction. We extend MIH services to provide tunnel establishment and switching before link break. Thus, the handoff is performed in background with no latency and no packet loss while pingpong scenario is almost avoided. In addition, our proposal saves cost and power consumption by optimizing the time of simultaneous use of multiple interfaces. We provide also NS2 simulation experiments identifying suitable parameter values used for estimation and validating the proposed model


Design of PIC12F675 Microcontroller Based Data Acquisition System for Slowly Varying Signals

Abstract: The present paper describes the design of a cost effective, better resolution data acquisition system (DAS) which is compatible to most of the PC and laptops. A low cost DAS has been designed using PIC12F675 having 4-channel analog input with 10-bit resolution for the monitoring of slowly varying signals. The DAS so designed is interfaced to the serial port of the PC. Firmware is written in Basic using Oshonsoft PIC IDE and burn to the microcontroller by using PICkit2 programmer. An application program is also developed using Visual Basic 6 which allows to display the waveform of the signal(s) and simultaneously the data also can be saved into the hard disk of the computer for future use and analysis.


Rule Based Expert System for Diagnosis of Neuromuscular Disorders

Abstract: In this paper, we discuss the implementation of a rule based expert system for diagnosing neuromuscular diseases. The proposed system is implemented as a rule based expert system in JESS for the diagnosis of Cerebral Palsy, Multiple Sclerosis, Muscular Dystrophy and Parkinson's disease. In the system, the user is presented with a list of questionnaires about the symptoms of the patients based on which the disease of the patient is diagnosed and possible treatment is suggested. The system can aid and support the patients suffering from neuromuscular diseases to get an idea of their disease and possible treatment for the disease.


High-throughput Genome-wide Association Analysis for Single and Multiple Phenotypes

Abstract: The variance component tests used in genomewide association studies of thousands of individuals become computationally exhaustive when multiple traits are analysed in the context of omics studies. We introduce two high-throughput algorithms -- CLAK-CHOL and CLAK-EIG -- for single and multiple phenotype genome-wide association studies (GWAS). The algorithms, generated with the help of an expert system, reduce the computational complexity to the point that thousands of traits can be analyzed for association with millions of polymorphisms in a course of days on a standard workstation. By taking advantage of problem specific knowledge, CLAK-CHOL and CLAK-EIG significantly outperform the current state-of-the-art tools in both single and multiple trait analysis.


Relationship between circuit complexity and symmetry

Abstract: It is already shown that a Boolean function for a NP-complete problem can be computed by a polynomial-sized circuit if its variables have enough number of automorphisms. Looking at this previous study from the different perspective gives us the idea that the small number of automorphisms might be a barrier for a polynomial time solution for NP-complete problems. Here I show that by interpreting a Boolean circuit as a graph, the small number of graph automorphisms and the large number of subgraph automorphisms in the circuit establishes the exponential circuit lower bound for NP-complete problems. As this strategy violates the largeness condition in Natural proof, this result shows that P!=NP without any contradictions to the existence of pseudorandom functions.


Reordering Rows for Better Compression: Beyond the Lexicographic Order

Abstract: Sorting database tables before compressing them improves the compression rate. Can we do better than the lexicographical order? For minimizing the number of runs in a run-length encoding compression scheme, the best approaches to row-ordering are derived from traveling salesman heuristics, although there is a significant trade-off between running time and compression. A new heuristic, Multiple Lists, which is a variant on Nearest Neighbor that trades off compression for a major running-time speedup, is a good option for very large tables. However, for some compression schemes, it is more important to generate long runs rather than few runs. For this case, another novel heuristic, Vortex, is promising. We find that we can improve run-length encoding up to a factor of 3 whereas we can improve prefix coding by up to 80%: these gains are on top of the gains due to lexicographically sorting the table. We prove that the new row reordering is optimal (within 10%) at minimizing the runs of identical values within columns, in a few cases.


Effective Enabling of Sharing and Reuse of Knowledge On Semantic Web by Ontology in Date Fruit Model

Abstract: Since Organizations have recognized that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantages, knowledge sharing has a vital role in present society. It is an activity through which information is exchanged among people through different media. Many problems face the area of knowledge sharing and knowledge reuse. Currently, knowledge sharing between entities is achieved in a very ad-hoc fashion, lacking proper understanding of the meaning of the data. Ontologies can potentially solve these problems by facilitating knowledge sharing and reuse through formal and real-world semantics. Ontologies, through formal semantics, are machine-understandable. A computer can process data, annotated with references to ontologies, and through the knowledge encapsulated in the ontology, deduce facts from the original data. The date fruit is the most enduring symbol of the Sultanate's rich heritage. Creating ontology for dates will enrich the farming group and research scholars in the agro farm area.


Using Constraints for Equivalent Mutant Detection

Abstract: In mutation testing the question whether a mutant is equivalent to its program is important in order to compute the correct mutation score. Unfortunately, answering this question is not always possible and can hardly be obtained just by having a look at the program's structure. In this paper we introduce a method for solving the equivalent mutant problem using a constraint representation of the program and its mutant. In particularly the approach is based on distinguishing test cases, i.e., test inputs that force the program and its mutant to behave in a different way. Beside the foundations of the approach, in this paper we also present the algorithms and first empirical results.


NAAS: Negotiation Automation Architecture with Buyer's Behavior Pattern Prediction Component

Abstract: In this era of "Services" everywhere, with the explosive growth of E-Commerce and B2B transactions, there is a pressing need for the development of intelligent negotiation systems which consists of feasible architecture, a reliable framework and flexible multi agent based protocols developed in specialized negotiation languages with complete semantics and support for message passing between the buyers and sellers. This is possible using web services on the internet. The key issue is negotiation and its automation. In this paper we review the classical negotiation methods and some of the existing architectures and frameworks. We are proposing here a new combinatory framework and architecture, NAAS. The key feature in this framework is a component for prediction or probabilistic behavior pattern recognition of a buyer, along with the other classical approaches of negotiation frameworks and architectures. Negotiation is practically very complex activity to automate without human intervention so in the future we also intend to develop a new protocol which will facilitate automation of all the types of negotiation strategies like bargaining, bidding, auctions, under our NAAS framework.


Verified System Development with the AutoFocus Tool Chain

Abstract: This work presents a model-based development methodology for verified software systems as well as a tool support for it: an applied AutoFocus tool chain and its basic principles emphasizing the verification of the system under development as well as the check mechanisms we used to raise the level of confidence in the correctness of the implementation of the automatic generators.


Predictive Software Measures based on Z Specifications - A Case Study

Abstract: Estimating the effort and quality of a system is a critical step at the beginning of every software project. It is necessary to have reliable ways of calculating these measures, and, it is even better when the calculation can be done as early as possible in the development life-cycle. Having this in mind, metrics for formal specifications are examined with a view to correlations to complexity and quality-based code measures. A case study, based on a Z specification and its implementation in ADA, analyzes the practicability of these metrics as predictors.


