Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity

Abstract: We describe a novel geometric methodology for analyzing free-energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent, and provides illustrations of its unique capabilities. An atlas is a labeled partition of the assembly landscape into a topological roadmap of maximal, contiguous, nearly-equipotential-energy conformational regions or macrostates, together with their neighborhood relationships. The new methodology decouples the roadmap generation from sampling and produces: (1) a query-able atlas of local potential energy minima, their basin structure, energy barriers, and neighboring basins; (2) paths between a specified pair of basins; and (3) approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities. Results demonstrating the core algorithm's capabilities have been generated by a resource-light, opensource software implementation EASAL. EASAL atlases several hundred thousand macrostates in minutes on a standard laptop. Subsequent path and basin computations each take seconds. The core algorithm's correctness, time complexity, and efficiency-accuracy tradeoffs are formally guaranteed using modern geometric constraint systems. The methodology further links geometric variables of the input assembling units to a type of intuitive topological bar-code of the output atlas, which in turn determine stable assembled structures and kinetics. This succinct input-output relationship facilitates reverse analysis, and control towards design. We use the novel convex Cayley (distance-based) parametrization that is unique to assembly, as opposed to folding. Sampling microstates with macrostate-specific Cayley parameters avoids gradient-descent search used by all prevailing methods. This increases sampling efficiency, significantly reduces the number of repeated and discarded samples.


Tree-width for first order formulae

Abstract: We introduce tree-width for first order formulae \phi, fotw(\phi). We show that computing fotw is fixed-parameter tractable with parameter fotw. Moreover, we show that on classes of formulae of bounded fotw, model checking is fixed parameter tractable, with parameter the length of the formula. This is done by translating a formula \phi\ with fotw(\phi)<k into a formula of the k-variable fragment L^k of first order logic. For fixed k, the question whether a given first order formula is equivalent to an L^k formula is undecidable. In contrast, the classes of first order formulae with bounded fotw are fragments of first order logic for which the equivalence is decidable. Our notion of tree-width generalises tree-width of conjunctive queries to arbitrary formulae of first order logic by taking into account the quantifier interaction in a formula. Moreover, it is more powerful than the notion of elimination-width of quantified constraint formulae, defined by Chen and Dalmau (CSL 2005): for quantified constraint formulae, both bounded elimination-width and bounded fotw allow for model checking in polynomial time. We prove that fotw of a quantified constraint formula \phi\ is bounded by the elimination-width of \phi, and we exhibit a class of quantified constraint formulae with bounded fotw, that has unbounded elimination-width. A similar comparison holds for strict tree-width of non-recursive stratified datalog as defined by Flum, Frick, and Grohe (JACM 49, 2002). Finally, we show that fotw has a characterization in terms of a cops and robbers game without monotonicity cost.


Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

Abstract: Now-a-days the amount of data stored in educational database increasing rapidly. These databases contain hidden information for improvement of students' performance. Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, Bayesian network etc can be applied on the educational data for predicting the student's performance in examination. This prediction will help to identify the weak students and help them to score better marks. The C4.5, ID3 and CART decision tree algorithms are applied on engineering student's data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to pass, fail or promoted to next year. The results provide steps to improve the performance of the students who were predicted to fail or promoted. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed for the next session. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result.


A Study on the Behavior of a Neural Network for Grouping the Data

Abstract: One of the frequently stated advantages of neural networks is that they can work effectively with non-normally distributed data. But optimal results are possible with normalized data.In this paper, how normality of the input affects the behaviour of a K-means fast learning artificial neural network(KFLANN) for grouping the data is presented. Basically, the grouping of high dimensional input data is controlled by additional neural network input parameters namely vigilance and tolerance.Neural networks learn faster and give better performance if the input variables are pre-processed before being fed to the input units of the neural network. A common way of dealing with data that is not normally distributed is to perform some form of mathematical transformation on the data that shifts it towards a normal distribution.In a neural network, data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user. Among various methods, Normalization is one which organizes data for more efficient access. Experimental results on several artificial and synthetic data sets indicate that the groups formed in the data vary with non-normally distributed data and normalized data and also depends on the normalization method used.


Handwritten digit Recognition using Support Vector Machine

Abstract: Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. The hybrid system gives better recognition result due to better discrimination capability of the NN. A major problem in handwriting recognition is the huge variability and distortions of patterns. Elastic models based on local observations and dynamic programming such HMM are not efficient to absorb this variability. But their vision is local. But they cannot face to length variability and they are very sensitive to distortions. Then the SVM is used to estimate global correlations and classify the pattern. Support Vector Machine (SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better recognition result. The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network


Computational Security Analysis of the UMTS and LTE Authentication and Key Agreement Protocols

Abstract: We present a computational security analysis of the Authentication and Key Agreement (AKA) protocols for both Long-Term Evolution (LTE) and Universal Mobile Telecommunications System (UMTS). This work constitutes the first security analysis of LTE AKA to date and the first computationally sound analysis of UMTS AKA. Our work is the first formal analysis to consider messages that are sent in the core network, where we take into account details of the carrying protocol (i.e., MAP or Diameter) and of the mechanism for secure transport (i.e., MAPsec/TCAPsec or IPsec ESP). Moreover, we report on a deficiency in the protocol specifications of UMTS AKA and LTE AKA and the specifications of the core network security (called network domain security), which may enable efficient attacks. The vulnerability allows an inside attacker not only to impersonate an honest protocol participant during a run of the protocol but also to subsequently use wireless services on his behalf. UMTS AKA run over MAP with MAPsec seems vulnerable in the most straight-forward application of the attack. On the other hand, our analysis shows that UMTS and LTE AKA over Diameter/IPsec and UMTS AKA over MAP/TCAPsec (with sufficiently long session identifiers) computationally satisfy intended authentication properties as well as some key secrecy properties, assuming that the used primitives meet standard cryptographic assumptions.


Personal data disclosure and data breaches: the customer's viewpoint

Abstract: Every time the customer (individual or company) has to release personal information to its service provider (e.g., an online store or a cloud computing provider), it faces a trade-off between the benefits gained (enhanced or cheaper services) and the risks it incurs (identity theft and fraudulent uses). The amount of personal information released is the major decision variable in that trade-off problem, and has a proxy in the maximum loss the customer may incur. We find the conditions for a unique optimal solution to exist for that problem as that maximizing the customer's surplus. We also show that the optimal amount of personal information is influenced most by the immediate benefits the customer gets, i.e., the price and the quantity of service offered by the service provider, rather than by maximum loss it may incur. Easy spenders take larger risks with respect to low-spenders, but an increase in price drives customers towards a more careful risk-taking attitude anyway. A major role is also played by the privacy level, which the service provider employs to regulate the benefits released to the customers. We also provide a closed form solution for the limit case of a perfectly secure provider, showing that the results do not differ significantly from those obtained in the general case. The trade-off analysis may be employed by the customer to determine its level of exposure in the relationship with its service provider.


A note on the fast power series' exponential

Abstract: It is shown that the exponential of a complex power series up to order n can be implemented via (23/12+o(1))M(n) binary arithmetic operations over complex field, where M(n) stands for the (smoothed) complexity of multiplication of polynomials of degree <n in FFT-model. Yet, it is shown how to raise a power series to a constant power with the complexity (27/8+o(1))M(n).


Decentralized Probabilistic Auto-Scaling for Heterogeneous Systems

Abstract: The DEPAS (Decentralized Probabilistic Auto-Scaling) algorithm assumes an overlay network of computing nodes where each node probabilistically decides to shut down, allocate one or more other nodes or do nothing. DEPAS was formulated, tested, and theoretically analyzed for the simplified case of homogenous systems. In this paper, we extend DEPAS to heterogeneous systems.


An Open Question about Dependency of Life Time of Hardware Components and Dynamic Voltage Scaling

Abstract: Open question about Dependency of Life Time of Hardware Components and Dynamic Voltage Scaling (A primary idea)


Building Healthcare - Patient Relationship with CRM 2.0: Lesson Learnt from Prita Mulyasari's Case

Abstract: Healthcare is implementing CRM as a strategy for managing interactions and communication with patients which involves using Information and Communication Technology (ICT) to organize, automate, and coordinate business processes. CRM with the Web technology provides healthcare the ability to broaden service beyond its usual practices, and thus provides a particular advantageous environment for them that want to use ICT to achieve complex healthcare goal. This paper we will discuss and demonstrate how a new approach in CRM will help the healthcare increasing their customer support, and promoting better health to patient. The patients benefited from the customized personal service so that they have full information access to perform self managed their own health and the healthcare provider will have a loyal and retains the right customer. A conceptual framework of approach will be highlighted. Customer centric paradigm in social network's era and value creation of healthcare's business process will be taken into consideration.


Stochastic Characteristics and Simulation of the Random Waypoint Mobility Model

Abstract: Simulation results for Mobile Ad-Hoc Networks (MANETs) are fundamentally governed by the underlying Mobility Model. Thus it is imperative to find whether events functionally dependent on the mobility model 'converge' to well defined functions or constants. This shall ensure the long-run consistency among simulation performed by disparate parties. This paper reviews a work on the discrete Random Waypoint Mobility Model (RWMM), addressing its long run stochastic stability. It is proved that each model in the targeted discrete class of the RWMM satisfies Birkhoff's pointwise ergodic theorem [13], and hence time averaged functions on the mobility model surely converge. We also simulate the most common and general version of the RWMM to give insight into its working.


Health Information Systems (HIS): Concept and Technology

Abstract: A health information system (HIS) is the intersection of between healthcare's business process, and information systems to deliver better healthcare services. The nature of healthcare industry, which is highly influenced by economic, social, politic, and technological factors, has changed over time. This paper will address some important concepts of healthcare and related terminologies to provide a holistic view for HIS. Related technological milestones and major events are briefly summarized. The trends and rapid development of health information technologies are also discussed.


Distributed Cooperative Q-learning for Power Allocation in Cognitive Femtocell Networks

Abstract: In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro-users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms of speed of convergence, fairness and aggregate femtocell capacity.


Path Planning Algorithm for Extinguishing Forest Fires

Abstract: One of the major impacts of climatic changes is due to destroying of forest. Destroying of forest takes place in many ways but the majority of the forest is destroyed due to wild forest fires. In this paper we have presented a path planning algorithm for extinguishing fires which uses Wireless Sensor and Actor Networks (WSANs) for detecting fires. Since most of the works on forest fires are based on Wireless Sensor Networks (WSNs) and a collection of work has been done on coverage, message transmission, deployment of nodes, battery power depletion of sensor nodes in WSNs we focused our work in path planning approach of the Actor to move to the target area where the fire has occurred and extinguish it. An incremental approach is presented in order to determine the successive moves of the Actor to extinguish fire in an environment with and without obstacles. This is done by comparing the moves determined with target location readings obtained using sensors until the Actor reaches the target area to extinguish fires.


A Fully Distributed Algorithm for Throughput Performance in Wireless Networks

Abstract: We study link scheduling in wireless networks under stochastic arrival processes of packets, and give an algorithm that achieves stability in the physical (SINR) interference model. The efficiency of such an algorithm is the fraction of the maximum feasible traffic that the algorithm can handle without queues growing indefinitely. Our algorithm achieves two important goals: (i) efficiency is independent of the size of the network, and (ii) the algorithm is fully distributed, i.e., individual nodes need no information about the overall network topology, not even local information.


Control Complexity in Bucklin, Fallback, and Plurality Voting: An Experimental Approach

Abstract: Walsh [Wal10, Wal09], Davies et al. [DKNW10, DKNW11], and Narodytska et al. [NWX11] studied various voting systems empirically and showed that they can often be manipulated effectively, despite their manipulation problems being NP-hard. Such an experimental approach is sorely missing for NP-hard control problems, where control refers to attempts to tamper with the outcome of elections by adding/deleting/partitioning either voters or candidates. We experimentally tackle NP-hard control problems for Bucklin and fallback voting. Among natural voting systems with efficient winner determination, fallback voting is currently known to display the broadest resistance to control in terms of NP-hardness, and Bucklin voting has been shown to behave almost as well in terms of control resistance [ER10, EPR11, EFPR11]. We also investigate control resistance experimentally for plurality voting, one of the first voting systems analyzed with respect to electoral control [BTT92, HHR07]. Our findings indicate that NP-hard control problems can often be solved effectively in practice. Moreover, our experiments allow a more fine-grained analysis and comparison-across various control scenarios, vote distribution models, and voting systems-than merely stating NP-hardness for all these control problems.


CloudGenius: Decision Support for Web Server Cloud Migration

Abstract: Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualized services in which users are free from the burden of worrying about the low-level system administration details. Migrating Web applications to Cloud services and integrating Cloud services into existing computing infrastructures is non-trivial. It leads to new challenges that often require innovation of paradigms and practices at all levels: technical, cultural, legal, regulatory, and social. The key problem in mapping Web applications to virtualized Cloud services is selecting the best and compatible mix of software images (e.g., Web server image) and infrastructure services to ensure that Quality of Service (QoS) targets of an application are achieved. The fact that, when selecting Cloud services, engineers must consider heterogeneous sets of criteria and complex dependencies between infrastructure services and software images, which are impossible to resolve manually, is a critical issue. To overcome these challenges, we present a framework (called CloudGenius) which automates the decision-making process based on a model and factors specifically for Web server migration to the Cloud. CloudGenius leverages a well known multi-criteria decision making technique, called Analytic Hierarchy Process, to automate the selection process based on a model, factors, and QoS parameters related to an application. An example application demonstrates the applicability of the theoretical CloudGenius approach. Moreover, we present an implementation of CloudGenius that has been validated through experiments.


Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines

Abstract: We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.


Scilab and SIP for Image Processing

Abstract: This paper is an overview of Image Processing and Analysis using Scilab, a free prototyping environment for numerical calculations similar to Matlab. We demonstrate the capabilities of SIP -- the Scilab Image Processing Toolbox -- which extends Scilab with many functions to read and write images in over 100 major file formats, including PNG, JPEG, BMP, and TIFF. It also provides routines for image filtering, edge detection, blurring, segmentation, shape analysis, and image recognition. Basic directions to install Scilab and SIP are given, and also a mini-tutorial on Scilab. Three practical examples of image analysis are presented, in increasing degrees of complexity, showing how advanced image analysis techniques seems uncomplicated in this environment.


Understanding Sampling Style Adversarial Search Methods

Abstract: UCT has recently emerged as an exciting new adversarial reasoning technique based on cleverly balancing exploration and exploitation in a Monte-Carlo sampling setting. It has been particularly successful in the game of Go but the reasons for its success are not well understood and attempts to replicate its success in other domains such as Chess have failed. We provide an in-depth analysis of the potential of UCT in domain-independent settings, in cases where heuristic values are available, and the effect of enhancing random playouts to more informed playouts between two weak minimax players. To provide further insights, we develop synthetic game tree instances and discuss interesting properties of UCT, both empirically and analytically.


Quantitative Multiscale Analysis using Different Wavelets in 1D Voice Signal and 2D Image

Abstract: Mutiscale analysis represents multiresolution scrutiny of a signal to improve its signal quality. Multiresolution analysis of 1D voice signal and 2D image is conducted using DCT, FFT and different wavelets such as Haar, Deubachies, Morlet, Cauchy, Shannon, Biorthogonal, Symmlet and Coiflet deploying the cascaded filter banks based decomposition and reconstruction. The outstanding quantitative analysis of the specified wavelets is done to investigate the signal quality, mean square error, entropy and peak-to-peak SNR at multiscale stage-4 for both 1D voice signal and 2D image. In addition, the 2D image compression performance is significantly found 93.00% in DB-4, 93.68% in bior-4.4, 93.18% in Sym-4 and 92.20% in Coif-2 during the multiscale analysis.