Ubiquitous HealthCare in Wireless Body Area Networks

Abstract: Recent advances in wireless communications, system on chip and low power sensor nodes allow realization of Wireless Body Area Networks (WBANs).WBANs comprise of tiny sensors, which collect information of a patient's vital signs and provide a real time feedback. In addition,WBANs also support many applications including ubiquitous healthcare, entertainment, gaming, military, etc. Ubiquitous healthcare is required by elderly people to facilitate them with instant monitoring anywhere they move around. In this paper, we provide a survey on different architectures used in WBANs for ubiquitous healthcare monitoring. Different standards and devices used in these architectures are also discussed in this paper. Finally, path loss in WBANs and its impact on communication is presented with the help of simulations performed for different models of In-Body communication and different factors (such as, attenuation, frequency, distance etc) influencing path loss in On-Body communications.


A Hybrid Forecast of Exchange Rate based on Discrete Grey-Markov and Grey Neural Network Model

Abstract: We propose a hybrid forecast model based on discrete grey-fuzzy Markov and grey neural network model and show that our hybrid model can improve much more the performance of forecast than traditional grey-Markov model and neural network models. Our simulation results are shown that our hybrid forecast method with the combinational weight based on optimal grey relation degree method is better than the hybrid model with combinational weight based minimization of error-squared criterion.


Challenges for Distributional Compositional Semantics

Abstract: This paper summarises the current state-of-the art in the study of compositionality in distributional semantics, and major challenges for this area. We single out generalised quantifiers and intensional semantics as areas on which to focus attention for the development of the theory. Once suitable theories have been developed, algorithms will be needed to apply the theory to tasks. Evaluation is a major problem; we single out application to recognising textual entailment and machine translation for this purpose.


Improvement of ISOM by using filter

Abstract: Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. It is important to maintain the image quality even if it is compressed to certain extent. Depend upon this the image compression is classified into two categories : lossy and lossless image compression. There are many lossy digital image compression techniques exists. Among this Incremental Self Organizing Map is a familiar one. The good pictures quality can be retrieved if image denoising technique is used for compression and also provides better compression ratio. Image denoising is an important pre-processing step for many image analysis and computer vision system. It refers to the task of recovering a good estimate of the true image from a degraded observation without altering and changing useful structure in the image such as discontinuities and edges. Many approaches have been proposed to remove the noise effectively while preserving the original image details and features as much as possible. This paper proposes a technique for image compression using Incremental Self Organizing Map (ISOM) with Discret Wavelet Transform (DWT) by applying filtering techniques which play a crucial role in enhancing the quality of a reconstructed image. The experimental result shows that the proposed technique obtained better compression ratio value.


Understanding the Learners' Actions when using Mathematics Learning Tools

Abstract: The use of computer-based mathematics tools is widespread in learning. Depending on the way that these tools assess the learner's solution paths, one can distinguish between automatic assessment tools and semi-automatic assessment tools. Automatic assessment tools directly provide all feedback necessary to the learners, while semi-automatic assessment tools involve the teachers as part the assessment process. They are provided with as much information as possible on the learners' interactions with the tool. How can the teachers know how the learning tools were used and which intermediate steps led to a solution? How can the teachers respond to a learner's question that arises while using a computer tool? Little is available to answer this beyond interacting directly with the computer and performing a few manipulations to understand the tools' state. This paper presents SMALA, a web-based logging architecture that addresses these problems by recording, analyzing and representing user actions. While respecting the learner's privacy, the SMALA architecture supports the teachers by offering fine-grained representations of the learners' activities as well as overviews of the progress of a classroom.


On Formal Specification of Maple Programs

Abstract: This paper is an example-based demonstration of our initial results on the formal specification of programs written in the computer algebra language MiniMaple (a substantial subset of Maple with slight extensions). The main goal of this work is to define a verification framework for MiniMaple. Formal specification of MiniMaple programs is rather complex task as it supports non-standard types of objects, e.g. symbols and unevaluated expressions, and additional functions and predicates, e.g. runtime type tests etc. We have used the specification language to specify various computer algebra concepts respective objects of the Maple package DifferenceDifferential developed at our institute.


Towards the Formal Specification and Verification of Maple Programs

Abstract: In this paper, we present our ongoing work and initial results on the formal specification and verification of MiniMaple (a substantial subset of Maple with slight extensions) programs. The main goal of our work is to find behavioral errors in such programs w.r.t. their specifications by static analysis. This task is more complex for widely used computer algebra languages like Maple as these are fundamentally different from classical languages: they support non-standard types of objects such as symbols, unevaluated expressions and polynomials and require abstract computer algebraic concepts and objects such as rings and orderings etc. As a starting point we have defined and formalized a syntax, semantics, type system and specification language for MiniMaple.


Stackelberg Shortest Path Tree Game, Revisited

Abstract: Let $G(V,E)$ be a directed graph with $n$ vertices and $m$ edges. The edges $E$ of $G$ are divided into two types: $E_F$ and $E_P$. Each edge of $E_F$ has a fixed price. The edges of $E_P$ are the priceable edges and their price is not fixed a priori. Let $r$ be a vertex of $G$. For an assignment of prices to the edges of $E_P$, the revenue is given by the following procedure: select a shortest path tree $T$ from $r$ with respect to the prices (a tree of cheapest paths); the revenue is the sum, over all priceable edges $e$, of the product of the price of $e$ and the number of vertices below $e$ in $T$. Assuming that $k=|E_P|\ge 2$ is a constant, we provide a data structure whose construction takes $O(m+n\log^{k-1} n)$ time and with the property that, when we assign prices to the edges of $E_P$, the revenue can be computed in $(\log^{k-1} n)$. Using our data structure, we save almost a linear factor when computing the optimal strategy in the Stackelberg shortest paths tree game of [D. Bil{\`o} and L. Gual{\`a} and G. Proietti and P. Widmayer. Computational aspects of a 2-Player Stackelberg shortest paths tree game. Proc. WINE 2008].


I/O-Efficient Dynamic Planar Range Skyline Queries

Abstract: We present the first fully dynamic worst case I/O-efficient data structures that support planar orthogonal \textit{3-sided range skyline reporting queries} in $\bigO (\log_{2B^\epsilon} n + \frac{t}{B^{1-\epsilon}})$ I/Os and updates in $\bigO (\log_{2B^\epsilon} n)$ I/Os, using $\bigO (\frac{n}{B^{1-\epsilon}})$ blocks of space, for $n$ input planar points, $t$ reported points, and parameter $0 \leq \epsilon \leq 1$. We obtain the result by extending Sundar's priority queues with attrition to support the operations \textsc{DeleteMin} and \textsc{CatenateAndAttrite} in $\bigO (1)$ worst case I/Os, and in $\bigO(1/B)$ amortized I/Os given that a constant number of blocks is already loaded in main memory. Finally, we show that any pointer-based static data structure that supports \textit{dominated maxima reporting queries}, namely the difficult special case of 4-sided skyline queries, in $\bigO(\log^{\bigO(1)}n +t)$ worst case time must occupy $\Omega(n \frac{\log n}{\log \log n})$ space, by adapting a similar lower bounding argument for planar 4-sided range reporting queries.


Cups Products in Z2-Cohomology of 3D Polyhedral Complexes

Abstract: Let $I=(\mathbb{Z}^3,26,6,B)$ be a 3D digital image, let $Q(I)$ be the associated cubical complex and let $\partial Q(I)$ be the subcomplex of $Q(I)$ whose maximal cells are the quadrangles of $Q(I)$ shared by a voxel of $B$ in the foreground -- the object under study -- and by a voxel of $\mathbb{Z}^3\smallsetminus B$ in the background -- the ambient space. We show how to simplify the combinatorial structure of $\partial Q(I)$ and obtain a 3D polyhedral complex $P(I)$ homeomorphic to $\partial Q(I)$ but with fewer cells. We introduce an algorithm that computes cup products on $H^*(P(I);\mathbb{Z}_2)$ directly from the combinatorics. The computational method introduced here can be effectively applied to any polyhedral complex embedded in $\mathbb{R}^3$.


Dichotomy for Holant* Problems with a Function on Domain Size 3

Abstract: Holant problems are a general framework to study the algorithmic complexity of counting problems. Both counting constraint satisfaction problems and graph homomorphisms are special cases. All previous results of Holant problems are over the Boolean domain. In this paper, we give the first dichotomy theorem for Holant problems for domain size $>2$. We discover unexpected tractable families of counting problems, by giving new polynomial time algorithms. This paper also initiates holographic reductions in domains of size $>2$. This is our main algorithmic technique, and is used for both tractable families and hardness reductions. The dichotomy theorem is the following: For any complex-valued symmetric function ${\bf F}$ with arity 3 on domain size 3, we give an explicit criterion on ${\bf F}$, such that if ${\bf F}$ satisfies the criterion then the problem ${\rm Holant}^*({\bf F})$ is computable in polynomial time, otherwise ${\rm Holant}^*({\bf F})$ is #P-hard.


Arabic CALL system based on pedagogically indexed text

Abstract: This article introduces the benefits of using computer as a tool for foreign language teaching and learning. It describes the effect of using Natural Language Processing (NLP) tools for learning Arabic. The technique explored in this particular case is the employment of pedagogically indexed corpora. This text-based method provides the teacher the advantage of building activities based on texts adapted to a particular pedagogical situation. This paper also presents ARAC: a Platform dedicated to language educators allowing them to create activities within their own pedagogical area of interest.


Bichromatic compatible matchings

Abstract: For a set $R$ of $n$ red points and a set $B$ of $n$ blue points, a $BR$-matching is a non-crossing geometric perfect matching where each segment has one endpoint in $B$ and one in $R$. Two $BR$-matchings are compatible if their union is also non-crossing. We prove that, for any two distinct $BR$-matchings $M$ and $M'$, there exists a sequence of $BR$-matchings $M = M_1, ..., M_k = M'$ such that $M_{i-1} $ is compatible with $M_i$. This implies the connectivity of the compatible bichromatic matching graph containing one node for each bichromatic matching and an edge joining each pair of compatible matchings, thereby answering the open problem posed by Aichholzer et al. in "Compatible matchings for bichromatic plane straight-line graphs"


Optimal Multi-Robot Path Planning with LTL Constraints: Guaranteeing Correctness Through Synchronization

Abstract: In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification. Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment. The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment. Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team. The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times. Our method relies on the communication capabilities of the robots to guarantee correctness and maintain performance during deployment. After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment. We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.


A Multi-Agents Architecture to Learn Vision Operators and their Parameters

Abstract: In a vision system, every task needs that the operators to apply should be {\guillemotleft} well chosen {\guillemotright} and their parameters should be also {\guillemotleft} well adjusted {\guillemotright}. The diversity of operators and the multitude of their parameters constitute a big challenge for users. As it is very difficult to make the {\guillemotleft} right {\guillemotright} choice, lack of a specific rule, many disadvantages appear and affect the computation time and especially the quality of results. In this paper we present a multi-agent architecture to learn the best operators to apply and their best parameters for a class of images. Our architecture consists of three types of agents: User Agent, Operator Agent and Parameter Agent. The User Agent determines the phases of treatment, a library of operators and the possible values of their parameters. The Operator Agent constructs all possible combinations of operators and the Parameter Agent, the core of the architecture, adjusts the parameters of each combination by treating a large number of images. Through the reinforcement learning mechanism, our architecture does not consider only the system opportunities but also the user preferences.


Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au diagnostic de tumeurs c\'er\'ebrales

Abstract: This article describes different models based on Bayesian networks RB modeling expertise in the diagnosis of brain tumors. Indeed, they are well adapted to the representation of the uncertainty in the process of diagnosis of these tumors. In our work, we first tested several structures derived from the Bayesian network reasoning performed by doctors on the one hand and structures generated automatically on the other. This step aims to find the best structure that increases diagnostic accuracy. The machine learning algorithms relate MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian network from a database incomplete, we have proposed an extension of the EM algorithm by adding a priori knowledge in the form of the thresholds calculated by the first phase of the algorithm RBE . The very encouraging results obtained are discussed at the end of the paper


Reasoning with Data-Centric Business Processes

Abstract: We describe an approach to modelling and reasoning about data-centric business processes and present a form of general model checking. Our technique extends existing approaches, which explore systems only from concrete initial states. Specifically, we model business processes in terms of smaller fragments, whose possible interactions are constrained by first-order logic formulae. In turn, process fragments are connected graphs annotated with instructions to modify data. Correctness properties concerning the evolution of data with respect to processes can be stated in a first-order branching-time logic over built-in theories, such as linear integer arithmetic, records and arrays. Solving general model checking problems over this logic is considerably harder than model checking when a concrete initial state is given. To this end, we present a tableau procedure that reduces these model checking problems to first-order logic over arithmetic. The resulting proof obligations are passed on to appropriate "off-the-shelf" theorem provers. We also detail our modelling approach, describe the reasoning components and report on first experiments.


Scalable Minimization Algorithm for Partial Bisimulation

Abstract: We present an efficient algorithm for computing the partial bisimulation preorder and equivalence for labeled transitions systems. The partial bisimulation preorder lies between simulation and bisimulation, as only a part of the set of actions is bisimulated, whereas the rest of the actions are simulated. Computing quotients for simulation equivalence is more expensive than for bisimulation equivalence, as for simulation one has to account for the so-called little brothers, which represent classes of states that can simulate other classes. It is known that in the absence of little brother states, (partial bi)simulation and bisimulation coincide, but still the complexity of existing minimization algorithms for simulation and bisimulation does not scale. Therefore, we developed a minimization algorithm and an accompanying tool that scales with respect to the bisimulated action subset.