When the Cut Condition is Enough: A Complete Characterization for Multiflow Problems in Series-Parallel Networks

Abstract: Let $G=(V,E)$ be a supply graph and $H=(V,F)$ a demand graph defined on the same set of vertices. An assignment of capacities to the edges of $G$ and demands to the edges of $H$ is said to satisfy the \emph{cut condition} if for any cut in the graph, the total demand crossing the cut is no more than the total capacity crossing it. The pair $(G,H)$ is called \emph{cut-sufficient} if for any assignment of capacities and demands that satisfy the cut condition, there is a multiflow routing the demands defined on $H$ within the network with capacities defined on $G$. We prove a previous conjecture, which states that when the supply graph $G$ is series-parallel, the pair $(G,H)$ is cut-sufficient if and only if $(G,H)$ does not contain an \emph{odd spindle} as a minor; that is, if it is impossible to contract edges of $G$ and delete edges of $G$ and $H$ so that $G$ becomes the complete bipartite graph $K_{2,p}$, with $p\geq 3$ odd, and $H$ is composed of a cycle connecting the $p$ vertices of degree 2, and an edge connecting the two vertices of degree $p$. We further prove that if the instance is \emph{Eulerian} --- that is, the demands and capacities are integers and the total of demands and capacities incident to each vertex is even --- then the multiflow problem has an integral solution. We provide a polynomial-time algorithm to find an integral solution in this case. In order to prove these results, we formulate properties of tight cuts (cuts for which the cut condition inequality is tight) in cut-sufficient pairs. We believe these properties might be useful in extending our results to planar graphs.


A Provably-Correct Protocol for Seamless Communication with Mobile, Multi-Homed Hosts

Abstract: Modern consumer devices, like smartphones and tablets, have multiple interfaces (e.g., WiFi and 3G) that attach to new access points as users move. These mobile, multi-homed computers are a poor match with an Internet architecture that binds connections to fixed end-points with topology- dependent addresses. As a result, hosts typically cannot spread a connection over multiple interfaces or paths, or change locations without breaking existing connections. In this paper, we introduce ECCP, an end-host connection control protocol that allows hosts to communicate over mul- tiple interfaces with dynamically-changing IP addresses. Each ECCP connection consists of one or more flows, each associated with an interface or path. A host can move an existing flow from one interface to another or change the IP address using in-band signaling, without any support from the underlying network. We use formal models to verify that ECCP works correctly in the presence of packet loss, out-of-order delivery, and frequent mobility, and to identify bugs and design limitations in earlier mobility protocols.


On Modelling and Prediction of Total CPU Usage for Applications in MapReduce Environments

Abstract: Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, in both public and private clouds. Two of the challenging questions in such environments are (1) choosing suitable values for MapReduce configuration parameters -e.g., number of mappers, number of reducers, and DFS block size-, and (2) predicting the amount of resources that a user should lease from the service provider. Currently, the tasks of both choosing configuration parameters and estimating required resources are solely the users' responsibilities. In this paper, we present an approach to provision the total CPU usage in clock cycles of jobs in MapReduce environment. For a MapReduce job, a profile of total CPU usage in clock cycles is built from the job past executions with different values of two configuration parameters e.g., number of mappers, and number of reducers. Then, a polynomial regression is used to model the relation between these configuration parameters and total CPU usage in clock cycles of the job. We also briefly study the influence of input data scaling on measured total CPU usage in clock cycles. This derived model along with the scaling result can then be used to provision the total CPU usage in clock cycles of the same jobs with different input data size. We validate the accuracy of our models using three realistic applications (WordCount, Exim MainLog parsing, and TeraSort). Results show that the predicted total CPU usage in clock cycles of generated resource provisioning options are less than 8% of the measured total CPU usage in clock cycles in our 20-node virtual Hadoop cluster.


Homomorphic Hashing for Sparse Coefficient Extraction

Abstract: We study classes of Dynamic Programming (DP) algorithms which, due to their algebraic definitions, are closely related to coefficient extraction methods. DP algorithms can easily be modified to exploit sparseness in the DP table through memorization. Coefficient extraction techniques on the other hand are both space-efficient and parallelisable, but no tools have been available to exploit sparseness. We investigate the systematic use of homomorphic hash functions to combine the best of these methods and obtain improved space-efficient algorithms for problems including LINEAR SAT, SET PARTITION, and SUBSET SUM. Our algorithms run in time proportional to the number of nonzero entries of the last segment of the DP table, which presents a strict improvement over sparse DP. The last property also gives an improved algorithm for CNF SAT with sparse projections.


A Digital Signature Scheme for Long-Term Security

Abstract: In this paper we propose a signature scheme based on two intractable problems, namely the integer factorization problem and the discrete logarithm problem for elliptic curves. It is suitable for applications requiring long-term security and provides a more efficient solution than the existing ones.


Reducing the Arity in Unbiased Black-Box Complexity

Abstract: We show that for all $1<k \leq \log n$ the $k$-ary unbiased black-box complexity of the $n$-dimensional $\onemax$ function class is $O(n/k)$. This indicates that the power of higher arity operators is much stronger than what the previous $O(n/\log k)$ bound by Doerr et al. (Faster black-box algorithms through higher arity operators, Proc. of FOGA 2011, pp. 163--172, ACM, 2011) suggests. The key to this result is an encoding strategy, which might be of independent interest. We show that, using $k$-ary unbiased variation operators only, we may simulate an unrestricted memory of size $O(2^k)$ bits.


A More Reliable Greedy Heuristic for Maximum Matchings in Sparse Random Graphs

Abstract: We propose a new greedy algorithm for the maximum cardinality matching problem. We give experimental evidence that this algorithm is likely to find a maximum matching in random graphs with constant expected degree c>0, independent of the value of c. This is contrary to the behavior of commonly used greedy matching heuristics which are known to have some range of c where they probably fail to compute a maximum matching.


Metadata Management in Scientific Computing

Abstract: Complex scientific codes and the datasets they generate are in need of a sophisticated categorization environment that allows the community to store, search, and enhance metadata in an open, dynamic system. Currently, data is often presented in a read-only format, distilled and curated by a select group of researchers. We envision a more open and dynamic system, where authors can publish their data in a writeable format, allowing users to annotate the datasets with their own comments and data. This would enable the scientific community to collaborate on a higher level than before, where researchers could for example annotate a published dataset with their citations. Such a system would require a complete set of permissions to ensure that any individual's data cannot be altered by others unless they specifically allow it. For this reason datasets and codes are generally presented read-only, to protect the author's data; however, this also prevents the type of social revolutions that the private sector has seen with Facebook and Twitter. In this paper, we present an alternative method of publishing codes and datasets, based on Fluidinfo, which is an openly writeable and social metadata engine. We will use the specific example of the Einstein Toolkit, a shared scientific code built using the Cactus Framework, to illustrate how the code's metadata may be published in writeable form via Fluidinfo.


Designing a WISHBONE Protocol Network Adapter for an Asynchronous Network-on-Chip

Abstract: The Scaling of microchip technologies, from micron to submicron and now to deep sub-micron (DSM) range, has enabled large scale systems-on-chip (SoC). In future deep submicron (DSM) designs, the interconnect effect will definitely dominate performance. Network-on-Chip (NoC) has become a promising solution to bus-based communication infrastructure limitations. NoC designs usually targets Application Specific Integrated Circuits (ASICs), however, the fabrication process costs a lot. Implementing a NoC on an FPGA does not only reduce the cost but also decreases programming and verification cycles. In this paper, an Asynchronous NoC has been implemented on a SPARTAN-3E\textregistered device. The NoC supports basic transactions of both widely used on-chip interconnection standards, the Open Core Protocol (OCP) and the WISHBONE Protocol. Although, FPGA devices are synchronous in nature, it has been shown that they can be used to prototype a Global Asynchronous Local Synchronous (GALS) systems, comprising an Asynchronous NoC connecting IP cores operating in different clock domains.