Bisimilarity on Basic Process Algebra is in 2-ExpTime (an explicit proof)

Abstract: Burkart, Caucal, Steffen (1995) showed a procedure deciding bisimulation equivalence of processes in Basic Process Algebra (BPA), i.e. of sequential processes generated by context-free grammars. They improved the previous decidability result of Christensen, H\"uttel, Stirling (1992), since their procedure has obviously an elementary time complexity and the authors claim that a close analysis would reveal a double exponential upper bound. Here a self-contained direct proof of the membership in 2-ExpTime is given. This is done via a Prover-Refuter game which shows that there is an alternating Turing machine deciding the problem in exponential space. The proof uses similar ingredients (size-measures, decompositions, bases) as the previous proofs, but one new simplifying factor is an explicit addition of infinite regular strings to the state space. An auxiliary claim also shows an explicit exponential upper bound on the equivalence level of nonbisimilar normed BPA processes. The importance of clarifying the 2-ExpTime upper bound for BPA bisimilarity has recently increased due to the shift of the known lower bound from PSpace (Srba, 2002) to ExpTime (Kiefer, 2012).


Kernelized Supervised Dictionary Learning

Abstract: In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and their corresponding labels is maximized. To maximize this dependency, the recently introduced Hilbert Schmidt independence criterion (HSIC) is used. One of the main advantages of this novel approach for SDL is that it can be easily kernelized by incorporating a kernel, particularly a data-derived kernel such as normalized compression distance, into the formulation. The learned dictionary is compact and the proposed approach is fast. We show that it outperforms other unsupervised and supervised dictionary learning approaches in the literature, using real-world data.


An XML-Format for Conjectures in Geometry (Work-in-Progress)

Abstract: With a large number of software tools dedicated to the visualisation and/or demonstration of properties of geometric constructions and also with the emerging of repositories of geometric constructions, there is a strong need of linking them, and making them and their corpora, widely usable. A common setting for interoperable interactive geometry was already proposed, the i2g format, but, in this format, the conjectures and proofs counterparts are missing. A common format capable of linking all the tools in the field of geometry is missing. In this paper an extension of the i2g format is proposed, this extension is capable of describing not only the geometric constructions but also the geometric conjectures. The integration of this format into the Web-based GeoThms, TGTP and Web Geometry Laboratory systems is also discussed.


Resource Allocation: Realizing Mean-Variability-Fairness Tradeoffs

Abstract: Network Utility Maximization (NUM) provides a key conceptual framework to study reward allocation amongst a collection of users/entities across disciplines as diverse as economics, law and engineering. In network engineering, this framework has been particularly insightful towards understanding how Internet protocols allocate bandwidth, and motivated diverse research efforts on distributed mechanisms to maximize network utility while incorporating new relevant constraints, on energy, power, storage, stability, etc., e.g., for systems ranging from communication networks to the smart-grid. However when the available resources and/or users' utilities vary over time, reward allocations will tend to vary, which in turn may have a detrimental impact on the users' overall satisfaction or quality of experience. This paper introduces a generalization of NUM framework which explicitly incorporates the detrimental impact of temporal variability in a user's allocated rewards. It explicitly incorporates tradeoffs amongst the mean and variability in users' reward allocations, as well as fairness. We propose a simple online algorithm to realize these tradeoffs, which, under stationary ergodic assumptions, is shown to be asymptotically optimal, i.e., achieves a long term performance equal to that of an offline algorithm with knowledge of the future variability in the system. This substantially extends work on NUM to an interesting class of relevant problems where users/entities are sensitive to temporal variability in their service or allocated rewards.


Quantified Differential Temporal Dynamic Logic for Verifying Properties of Distributed Hybrid Systems

Abstract: We combine quantified differential dynamic logic (QdL) for reasoning about the possible behavior of distributed hybrid systems with temporal logic for reasoning about the temporal behavior during their operation. Our logic supports verification of temporal and non-temporal properties of distributed hybrid systems and provides a uniform treatment of discrete transitions, continuous evolution, and dynamic dimensionality-changes. For our combined logic, we generalize the semantics of dynamic modalities to refer to hybrid traces instead of final states. Further, we prove that this gives a conservative extension of QdL for distributed hybrid systems. On this basis, we provide a modular verification calculus that reduces correctness of temporal behavior of distributed hybrid systems to non-temporal reasoning, and prove that we obtain a complete axiomatization relative to the non-temporal base logic QdL. Using this calculus, we analyze temporal safety properties in a distributed air traffic control system where aircraft can appear dynamically.


LPC(ID): A Sequent Calculus Proof System for Propositional Logic Extended with Inductive Definitions

Abstract: The logic FO(ID) uses ideas from the field of logic programming to extend first order logic with non-monotone inductive definitions. Such logic formally extends logic programming, abductive logic programming and datalog, and thus formalizes the view on these formalisms as logics of (generalized) inductive definitions. The goal of this paper is to study a deductive inference method for PC(ID), which is the propositional fragment of FO(ID). We introduce a formal proof system based on the sequent calculus (Gentzen-style deductive system) for this logic. As PC(ID) is an integration of classical propositional logic and propositional inductive definitions, our sequent calculus proof system integrates inference rules for propositional calculus and definitions. We present the soundness and completeness of this proof system with respect to a slightly restricted fragment of PC(ID). We also provide some complexity results for PC(ID). By developing the proof system for PC(ID), it helps us to enhance the understanding of proof-theoretic foundations of FO(ID), and therefore to investigate useful proof systems for FO(ID).


Face Recognition Algorithms based on Transformed Shape Features

Abstract: Human face recognition is, indeed, a challenging task, especially under the illumination and pose variations. We examine in the present paper effectiveness of two simple algorithms using coiflet packet and Radon transforms to recognize human faces from some databases of still gray level images, under the environment of illumination and pose variations. Both the algorithms convert 2-D gray level training face images into their respective depth maps or physical shape which are subsequently transformed by Coiflet packet and Radon transforms to compute energy for feature extraction. Experiments show that such transformed shape features are robust to illumination and pose variations. With the features extracted, training classes are optimally separated through linear discriminant analysis (LDA), while classification for test face images is made through a k-NN classifier, based on L1 norm and Mahalanobis distance measures. Proposed algorithms are then tested on face images that differ in illumination,expression or pose separately, obtained from three databases,namely, ORL, Yale and Essex-Grimace databases. Results, so obtained, are compared with two different existing algorithms.Performance using Daubechies wavelets is also examined. It is seen that the proposed Coiflet packet and Radon transform based algorithms have significant performance, especially under different illumination conditions and pose variation. Comparison shows the proposed algorithms are superior.


A New Weighted Spearman's Footrule as A Measure of Distance between Rankings

Abstract: Many applications motivate the distance measure between rankings, such as comparing top-k lists and rank aggregation for voting, and intrigue great interest to researchers. For example, for a search engine, the use of different ranking algorithms may return different ranking lists. The effect of a ranking algorithm can be estimated by computing the distance (similarity) between the result ranking it returns and the appropriate ranking people expect. People may be interested in only the first few items of result ranking, therefore the metric for measuring the distance should emphasize on the items in higher positions. Besides, in an extreme case, if a result ranking is the total reverse of the expected ranking, then it is considered to be the worst ranking with the maximum distance. Therefore, a metric is called for, which can satisfy both of the two intuitions. To address this problem, we present a weighted metric based on the classical Spearman's footrule metric to measure the distance between two permutations of n objects. This metric can be applied in rank aggregation problem with a polynomial time algorithm, and produces a 2-approximation for adopting the weighted Kendall's tau distance proposed by Farnoud et al.


Variable and Thread Bounding for Systematic Testing of Multithreaded Programs

Abstract: Previous approaches to systematic state-space exploration for testing multi-threaded programs have proposed context-bounding and depth-bounding to be effective ranking algorithms for testing multithreaded programs. This paper proposes two new metrics to rank thread schedules for systematic state-space exploration. Our metrics are based on characterization of a concurrency bug using v (the minimum number of distinct variables that need to be involved for the bug to manifest) and t (the minimum number of distinct threads among which scheduling constraints are required to manifest the bug). Our algorithm is based on the hypothesis that in practice, most concurrency bugs have low v (typically 1- 2) and low t (typically 2-4) characteristics. We iteratively explore the search space of schedules in increasing orders of v and t. We show qualitatively and empirically that our algorithm finds common bugs in fewer number of execution runs, compared with previous approaches. We also show that using v and t improves the lower bounds on the probability of finding bugs through randomized algorithms. Systematic exploration of schedules requires instrumenting each variable access made by a program, which can be very expensive and severely limits the applicability of this approach. Previous work [5, 19] has avoided this problem by interposing only on synchronization operations (and ignoring other variable accesses). We demonstrate that by using variable bounding (v) and a static imprecise alias analysis, we can interpose on all variable accesses (and not just synchronization operations) at 10-100x less overhead than previous approaches.


The Cerny conjecture for automata respecting intervals of a directed graph

Abstract: The \v{C}ern\'y's conjecture states that for every synchronizing automaton with n states there exists a reset word of length not exceeding (n-11)^2. We prove this conjecture for a class of automata preserving certain properties of intervals of a directed graph. Our result unifies and generalizes some earlier results obtained by other authors.


Quantifying Spatiotemporal Stability by means of Entropy: Approach and Motivations

Abstract: Several studies demonstrate that there are critical differences between real wireless networks and simulation models. This finding has permitted to extract spatial and temporal properties for links and to provide efficient methods as biased link sampling to guarantee efficient routing structure. Other works have focused on computing metrics to improve routing, specially the reuse of the measure of entropy. From there, rises the idea of formulating a new measure of entropy that gives an overview of the spatiotemporal stability of a link. This measure will rely on spatial and temporal properties of links and fed with the efficiency of biased link sampling.


Energy Efficient MAC Protocols

Abstract: This paper presents a survey of energy efficiency of Medium Access Control (MAC) protocols for Wireless Body Area Sensor Networks (WBASNs). We highlight the features of MAC protocols along with their advantages and limitations in context of WBASNs. Comparison of Low Power Listening (LPL), Scheduled Contention and Time Division Multiple Access (TDMA) is also elaborated. MAC protocols with respect to different approaches and techniques which are used for energy minimization, traffic control mechanisms for collision avoidance are discussed.We also present a survey of path loss models for In-body, On-body and Off-body communications in WBASNs and analytically discuss that path loss is maximum in In-body communication because of low energy levels to take care of tissues and organs located inside the body. Survey of Power model for WBANs of CSMA/CA and beacon mode is also presented.


Noise Filtering, Channel Modeling and Energy Utilization in Wireless Body Area Networks

Abstract: Constant monitoring of patients without disturbing their daily activities can be achieved through mobile networks. Sensor nodes distributed in a home environment to provide home assistance gives concept of Wireless Wearable Body Area Networks. Gathering useful information and its transmission to the required destination may face several problems. In this paper we figure out different issues and discuss their possible solutions in order to obtain an optimized infrastructure for the care of elderly people. Different channel models along with their characteristics, noise filtering in different equalization techniques, energy consumption and effect of different impairments have been discussed in our paper. The novelty of this work is that we highlighted multiple issues along with their possible solutions that a BAN infrastructure is still facing.


Network Lifetime Analysis of AODV, DSR and ZRP at Different Network Parameters

Abstract: Enhancement of network lifetime is a key design criterion for most of the energy constrained networks as nodes are battery operated. In multi-hop wireless network, proper utilization of battery power is very much necessary to maintain network connectivity. If the battery power of a node drains quickly then its connectivity in its neighborhood will be lost. So the study of network lifetime is very much crucial as compared to other network parameters. Considering this importance we made an attempt to study the behaviour of three most common routing protocols in ad hoc network. Extensive simulations are done on AODV, DSR and ZRP to determine the network lifetime at different node mobility and at different network load. Simulation results suggest that AODV is the most energy efficient protocol as compared to other


Novel Grey Interval Weight Determining and Hybrid Grey Interval Relation Method in Multiple Attribute Decision-Making

Abstract: This paper proposes a grey interval relation TOPSIS for the decision making in which all of the attribute weights and attribute values are given by the interval grey numbers. The feature of our method different from other grey relation decision-making is that all of the subjective and objective weights are obtained by interval grey number and that decisionmaking is performed based on the relative approach degree of grey TOPSIS, the relative approach degree of grey incidence and the relative membership degree of grey incidence using 2-dimensional Euclidean distance. The weighted Borda method is used for combining the results of three methods. An example shows the applicability of the proposed approach.


Full Data Controlled Web-Based Feed Aggregator

Abstract: Feed syndication is analogous to electronic newsletters, both are aimed at delivering feeds to subscribers; the difference is that while newsletter subscription requires e-mail and exposed you to spam and security challenges, feed syndication ensures that you only get what you requested for. This paper reports a review on the state of the art of feed aggregation technology and the development of a locally hosted web based feed aggregator as a research tool using the core features of WordPress; the software was further enhanced with plugins and widgets for dynamic content publishing, database and object caching, social web syndication, back-up and maintenance, among others. The results highlight the current developments in software re-use and describes; how open source content management systems can be used for both online and offline publishing, a means whereby feed aggregator users can control and share feed data, as well as how web developers can focus on extending the features of built-in software libraries in applications rather than reinventing the wheel.