Irida: A real-time Wireless Sensor Network visualization feedback protocol

Abstract: In this paper, we describe the implementation of a real time visualization and feedback system for Wireless Sensor Network algorithms. The system is based on a fixed hardware testbed, which is deployed on a vertical flat surface and a feedback loop system that takes information about the current state of the network and projects this state, in a visual way, on the surface itself using a video projector. The protocol used is open and simple to use, and can be easily adapted for different hardware configurations. We call our system Irida.


Quartile Clustering: A quartile based technique for Generating Meaningful Clusters

Abstract: Clustering is one of the main tasks in exploratory data analysis and descriptive statistics where the main objective is partitioning observations in groups. Clustering has a broad range of application in varied domains like climate, business, information retrieval, biology, psychology, to name a few. A variety of methods and algorithms have been developed for clustering tasks in the last few decades. We observe that most of these algorithms define a cluster in terms of value of the attributes, density, distance etc. However these definitions fail to attach a clear meaning/semantics to the generated clusters. We argue that clusters having understandable and distinct semantics defined in terms of quartiles/halves are more appealing to business analysts than the clusters defined by data boundaries or prototypes. On the samepremise, we propose our new algorithm named as quartile clustering technique. Through a series of experiments we establish efficacy of this algorithm. We demonstrate that the quartile clustering technique adds clear meaning to each of the clusters compared to K-means. We use DB Index to measure goodness of the clusters and show our method is comparable to EM (Expectation Maximization), PAM (Partition around Medoid) and K Means. We have explored its capability in detecting outlier and the benefit of added semantics. We discuss some of the limitations in its present form and also provide a rough direction in addressing the issue of merging the generated clusters.


A Novel Robust Approach to Least Squares Problems with Bounded Data Uncertainties

Abstract: In this correspondence, we introduce a minimax regret criteria to the least squares problems with bounded data uncertainties and solve it using semi-definite programming. We investigate a robust minimax least squares approach that minimizes a worst case difference regret. The regret is defined as the difference between a squared data error and the smallest attainable squared data error of a least squares estimator. We then propose a robust regularized least squares approach to the regularized least squares problem under data uncertainties by using a similar framework. We show that both unstructured and structured robust least squares problems and robust regularized least squares problem can be put in certain semi-definite programming forms. Through several simulations, we demonstrate the merits of the proposed algorithms with respect to the the well-known alternatives in the literature.


Outlier Detection Techniques for SQL and ETL Tuning

Abstract: RDBMS is the heart for both OLTP and OLAP types of applications. For both types of applications thousands of queries expressed in terms of SQL are executed on daily basis. All the commercial DBMS engines capture various attributes in system tables about these executed queries. These queries need to conform to best practices and need to be tuned to ensure optimal performance. While we use checklists, often tools to enforce the same, a black box technique on the queries for profiling, outlier detection is not employed for a summary level understanding. This is the motivation of the paper, as this not only points out to inefficiencies built in the system, but also has the potential to point evolving best practices and inappropriate usage. Certainly this can reduce latency in information flow and optimal utilization of hardware and software capacity. In this paper we start with formulating the problem. We explore four outlier detection techniques. We apply these techniques over rich corpora of production queries and analyze the results. We also explore benefit of an ensemble approach. We conclude with future courses of action. The same philosophy we have used for optimization of extraction, transform, load (ETL) jobs in one of our previous work. We give a brief introduction of the same in section four.


Linear MMSE-Optimal Turbo Equalization Using Context Trees

Abstract: Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.


SignsWorld; Deeping Into the Silence World and Hearing Its Signs (State of the Art)

Abstract: Automatic speech processing systems are employed more and more often in real environments. Although the underlying speech technology is mostly language independent, differences between languages with respect to their structure and grammar have substantial effect on the recognition systems performance. In this paper, we present a review of the latest developments in the sign language recognition research in general and in the Arabic sign language (ArSL) in specific. This paper also presents a general framework for improving the deaf community communication with the hearing people that is called SignsWorld. The overall goal of the SignsWorld project is to develop a vision-based technology for recognizing and translating continuous Arabic sign language ArSL.


Research collaboration and the expanding science grid: Measuring globalization processes worldwide

Abstract: This paper applies a new model and analytical tool to measure and study contemporary globalization processes in collaborative science - a world in which scientists, scholars, technicians and engineers interact within a 'grid' of interconnected research sites and collaboration networks. The building blocks of our metrics are the cities where scientific research is conducted, as mentioned in author addresses on research publications. The unit of analysis is the geographical distance between those cities. In our macro-level trend analysis, covering the years 2000-2010, we observe that research collaboration distances have been increasing, while the share of collaborative contacts with foreign cities has leveled off. Collaboration distances and growth rates differ significantly between countries and between fields of science. The application of a distance metrics to compare and track these processes opens avenues for further studies, both at the meso-level and at the micro-level, into how research collaboration patterns and trends are driving and shaping the connectivity fabric of world science.


Clustering Using Isoperimetric Number of Trees

Abstract: In this paper we propose a graph-based data clustering algorithm which is based on exact clustering of a minimum spanning tree in terms of a minimum isoperimetry criteria. We show that our basic clustering algorithm runs in $O(n \log n)$ and with post-processing in $O(n^2)$ (worst case) time where $n$ is the size of the data set. We also show that our generalized graph model which also allows the use of potentials at vertices can be used to extract a more detailed pack of information as the {\it outlier profile} of the data set. In this direction we show that our approach can be used to define the concept of an outlier-set in a precise way and we propose approximation algorithms for finding such sets. We also provide a comparative performance analysis of our algorithm with other related ones and we show that the new clustering algorithm (without the outlier extraction procedure) behaves quite effectively even on hard benchmarks and handmade examples.


Low Complexity Turbo-Equalization: A Clustering Approach

Abstract: We introduce a low complexity approach to iterative equalization and decoding, or "turbo equalization", that uses clustered models to better match the nonlinear relationship that exists between likelihood information from a channel decoder and the symbol estimates that arise in soft-input channel equalization. The introduced clustered turbo equalizer uses piecewise linear models to capture the nonlinear dependency of the linear minimum mean square error (MMSE) symbol estimate on the symbol likelihoods produced by the channel decoder and maintains a computational complexity that is only linear in the channel memory. By partitioning the space of likelihood information from the decoder, based on either hard or soft clustering, and using locally-linear adaptive equalizers within each clustered region, the performance gap between the linear MMSE equalizer and low-complexity, LMS-based linear turbo equalizers can be dramatically narrowed.


A New Analysis of an Adaptive Convex Mixture: A Deterministic Approach

Abstract: We introduce a new analysis of an adaptive mixture method that combines outputs of two constituent filters running in parallel to model an unknown desired signal. This adaptive mixture is shown to achieve the mean square error (MSE) performance of the best constituent filter, and in some cases outperforms both, in the steady-state. However, the MSE analysis of this mixture in the steady-state and during the transient regions uses approximations and relies on statistical models on the underlying signals and systems. Hence, such an analysis may not be useful or valid for signals generated by various real life systems that show high degrees of nonstationarity, limit cycles and, in many cases, that are even chaotic. To this end, we perform the transient and the steady-state analysis of this adaptive mixture in a "strong" deterministic sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. In particular, we relate the time-accumulated squared estimation error of this adaptive mixture at any time to the time-accumulated squared estimation error of the optimal convex mixture of the constituent filters directly tuned to the underlying signal in an individual sequence manner.


Do Linguistic Style and Readability of Scientific Abstracts affect their Virality?

Abstract: Reactions to textual content posted in an online social network show different dynamics depending on the linguistic style and readability of the submitted content. Do similar dynamics exist for responses to scientific articles? Our intuition, supported by previous research, suggests that the success of a scientific article depends on its content, rather than on its linguistic style. In this article, we examine a corpus of scientific abstracts and three forms of associated reactions: article downloads, citations, and bookmarks. Through a class-based psycholinguistic analysis and readability indices tests, we show that certain stylistic and readability features of abstracts clearly concur in determining the success and viral capability of a scientific article.


DiscopFlow: A new Tool for Discovering Organizational Structures and Interaction Protocols in WorkFlow

Abstract: This work deals with Workflow Mining (WM) a very active and promising research area. First, in this paper we give a critical and comparative study of three representative WM systems of this area: the ProM, InWolve and WorkflowMiner systems. The comparison is made according to quality criteria that we have defined such as the capacity to filter and convert a Workflow log, the capacity to discover workflow perspectives and the capacity to support Multi-Analysis of processes. The major drawback of these systems is the non possibility to deal with organizational perspective discovering issue. We mean by organizational perspective, the organizational structures (federation, coalition, market or hierarchy) and interaction protocols (contract net, auction or vote). This paper defends the idea that organizational dimension in Multi-Agent System is an appropriate approach to support the discovering of this organizational perspective. Second, the paper proposes a Workflow log meta-model which extends the classical one by considering the interactions among actors thanks to the FIPA-ACL Performatives. Third, it describes in details our DiscopFlow tool which validates our contribution.


Parameter Learning in PRISM Programs with Continuous Random Variables

Abstract: Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD, combines statistical and logical knowledge representation and inference. Inference in these languages is based on enumerative construction of proofs over logic programs. Consequently, these languages permit very limited use of random variables with continuous distributions. In this paper, we extend PRISM with Gaussian random variables and linear equality constraints, and consider the problem of parameter learning in the extended language. Many statistical models such as finite mixture models and Kalman filter can be encoded in extended PRISM. Our EM-based learning algorithm uses a symbolic inference procedure that represents sets of derivations without enumeration. This permits us to learn the distribution parameters of extended PRISM programs with discrete as well as Gaussian variables. The learning algorithm naturally generalizes the ones used for PRISM and Hybrid Bayesian Networks.


CRM 2.0 within E-Health Systems: Towards Achieving Health Literacy & Customer Satisfaction

Abstract: Customer Relationship Management (CRM) within healthcare organization can be viewed as a strategy to attract new customers and retaining them throughout their entire lifetime of relationships. At the same time, the advancement of Web technology known as Web 2.0 plays a significant part in the CRM transition which drives social change that impacts all institutions including business and healthcare organizations. This new paradigm has been named as Social CRM or CRM 2.0 because it is based on Web 2.0. We conducted survey to examine the features of CRM 2.0 in healthcare scenario to the customer in Brunei Darussalam. We draw the conclusion that the CRM 2.0 in healthcare technologies has brought a possibility to extend the services of e-health by enabling patients, patient's families, and community at large to participate more actively in the process of health education; it helps improve health literacy through empowerment, social networking process, and online health educator. This paper is based on our works presented at ICID 2011.


Correlated Node Behavior Model based on Semi Markov Process for MANETS

Abstract: This paper introduces a new model for node behavior namely Correlated Node Behavior Model which is an extension of Node Behavior Model. The model adopts semi Markov process in continuous time which clusters the node that has correlation. The key parameter of the process is determined by five probabilistic parameters based on the Markovian model. Computed from the transition probabilities of the semi-Markov process, the node correlation impact on network survivability and resilience can be measure quantitatively. From the result, the quantitative analysis of correlated node behavior on the survivability is obtained through mathematical description, and the effectiveness and rationality of the proposed model are verified through numerical analysis. The analytical results show that the effect from correlated failure nodes on network survivability is much severer than other misbehaviors.


Distributed Consensus Resilient to Both Crash Failures and Strategic Manipulations

Abstract: In this paper, we study distributed consensus in synchronous systems subject to both unexpected crash failures and strategic manipulations by rational agents in the system. We adapt the concept of collusion-resistant Nash equilibrium to model protocols that are resilient to both crash failures and strategic manipulations of a group of colluding agents. For a system with $n$ distributed agents, we design a deterministic protocol that tolerates 2 colluding agents and a randomized protocol that tolerates $n - 1$ colluding agents, and both tolerate any number of failures. We also show that if colluders are allowed an extra communication round after each synchronous round, there is no protocol that can tolerate even 2 colluding agents and 1 crash failure.


Analysis of Quality of Service Performances of Connection Admission Control Mechanisms in OFDMA IEEE 802.16 Network using BMAP Queuing

Abstract: In this paper, we consider a single-cell IEEE 802.16 environment in which the base station allocates subchannels to the subscriber stations in its coverage area. The subchannels allocated to a subscriber station are shared by multiple connections at that subscriber station. To ensure the Quality of Service (QoS) performances, two Connection Admission Control (CAC) mechanisms, namely, threshold-based and queue-aware CAC mechanisms are considered at a subscriber station. A queuing analytical framework for these admission control mechanisms is presented considering Orthogonal Frequency Division Multiple Access (OFDMA) based transmission at the physical layer. Then, based on the queuing model, both the connection-level and the packet-level performances are studied and compared with their analogues in the case without CAC. The connection arrival is modeled by a Poisson process and the packet arrival for a connection by Batch Markov Arrival Process (BMAP). We determine analytically and numerically different QoS performance measures (connection blocking probability, average number of ongoing connections, average queue length, packet dropping probability, queue throughput and average packet delay).


Onboard Flight Control of a Small Quadrotor Using Single Strapdown Optical Flow Sensor

Abstract: This paper considers onboard control of a small-sized quadrotor using a strapdown embedded optical flow sensor which is conventionally used for desktop mice. The vehicle considered in this paper can carry only few dozen grams of payload, therefore conventional camera-based optical flow methods are not applicable. We present hovering control of the small-sized quadrotor using a single-chip optical flow sensor, implemented on an 8-bit microprocessor without external sensors or communication with a ground control station. Detailed description of all the system components is provided along with evaluation of the accuracy. Experimental results from flight tests are validated with the ground-truth data provided by a high-accuracy reference system.


Real-time Image-based 6-DOF Localization in Large-Scale Environments

Abstract: We present a real-time approach for image-based localization within large scenes that have been reconstructed offline using structure from motion (Sfm). From monocular video, our method continuously computes a precise 6-DOF camera pose, by efficiently tracking natural features and matching them to 3D points in the Sfm point cloud. Our main contribution lies in efficiently interleaving a fast keypoint tracker that uses inexpensive binary feature descriptors with a new approach for direct 2D-to-3D matching. The 2D-to-3D matching avoids the need for online extraction of scale-invariant features. Instead, offline we construct an indexed database containing multiple DAISY descriptors per 3D point extracted at multiple scales. The key to the efficiency of our method lies in invoking DAISY descriptor extraction and matching sparingly during localization, and in distributing this computation over a window of successive frames. This enables the algorithm to run in real-time, without fluctuations in the latency over long durations. We evaluate the method in large indoor and outdoor scenes. Our algorithm runs at over 30 Hz on a laptop and at 12 Hz on a low-power, mobile computer suitable for onboard computation on a quadrotor micro aerial vehicle.


Teacher Module in an Assistance Tool - Adaptating a device to a teaching context and and teacher's preferences

Abstract: This communication presents the genesis and the implementation of a teacher module, which is included in an Assistance Tool (AT). The teacher module is based on a teacher model for which we did a thorough analysis of the state of the art. The aim of the AT is to help a teacher to design pedagogical devices. Teachers can formulate their needs (assistance in the design) and the AT can relieve them from repetitive tasks related to the deployment of a teaching device (assistance in the deployment).


Thesis Report: Resource Utilization Provisioning in MapReduce

Abstract: In this thesis report, we have a survey on state-of-the-art methods for modelling resource utilization of MapReduce applications regard to its configuration parameters. After implementation of one of the algorithms in literature, we tried to find that if CPU usage modelling of a MapReduce application can be used to predict CPU usage of another MapReduce application.