Automated Training and Maintenance through Kinect

Abstract: In this paper, we have worked on reducing burden on mechanic involving complex automobile maintenance activities that are performed in centralised workshops. We have presented a system prototype that combines Augmented Reality with Kinect. With the use of Kinect, very high quality sensors are available at considerably low costs, thus reducing overall expenditure for system design. The system can be operated either in Speech mode or in Gesture mode. The system can be controlled by various audio commands if user opts for Speech mode. The same controlling can also be done by using a set of Gestures in Gesture mode. Gesture recognition is the task performed by Kinect system. This system, bundled with RGB and Depth camera, processes the skeletal data by keeping track of 20 different body joints. Recognizing Gestures is done by verifying user movements and checking them against predefined condition. Augmented Reality module captures real-time image data streams from high resolution camera. This module then generates 3D model that is superimposed on real time data.


Hitting Sets Online and Unique-Max Coloring

Abstract: We consider the problem of hitting sets online. The hypergraph (i.e., range-space consisting of points and ranges) is known in advance, and the ranges to be stabbed are input one-by-one in an online fashion. The online algorithm must stab each range upon arrival. An online algorithm may add points to the hitting set but may not remove already chosen points. The goal is to use the smallest number of points. The best known competitive ratio for hitting sets online by Alon et al. \cite{alon2009online} is $O(\log n \cdot \log m)$ for general hypergraphs, where $n$ and $m$ denote the number of points and the number of ranges, respectively. We consider hypergraphs in which the union of two intersecting ranges is also a range. Our main result for such hypergraphs is as follows. The competitive ratio of the online hitting set problem is at most the unique-max number and at least this number minus one.


A Novel Approach Coloured Object Tracker with Adaptive Model and Bandwidth using Mean Shift Algorithm

Abstract: The traditional color-based mean-shift tracking algorithm is popular among tracking methods due to its simple and efficient procedure, however, the lack of dynamism in its target model makes it unsuitable for tracking objects which have changes in their sizes and shapes. In this paper, we propose a fast novel threephase colored object tracker algorithm based on mean shift idea while utilizing adaptive model. The proposed method can improve the mentioned weaknesses of the original mean-shift algorithm. The experimental results show that the new method is feasible, robust and has acceptable speed in comparison with other algorithms.15 page,


DQSB: A Reliable Broadcast Protocol Based on Distributed Quasi-Synchronized Mechanism for Low Duty-Cycled Wireless Sensor Networks

Abstract: In duty-cycled wireless sensor networks, deployed sensor nodes are usually put to sleep for energy efficiency according to sleep scheduling approaches. Any sleep scheduling scheme with its supporting protocols ensures that data can always be routed from source to sink. In this paper, we investigate a problem of multi-hop broadcast and routing in random sleep scheduling scheme, and propose a novel protocol, called DQSB, by quasi-synchronization mechanism to achieve reliable broadcast and less latency routing. DQSB neither assumes time synchronization which requires all neighboring nodes wake up at the same time, nor assumes duty-cycled awareness which makes it difficult to use in asynchronous WSNs. Furthermore, the benefit of quasi-synchronized mechanism for broadcast from sink to other nodes is the less latency routing paths for reverse data collection to sink because of no or less sleep waiting time. Simulation results show that DQSB outperforms the existing protocols in broadcast times performance and keeps relative tolerant broadcast latency performance, even in the case of unreliable links. The proposed DQSB protocol, in this paper, can be recognized as a tradeoff between broadcast times and broadcast latency. We also explore the impact of parameters in the assumption and the approach to get proper values for supporting DQSB.


Ontology for Mobile Phone Operating Systems

Abstract: This ongoing study deals with an important part of a line of research that constitutes a challenging burden. It is an initial investigation into the development of a Holistic Framework for Cellular Communication (HFCC). The main purpose is to establish mechanisms by which existing wireless cellular communication components and models can work holistically together. It demonstrates that establishing a mathematical framework that allows existing cellular communication technologies (and tools supporting those technologies) to seamlessly interact is technically feasible. The longer-term future goals are to actually improve the interoperability, the efficiency of mobile communication, calls quality, and reliability by applying the framework to specific development efforts.


Load Balancing with Reduced Unnecessary Handoff in Energy Efficient Macro/Femto-cell based BWA Networks

Abstract: The hierarchical macro/femto cell based BWA networks are observed to be quite promising for mobile operators as it improves their network coverage and capacity at the outskirt of the macro cell. However, this new technology introduces increased number of macro/femto handoff and wastage of electrical energy which in turn may affect the system performance. Users moving with high velocity or undergoing real-time transmission suffers degraded performance due to huge number of unnecessary macro/femto handoff. On the other hand, huge amount of electrical energy is wasted when a femto BS is active in the network but remains unutilized due to low network load. Our proposed energy efficient handoff decision algorithm eliminates the unnecessary handoff while balancing the load of the macro and femto cells at minimal energy consumption. The performance of the proposed algorithm is analyzed using Continuous Time Markov Chain (CTMC) Model. In addition, we have also contributed a method to determine the balanced threshold level of the received signal strength (RSS) from macro base station (BS). The balanced threshold level provides equal load distribution of the mobile users to the macro and femto BSs. The balanced threshold level is evaluated based on the distant location of the femto cells for small scaled networks. Numerical analysis shows that threshold level above the balanced threshold results in higher load distribution of the mobile users to the femto BSs.


Survey of Extended LEACH-Based Clustering Routing Protocols for Wireless Sensor Networks

Abstract: An energy efficient routing protocol is the major concern in Wireless Sensor Networks (WSNs). In this survey paper, we present energy efficient hierarchical routing protocols, developed from conventional LEACH routing protocol. Main focus of our study is how these extended protocols work in order to increase the life time and how quality routing protocol are improved for WSNs. Furthermore, this paper also highlights some of the issues faced by LEACH and also explains how these issues are tackled by extended versions of LEACH. We compare the features and performance issues of the selected hierarchal routing protocols.


Broccoli: Semantic Full-Text Search at your Fingertips

Abstract: We present Broccoli, a fast and easy-to-use search engine for what we call semantic full-text search. Semantic full-text search combines the capabilities of standard full-text search and ontology search. The search operates on four kinds of objects: ordinary words (e.g., edible), classes (e.g., plants), instances (e.g., Broccoli), and relations (e.g., occurs-with or native-to). Queries are trees, where nodes are arbitrary bags of these objects, and arcs are relations. The user interface guides the user in incrementally constructing such trees by instant (search-as-you-type) suggestions of words, classes, instances, or relations that lead to good hits. Both standard full-text search and pure ontology search are included as special cases. In this paper, we describe the query language of Broccoli, the main idea behind a new kind of index that enables fast processing of queries from that language as well as fast query suggestion, the natural language processing required, and the user interface. We evaluated query times and result quality on the full version of the English Wikipedia (40 GB XML dump) combined with the YAGO ontology (26 million facts). We have implemented a fully functional prototype based on our ideas and provide a web application to reproduce our quality experiments. Both are accessible via http://broccoli.informatik.uni-freiburg.de/repro-corr/ .