VoIP Steganography and Its Detection - A Survey

Abstract: Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This paper is a survey of the existing VoIP steganography (steganophony) methods and their countermeasures.


Analyzing closed frequent itemsets with convex polytopes

Abstract: Frequent itemsets form a polytope and can be found and analyzed with Linear Programming.


On Training Deep Boltzmann Machines

Abstract: The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.


Blind Channel Estimation Enhancement for Mimo- OFDM Systems under High Mobility Conditions

Abstract: In this paper, we propose an enhancement of a blind channel estimator based on a subspace approach in a MIMO OFDM context (Multi Input Multi Output Orthogonal Frequency Division Multiplexing) in high mobility scenario. As known, the combination between the MIMO context and the OFDM system has stimulated mainly the evolution of the fourth generation broadband wireless communications. The simulations results have demonstrated the effectiveness of the approach for a 16 QAM modulation scheme and had been evaluated in term of bit error rate BER and mean square error MSE versus the signal to noise ratio SNR.


Budget Feasible Mechanism Design: From Prior-Free to Bayesian

Abstract: Budget feasible mechanism design studies procurement combinatorial auctions where the sellers have private costs to produce items, and the buyer(auctioneer) aims to maximize a social valuation function on subsets of items, under the budget constraint on the total payment. One of the most important questions in the field is "which valuation domains admit truthful budget feasible mechanisms with `small' approximations (compared to the social optimum)?" Singer showed that additive and submodular functions have such constant approximations. Recently, Dobzinski, Papadimitriou, and Singer gave an O(log^2 n)-approximation mechanism for subadditive functions; they also remarked that: "A fundamental question is whether, regardless of computational constraints, a constant-factor budget feasible mechanism exists for subadditive functions." We address this question from two viewpoints: prior-free worst case analysis and Bayesian analysis. For the prior-free framework, we use an LP that describes the fractional cover of the valuation function; it is also connected to the concept of approximate core in cooperative game theory. We provide an O(I)-approximation mechanism for subadditive functions, via the worst case integrality gap I of LP. This implies an O(log n)-approximation for subadditive valuations, O(1)-approximation for XOS valuations, and for valuations with a constant I. XOS valuations are an important class of functions that lie between submodular and subadditive classes. We give another polynomial time O(log n/loglog n) sub-logarithmic approximation mechanism for subadditive valuations. For the Bayesian framework, we provide a constant approximation mechanism for all subadditive functions, using the above prior-free mechanism for XOS valuations as a subroutine. Our mechanism allows correlations in the distribution of private information and is universally truthful.


Optimization of Cellular Resources Evading Intra and Inter Tier Interference in Femto cells Equipped Macro cell Networks

Abstract: Cellular network resources are essential to be optimized in Femto cells equipped macro cell networks. This is achieved by increasing the cellular coverage and channel capacity, and reducing power usage and interference between femto cells and macro cells. In this paper, the optimization approach for cellular resources with installed femto cells in macro cell networks has been addressed by deploying smart antennas applications and effect power adaptation method which significantly optimize the cellular coverage, channel capacity, power usage, and intra and inter tier interference. The simulation results also illustrate the outstanding performance of this optimization methodology.


Dynamic PL&T using Two Reference Nodes Equipped with Steered Directional Antenna for Significant PL&T Accuracy

Abstract: Dynamic Position Location and Tracking (PL&T) is proposed deploying the integrated approach of zone finding and triangulation using two friendly nodes equipped with Steered Directional Antenna (DA) in Mobile Ad hoc Networks (MANET). This approach allows the system to use only two references instead of a typical 3 references for a straight triangulation. Moreover, the performance of the proposed algorithm with references using directional antennas shows significant improvement over triangulation using references with Omnidirectional antennas as the beam power is concentrated. However, dynamic switching of reference nodes is frequently required as the target moves outside the predicted zone. This paper presents a better tracking accuracy in using proposed dynamic PL&T as compared to other PL&T techniques. The multipath fading is also addressed with the use of KV transform coding technique which uses forward error correction and sample interleaving achieves greater than 90% tracking accuracy with BERs of 10-6 or better.


Real Time Position Location & Tracking (PL&T) using Prediction Filter and Integrated Zone Finding in OFDM Channel

Abstract: The nature of pre-determined and on-demand mobile network fabrics can be exploited for real time Position Location and Tracking (PL&T) of radios and sensors (nodes) for Global Positioning System (GPS) denied or GPS-free systems. This issue is addressed by a novel system of integrated zone finding and triangulation method for determining the PL&T of nodes when mobile network fabrics are employed based on using directional antennas for radio communications. Each mobile node is switched dynamically between being a reference and a target node in PL&T operation to improve the PL&T accuracy of a target node. This paper presents the Baseline PL&T with predictive Kalman filter and Integrated Zone based PL&T algorithm design that integrates zone finding and triangulation method. The performance of the proposed algorithm is analysed using Interleaving-KV sample coding & error correction in Rayleigh and Rician channel using Orthogonal Frequency Division Multiplexing (OFDM) system under the severe multipath fading.


Automation of Mobile Pick and Place Robotic System for Small Food Industry

Abstract: The use of robotics in food industry is becoming more popular in recent years. The trend seems to continue as long as the robotics technology meets diverse and challenging needs of the food producers. Rapid developments in digital computers and control systems technologies have significant impact in robotics like any other engineering fields. By utilizing new hardware and software tools, design of these complex systems that need strong integration of distinct disciplines is no longer difficult compared to the past. Therefore, the purpose of this paper is to design and implement a micro-controller based on reliable and high performance robotic system for food / biscuit manufacturing line. We propose a design of a vehicle. The robot is capable of picking unbaked biscuits tray and places them into furnace and then after baking it picks the biscuits tray from the furnace. A special gripper is designed to pick and place the biscuits tray with flexibility.


Matrix Recipes for Hard Thresholding Methods

Abstract: In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configurations to achieve complexity vs. accuracy tradeoffs. Moreover, we study acceleration schemes via memory-based techniques and randomized, $\epsilon$-approximate matrix projections to decrease the computational costs in the recovery process. For most of the configurations, we present theoretical analysis that guarantees convergence under mild problem conditions. Simulation results demonstrate notable performance improvements as compared to state-of-the-art algorithms both in terms of reconstruction accuracy and computational complexity.


Recommender systems in industrial contexts

Abstract: This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.


Can an Ad-hoc ontology Beat a Medical Search Engine? The Chronious Search Engine case

Abstract: Chronious is an Open, Ubiquitous and Adaptive Chronic Disease Management Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease (CKD) and Renal Insufficiency. It consists of several modules: an ontology based literature search engine, a rule based decision support system, remote sensors interacting with lifestyle interfaces (PDA, monitor touch-screen) and a machine learning module. All these modules interact each other to allow the monitoring of two types of chronic diseases and to help clinician in taking decision for care purpose. This paper illustrates how the ontology search engine was created and fed and how some comparative test indicated that the ontology based approach give better results, on some estimation parameters, than the main reference web search engine.


Extended Report: The Implicit Calculus

Abstract: Generic programming (GP) is an increasingly important trend in programming languages. Well-known GP mechanisms, such as type classes and the C++0x concepts proposal, usually combine two features: 1) a special type of interfaces; and 2) implicit instantiation of implementations of those interfaces. Scala implicits are a GP language mechanism, inspired by type classes, that break with the tradition of coupling implicit instantiation with a special type of interface. Instead, implicits provide only implicit instantiation, which is generalized to work for any types. This turns out to be quite powerful and useful to address many limitations that show up in other GP mechanisms. This paper synthesizes the key ideas of implicits formally in a minimal and general core calculus called the implicit calculus, and it shows how to build source languages supporting implicit instantiation on top of it. A novelty of the calculus is its support for partial resolution and higher-order rules (a feature that has been proposed before, but was never formalized or implemented). Ultimately, the implicit calculus provides a formal model of implicits, which can be used by language designers to study and inform implementations of similar mechanisms in their own languages.


Secured Position Location and Tracking (SPL&T) for Detection of Multiple Malicious Nodes Maintaining Two Friendly References in Mobile Ad hoc Networks

Abstract: Secured Position Location and Tracking (PL&T) scheme is developed for multiple malicious radios or nodes detection using integrated key based strict friendly scheme and position location and tracking by multi-sectored based multiple target's PL&T. The friendly and malicious nodes detection is based on the integrated key consisting of symmetric keys, geographic location and round trip response time. Two strictly friend references dynamically form the tracking zone over the detected multiple malicious nodes using the multi-sectored adaptive beam forming. This PL&T technique is robust, precise, scalable, and faster than using the single reference, two reference and three reference nodes based PL&T method in the battlefield oriented Mobile Ad hoc Networks. The simulation results show that the lower relative speed bound of any participating node increased the switching overhead, the decreasing received energy with increasing number of the multi-sectored beams reduced tracking accuracy and the strict friendly authentication overhead depends upon the time period between two latest periodic authentication failures.


On the Hamming Distance between base-n representations of whole numbers

Abstract: We first introduce the Hamming distance between two strings. Then, we apply this concept to the representations of whole numbers in base n for all positive integers n > 2. We claim that a simple formula exists for the sum of all Hamming distances between pairs of consec- utive integers from 1 to m, which we will derive. We also state and prove other interesting results concerning the aforementioned topic.


Adaptive Mixture Methods Based on Bregman Divergences

Abstract: We investigate adaptive mixture methods that linearly combine outputs of $m$ constituent filters running in parallel to model a desired signal. We use "Bregman divergences" and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of $m$ constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.


Arabic Keyphrase Extraction using Linguistic knowledge and Machine Learning Techniques

Abstract: In this paper, a supervised learning technique for extracting keyphrases of Arabic documents is presented. The extractor is supplied with linguistic knowledge to enhance its efficiency instead of relying only on statistical information such as term frequency and distance. During analysis, an annotated Arabic corpus is used to extract the required lexical features of the document words. The knowledge also includes syntactic rules based on part of speech tags and allowed word sequences to extract the candidate keyphrases. In this work, the abstract form of Arabic words is used instead of its stem form to represent the candidate terms. The Abstract form hides most of the inflections found in Arabic words. The paper introduces new features of keyphrases based on linguistic knowledge, to capture titles and subtitles of a document. A simple ANOVA test is used to evaluate the validity of selected features. Then, the learning model is built using the LDA - Linear Discriminant Analysis - and training documents. Although, the presented system is trained using documents in the IT domain, experiments carried out show that it has a significantly better performance than the existing Arabic extractor systems, where precision and recall values reach double their corresponding values in the other systems especially for lengthy and non-scientific articles.


On the Boundaries of Trust and Security in Computing and Communications Systems

Abstract: This article analyzes trust and security in computing and communications systems. While in human-life, trust usually has some kind of commonly understood meaning, in the realm of computing and communications systems, it could be interpreted differently in different environments and settings. On the other hand, security is about making sure that the participating entities are legitimate in a communication event or incident so that the core requirements of privacy, integrity, and authenticity are maintained. This notion is also true for our human life, even for example entering a house needs legitimacy of a person. Some boundary lines preserve the security; otherwise an unwanted access is called a 'security breach'. The intent of this article is to compare and discuss these two terms with our societal behavior and understanding amongst entities. To illustrate these issues especially in computing and communications world, some of the innovating and recent technologies are discussed which demand trust and security within their core operational structures. Alongside presenting generally established ideas, some critical points are mentioned that may be sometimes debatable within the research community.


Online Load Balancing on Unrelated Machines with Startup Costs

Abstract: Motivated by applications in energy-efficient scheduling in data centers, Khuller, Li, and Saha introduced the {\em machine activation} problem as a generalization of the classical optimization problems of set cover and load balancing on unrelated machines. In this problem, a set of $n$ jobs have to be distributed among a set of $m$ (unrelated) machines, given the processing time of each job on each machine, where each machine has a startup cost. The goal is to produce a schedule of minimum total startup cost subject to a constraint $\bf L$ on its makespan. While Khuller {\em et al} considered the offline version of this problem, a typical scenario in scheduling is one where jobs arrive online and have to be assigned to a machine immediately on arrival. We give an $(O(\log (mn)\log m), O(\log m))$-competitive randomized online algorithm for this problem, i.e. the schedule produced by our algorithm has a makespan of $O({\bf L} \log m)$ with high probability, and a total expected startup cost of $O(\log (mn)\log m)$ times that of an optimal offline schedule with makespan $\bf L$. The competitive ratios of our algorithm are (almost) optimal. Our algorithms use the online primal dual framework introduced by Alon {\em et al} for the online set cover problem, and subsequently developed further by Buchbinder, Naor, and co-authors. To the best of our knowledge, all previous applications of this framework have been to linear programs (LPs) with either packing or covering constraints. One novelty of our application is that we use this framework for a mixed LP that has both covering and packing constraints. We hope that the algorithmic techniques developed in this paper to simultaneously handle packing and covering constraints will be useful for solving other online optimization problems as well.


Truthfulness, Proportional Fairness, and Efficiency

Abstract: How does one allocate a collection of resources to a set of strategic agents in a fair and efficient manner without using money? For in many scenarios it is not feasible to use money to compensate agents for otherwise unsatisfactory outcomes. This paper studies this question, looking at both fairness and efficiency measures. We employ the proportionally fair solution, which is a well-known fairness concept for money-free settings. But although finding a proportionally fair solution is computationally tractable, it cannot be implemented in a truthful fashion. Consequently, we seek approximate solutions. We give several truthful mechanisms which achieve proportional fairness in an approximate sense. We use a strong notion of approximation, requiring the mechanism to give each agent a good approximation of its proportionally fair utility. In particular, one of our mechanisms provides a better and better approximation factor as the minimum demand for every good increases. A motivating example is provided by the massive privatization auction in the Czech republic in the early 90s. With regard to efficiency, prior work has shown a lower bound of 0.5 on the approximation factor of any swap-dictatorial mechanism approximating a social welfare measure even for the two agents and multiple goods case. We surpass this lower bound by designing a non-swap-dictatorial mechanism for this case. Interestingly, the new mechanism builds on the notion of proportional fairness.


A Novel Bluetooth Man-In-The-Middle Attack Based On SSP using OOB Association model

Abstract: As an interconnection technology, Bluetooth has to address all traditional security problems, well known from the distributed networks. Moreover, as Bluetooth networks are formed by the radio links, there are also additional security aspects whose impact is yet not well understood. In this paper, we propose a novel Man-In-The-Middle (MITM) attack against Bluetooth enabled mobile phone that support Simple Secure Pairing(SSP). From the literature it was proved that the SSP association models such as Numeric comparison, Just works and passkey Entry are not more secure. Here we propose the Out Of Band (OOB) channeling with enhanced security than the previous methods.


Turing machines can be efficiently simulated by the General Purpose Analog Computer

Abstract: The Church-Turing thesis states that any sufficiently powerful computational model which captures the notion of algorithm is computationally equivalent to the Turing machine. This equivalence usually holds both at a computability level and at a computational complexity level modulo polynomial reductions. However, the situation is less clear in what concerns models of computation using real numbers, and no analog of the Church-Turing thesis exists for this case. Recently it was shown that some models of computation with real numbers were equivalent from a computability perspective. In particular it was shown that Shannon's General Purpose Analog Computer (GPAC) is equivalent to Computable Analysis. However, little is known about what happens at a computational complexity level. In this paper we shed some light on the connections between this two models, from a computational complexity level, by showing that, modulo polynomial reductions, computations of Turing machines can be simulated by GPACs, without the need of using more (space) resources than those used in the original Turing computation, as long as we are talking about bounded computations. In other words, computations done by the GPAC are as space-efficient as computations done in the context of Computable Analysis.