A Review of Techniques to Mitigate Sybil Attacks

Abstract: Any decentralised distributed network is particularly vulnerable to the Sybil attack wherein a malicious node masquerades as several different nodes, called Sybil nodes, simultaneously in an attempt to disrupt the proper functioning of the network. Such attacks may cause damage on a fairly large scale especially since they are difficult to detect and there has been no universally accepted scheme to counter them as yet. In this paper, we discuss the different kinds of Sybil attacks including those occurring in peer-to-peer reputation systems, self-organising networks and even social network systems. In addition, various methods that have been suggested over time to decrease or eliminate their risk completely are also analysed along with their modus operandi.


Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism

Abstract: Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach.


Nugget Discovery with a Multi-objective Cultural Algorithm

Abstract: Partial classification popularly known as nugget discovery comes under descriptive knowledge discovery. It involves mining rules for a target class of interest. Classification "If-Then" rules are the most sought out by decision makers since they are the most comprehensible form of knowledge mined by data mining techniques. The rules have certain properties namely the rule metrics which are used to evaluate them. Mining rules with user specified properties can be considered as a multi-objective optimization problem since the rules have to satisfy more than one property to be used by the user. Cultural algorithm (CA) with its knowledge sources have been used in solving many optimization problems. However research gap exists in using cultural algorithm for multi-objective optimization of rules. In the current study a multi-objective cultural algorithm is proposed for partial classification. Results of experiments on benchmark data sets reveal good performance.


On Optimal Top-K String Retrieval

Abstract: Let ${\cal{D}}$ = $\{d_1, d_2, d_3, ..., d_D\}$ be a given set of $D$ (string) documents of total length $n$. The top-$k$ document retrieval problem is to index $\cal{D}$ such that when a pattern $P$ of length $p$, and a parameter $k$ come as a query, the index returns the $k$ most relevant documents to the pattern $P$. Hon et. al. \cite{HSV09} gave the first linear space framework to solve this problem in $O(p + k\log k)$ time. This was improved by Navarro and Nekrich \cite{NN12} to $O(p + k)$. These results are powerful enough to support arbitrary relevance functions like frequency, proximity, PageRank, etc. In many applications like desktop or email search, the data resides on disk and hence disk-bound indexes are needed. Despite of continued progress on this problem in terms of theoretical, practical and compression aspects, any non-trivial bounds in external memory model have so far been elusive. Internal memory (or RAM) solution to this problem decomposes the problem into $O(p)$ subproblems and thus incurs the additive factor of $O(p)$. In external memory, these approaches will lead to $O(p)$ I/Os instead of optimal $O(p/B)$ I/O term where $B$ is the block-size. We re-interpret the problem independent of $p$, as interval stabbing with priority over tree-shaped structure. This leads us to a linear space index in external memory supporting top-$k$ queries (with unsorted outputs) in near optimal $O(p/B + \log_B n + \log^{(h)} n + k/B)$ I/Os for any constant $h${$\log^{(1)}n =\log n$ and $\log^{(h)} n = \log (\log^{(h-1)} n)$}. Then we get $O(n\log^*n)$ space index with optimal $O(p/B+\log_B n + k/B)$ I/Os.


RFID Security Using Lightweight Mutual Authentication And Ownership Transfer Protocol

Abstract: In recent years, radio frequency identification technology has moved into the mainstream applications that help to speed up handling of manufactured goods and materials. RFID tags are divided into two classes: active and passive. Active tag requires a power source that's why its cost is more than passive tags. However, the low-cost RFID tags are facing new challenges to security and privacy. Some solutions utilize expensive cryptographic primitives such as hash or encryption functions, and some lightweight approaches have been reported to be not secure. This paper describes a lightweight Mutual authentication and ownership transfer protocol utilizing minimalistic cryptography using Physically Unclonable Functions (PUF) and Linear Feedback Shift Registers (LFSR). PUFs and LFSRs are very efficient in hardware and particularly suitable for the low-cost RFID tags. To functioning security in low cost RFID tag minimum gate requirement is 2000 gates. To implement security protocols using PUF and LFSR functions need only approx 800 gates. In this paper it is explained how we can authenticate and transfer ownership of low cost RFID tag securely using LFSR and PUF as compared to existing solutions based on hash functions.


Camera identification by grouping images from database, based on shared noise patterns

Abstract: Previous research showed that camera specific noise patterns, so-called PRNU-patterns, are extracted from images and related images could be found. In this particular research the focus is on grouping images from a database, based on a shared noise pattern as an identification method for cameras. Using the method as described in this article, groups of images, created using the same camera, could be linked from a large database of images. Using MATLAB programming, relevant image noise patterns are extracted from images much quicker than common methods by the use of faster noise extraction filters and improvements to reduce the calculation costs. Relating noise patterns, with a correlation above a certain threshold value, can quickly be matched. Hereby, from a database of images, groups of relating images could be linked and the method could be used to scan a large number of images for suspect noise patterns.


Multimedia Steganographic Scheme using Multiresolution Analysis

Abstract: Digital steganography or data hiding has emerged as a new area of research in connection to the communication in secured channel as well as intellectual property protection for multimedia signals. The redundancy in image representation can be exploited successfully to embed specified characteristic information with a good quality of imperceptibility. The hidden multimedia information will be communicated to the authentic user through secured channel as a part of the data. This article deals with a transform domain, block-based and signal non-adaptive/adaptive technique for inserting multimedia signals into an RGB image. The robustness of the proposed method has been tested compared to the other transform domain techniques. Proposed algorithm also shows improvement in visual and statistical invisibility of the hidden information.


IP over Voice-over-IP for censorship circumvention

Abstract: Open communication over the Internet poses a serious threat to countries with repressive regimes, leading them to develop and deploy network-based censorship mechanisms within their networks. Existing censorship circumvention systems face different difficulties in providing unobservable communication with their clients; this limits their availability and poses threats to their users. To provide the required unobservability, several recent circumvention systems suggest modifying Internet routers running outside the censored region to intercept and redirect packets to censored destinations. However, these approaches require modifications to ISP networks, and hence requires cooperation from ISP operators and/or network equipment vendors, presenting a substantial deployment challenge. In this report we propose a deployable and unobservable censorship-resistant infrastructure, called FreeWave. FreeWave works by modulating a client's Internet connections into acoustic signals that are carried over VoIP connections. Such VoIP connections are targeted to a server, FreeWave server, that extracts the tunneled traffic of clients and proxies them to the uncensored Internet. The use of actual VoIP connections, as opposed to traffic morphing, allows FreeWave to relay its VoIP connections through oblivious VoIP nodes, hence keeping itself unblockable from censors that perform IP address blocking. Also, the use of end-to-end encryption prevents censors from identifying FreeWave's VoIP connections using packet content filtering technologies, like deep-packet inspection. We prototype the designed FreeWave system over the popular VoIP system of Skype. We show that FreeWave is able to reliably achieve communication bandwidths that are sufficient for web browsing, even when clients are far distanced from the FreeWave server.


Performance Evaluation of Spread Spectrum Watermarking using Error Control Coding

Abstract: This paper proposes an oblivious watermarking algorithm with blind detection approach for high volume data hiding in image signals. We present a detection reliable signal adaptive embedding scheme for multiple messages in selective sub-bands of wavelet (DWT) coefficients using direct sequence spread spectrum (DS-SS) modulation technique. Here the impact of volumetric distortion sources is analyzed on the ability of analytical bounds in order to recover the watermark messages. In this context, the joint source-channel coding scheme has been employed to obtain the better control of the system robustness. This structure prevents the desynchronisation between encoder and decoder due to selective embedding. The experimental results obtained for Spread Spectrum (SS) transformed domain watermarking demonstrate the efficiency of the proposed system. This algorithmic architecture utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.


Genetic agent approach for improving on-the-fly web map generalization

Abstract: The utilization of web mapping becomes increasingly important in the domain of cartography. Users want access to spatial data on the web specific to their needs. For this reason, different approaches were appeared for generating on-the-fly the maps demanded by users, but those not suffice for guide a flexible and efficient process. Thus, new approach must be developed for improving this process according to the user needs. This work focuses on defining a new strategy which improves on-the-fly map generalization process and resolves the spatial conflicts. This approach uses the multiple representation and cartographic generalization. The map generalization process is based on the implementation of multi- agent system where each agent was equipped with a genetic patrimony.


Robust Image Watermarking Under Pixel Wise Masking Framework

Abstract: The current paper presents a robust watermarking method for still images, which uses the similarity of discrete wavelet transform and human visual system (HVS). The proposed scheme makes the use of pixel wise masking in order to make binary watermark imperceptible to the HVS. The watermark is embedded in the perceptually significant, spatially selected detail coefficients using sub band adaptive threshold scheme. The threshold is computed based on the statistical analysis of the wavelet coefficients. The watermark is embedded several times to achieve better robustness. Here, a new type of non-oblivious detection method is proposed. The improvement in robustness performance against different types of deliberate and non-intentional image impairments (lossy compression, scaling, cropping, filtering etc) is supported through experimental results. The reported result also shows improvement in visual and statistical invisibility of the hidden data. The proposed method is compared with a state of the art frequency based watermarking technique, highlighting its performance. This algorithmic architecture utilizes the existing allocated bandwidth in the data transmission channel in a more efficient manner.


Spread Spectrum based Robust Image Watermark Authentication

Abstract: In this paper, a new approach to Spread Spectrum (SS) watermarking technique is introduced. This problem is particularly interesting in the field of modern multimedia applications like internet when copyright protection of digital image is required. The approach exploits two-predecessor single attractor (TPSA) cellular automata (CA) suitability to work as efficient authentication function in wavelet based SS watermarking domain. The scheme is designed from the analytical study of state transition behaviour of non-group CA and the basic cryptography/encryption scheme is significantly different from the conventional SS data hiding approaches. Experimental studies confirm that the scheme is robust in terms of confidentiality, authentication, non-repudiation and integrity. The transform domain blind watermarking technique offers better visual & statistical imperceptibility and resiliency against different types of intentional & unintentional image degradations. Interleaving and interference cancellation methods are employed to improve the robustness performance significantly compared to conventional matched filter detection.


Efficient Resource Allocation in Resource provisioning policies over Resource Cloud Communication Paradigm

Abstract: Optimal resource utilization for executing tasks within the cloud is one of the biggest challenges. In executing the task over a cloud, the resource provisioner is responsible for providing the resources to create virtual machines. To utilize the resources optimally, the resource provisioner has to take care of the process of allocating resources to Virtual Machine Manager (VMM). In this paper, an efficient way to utilize the resources, within the cloud, to create virtual machines has been proposed considering optimum cost based on performance factor. This performance factor depends upon the overall cost of the resource, communication channel cost, reliability and popularity factor. We have proposed a framework for communication between resource owner and cloud using Resource Cloud Communication Paradigm (RCCP). We extend the CloudSim[2] adding provisioner policies and Efficient Resource Allocation (ERA) algorithm in VMM allocation policy as a decision support for resource provisioner.


Secure Cloud Communication for Effective Cost Management System through MSBE

Abstract: In Cloud Computing Architecture, Brokers are responsible to provide services to the end users. An Effective Cost Management System (ECMS) which works over Secure Cloud Communication Paradigm (SCCP) helps in finding a communication link with overall minimum cost of links. We propose an improved Broker Cloud Communication Paradigm (BCCP) with integration of security issues. Two algorithms are included, first is Secure Optimized Route Cost Finder (S-ORCF) to find optimum route between broker and cloud on the behalf of cost factor and second is Secure Optimized Route Management (S-ORM) to maintain optimum route. These algorithms proposed with cryptographic integrity of the secure route discovery process in efficient routing approaches between broker and cloud. There is lack in Dynamic Source Routing Approach to verify whether any intermediate node has been deleted, inserted or modified with no valid authentication. We use symmetric cryptographic primitives, which is made possible due to multisource broadcast encryption scheme. This paper outlines the use of secure route discovery protocol (SRDP)that employs such a security paradigm in cloud computing.


Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling Mechanism over Broker Virtual Machine Communication Framework

Abstract: In current scenario cloud computing is most widely increasing platform for task execution. Lot of research is going on to cut down the cost and execution time. In this paper, we propose an efficient algorithm to have an effective and fast execution of task assigned by the user. We proposed an effective communication framework between broker and virtual machine for assigning the task and fetching the results in optimum time and cost using Broker Virtual Machine Communication Framework (BVCF). We implement it over cloudsim under VM scheduling policies by modification based on Virtual Machine Cost. Scheduling over Virtual Machine as well as over Cloudlets and Retransmission of Cloudlets are the basic building blocks of the proposed work on which the whole architecture is dependent. Execution of cloudlets is being analyzed over Round Robin and FCFS scheduling policy.


Clustering based approach extracting collocations

Abstract: The following study presents a collocation extraction approach based on clustering technique. This study uses a combination of several classical measures which cover all aspects of a given corpus then it suggests separating bigrams found in the corpus in several disjoint groups according to the probability of presence of collocations. This will allow excluding groups where the presence of collocations is very unlikely and thus reducing in a meaningful way the search space.