Optimizing the Replay Protection at the Link Layer Security Framework in Wireless Sensor Networks

Abstract: Ensuring communications security in Wireless Sensor Networks (WSNs) is very vital because the security protocols therein, should be devised to work at the link layer. Theoretically, any link layer security protocol must support three vital security attributes viz. Confidentiality, Message Integrity and Replay protection. However, in order to ensure lesser overhead, replay protection is often not incorporated as part of the link layer security framework. We argue here, that it is essential to implement replay protection at the link layer only and devise a simple scheme to do so. We first survey the common approaches to ensuring replay protection in conventional networks. We also implement the conventional algorithms for replay protection using the link layer framework for WSNs viz. TinySec as the underlying platform. Subsequently analyzing their limitations, we propose a novel Bloom-filter based replay protection algorithm for unicast communications. We show that our algorithm is better than the other contemporary approaches for ensuring replay protection in unicast communications in the WSNs.


FlexiSec: A Configurable Link Layer Security Architecture for Wireless Sensor Networks

Abstract: Ensuring communications security in Wireless Sensor Networks (WSNs) indeed is critical; due to the criticality of the resources in the sensor nodes as well as due to their ubiquitous and pervasive deployment, with varying attributes and degrees of security required. The proliferation of the next generation sensor nodes, has not solved this problem, because of the greater emphasis on low-cost deployment. In addition, the WSNs use data-centric multi-hop communication that in turn, necessitates the security support to be devised at the link layer (increasing the cost of security related operations), instead of being at the application layer, as in general networks. Therefore, an energy-efficient link layer security framework is necessitated. There do exists a number of link layer security architectures that offer some combinations of the security attributes desired by different WSN applications. However, as we show in this paper, none of them is responsive to the actual security demands of the applications. Therefore, we believe that there is a need for investigating the feasibility of a configurable software-based link layer security architecture wherein an application can be compiled flexibly, with respect to its actual security demands. In this paper, we analyze, propose and experiment with the basic design of such configurable link layer security architecture for WSNs. We also experimentally evaluate various aspects related to our scheme viz. configurable block ciphers, configurable block cipher modes of operations, configurable MAC sizes and configurable replay protection. The architecture proposed is aimed to offer the optimal level of security at the minimal overhead, thus saving the precious resources in the WSNs.


A Novel Approach for Secure Data Aggregation in Wireless Sensor Networks

Abstract: The Wireless Sensor Networks (WSNs) are composed of resource starved sensor nodes that are deployed to sense, process and communicate vital information to the base station. Due to the stringent constraints on the resources in the sensor nodes on one hand and due to the communications costs being always significantly higher than the data processing costs, the WSNs typically, employ in-network processing, which aims at reducing effectively, the total number of packets eventually transmitted to the base station. Such innetwork processing largely employs data aggregation operations that aggregate the data into a compact representation for further transmission. However, due to the ubiquitous & pervasive deployment, heavier resource demands of the security protocols and due to the stringent resource constraints in WSN nodes, the security concerns in WSNs are even otherwise critical. These concerns assume alarming proportions when using data aggregation in which the output of the data aggregator nodes depends on that of various other nodes. Hence, the protocols for data aggregation have to carefully devised with a constant vigil on ensuring security of the data. In this paper, based on our survey of the existing research efforts for ensuring secure data aggregation, we propose a novel approach using homomorphic encryption and additive digital signatures to achieve confidentiality, integrity and availability for secure data aggregation in wireless sensor networks.


On Irrelevance and Algorithmic Equality in Predicative Type Theory

Abstract: Dependently typed programs contain an excessive amount of static terms which are necessary to please the type checker but irrelevant for computation. To separate static and dynamic code, several static analyses and type systems have been put forward. We consider Pfenning's type theory with irrelevant quantification which is compatible with a type-based notion of equality that respects eta-laws. We extend Pfenning's theory to universes and large eliminations and develop its meta-theory. Subject reduction, normalization and consistency are obtained by a Kripke model over the typed equality judgement. Finally, a type-directed equality algorithm is described whose completeness is proven by a second Kripke model.


Citation Analysis with Medical Subject Headings (MeSH) using the Web of Knowledge: A new routine

Abstract: Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case-by-case basis. A technique is here developed for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science. This freeware routine is applied to the case of "Brugada Syndrome," a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to Web-of-Science Categories other than "Cardiac and Cardiovascular Systems"), perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.


A Unifying Framework to Characterize the Power of a Language to Express Relations

Abstract: In this extended abstract we provide a unifying framework that can be used to characterize and compare the expressive power of query languages for different data base models. The framework is based upon the new idea of valid partition, that is a partition of the elements of a given data base, where each class of the partition is composed by elements that cannot be separated (distinguished) according to some level of information contained in the data base. We describe two applications of this new framework, first by deriving a new syntactic characterization of the expressive power of relational algebra which is equivalent to the one given by Paredaens, and subsequently by studying the expressive power of a simple graph-based data model.


Altmetrics in the wild: Using social media to explore scholarly impact

Abstract: In growing numbers, scholars are integrating social media tools like blogs, Twitter, and Mendeley into their professional communications. The online, public nature of these tools exposes and reifies scholarly processes once hidden and ephemeral. Metrics based on this activities could inform broader, faster measures of impact, complementing traditional citation metrics. This study explores the properties of these social media-based metrics or "altmetrics", sampling 24,331 articles published by the Public Library of Science. We find that that different indicators vary greatly in activity. Around 5% of sampled articles are cited in Wikipedia, while close to 80% have been included in at least one Mendeley library. There is, however, an encouraging diversity; a quarter of articles have nonzero data from five or more different sources. Correlation and factor analysis suggest citation and altmetrics indicators track related but distinct impacts, with neither able to describe the complete picture of scholarly use alone. There are moderate correlations between Mendeley and Web of Science citation, but many altmetric indicators seem to measure impact mostly orthogonal to citation. Articles cluster in ways that suggest five different impact "flavors", capturing impacts of different types on different audiences; for instance, some articles may be heavily read and saved by scholars but seldom cited. Together, these findings encourage more research into altmetrics as complements to traditional citation measures.


Optimism for Boosting Concurrency

Abstract: Modern concurrent programming benefits from a large variety of synchronization techniques. These include conventional pessimistic locking, as well as optimistic techniques based on conditional synchronization primitives or transactional memory. Yet, it is unclear which of these approaches better leverage the concurrency inherent to multi-cores. In this paper, we compare the level of concurrency one can obtain by converting a sequential program into a concurrent one using optimistic or pessimistic techniques. To establish fair comparison of such implementations, we introduce a new correctness criterion for concurrent programs, defined independently of the synchronization techniques they use. We treat a program's concurrency as its ability to accept a concurrent schedule, a metric inspired by the theories of both databases and transactional memory. We show that pessimistic locking can provide strictly higher concurrency than transactions for some applications whereas transactions can provide strictly higher concurrency than pessimistic locks for others. Finally, we show that combining the benefits of the two synchronization techniques can provide strictly more concurrency than any of them individually. We propose a list-based set algorithm that is optimal in the sense that it accepts all correct concurrent schedules. As we show via experimentation, the optimality in terms of concurrency is reflected by scalability gains.


Very Short Literature Survey From Supervised Learning To Surrogate Modeling

Abstract: The past century was era of linear systems. Either systems (especially industrial ones) were simple (quasi)linear or linear approximations were accurate enough. In addition, just at the ending decades of the century profusion of computing devices were available, before then due to lack of computational resources it was not easy to evaluate available nonlinear system studies. At the moment both these two conditions changed, systems are highly complex and also pervasive amount of computation strength is cheap and easy to achieve. For recent era, a new branch of supervised learning well known as surrogate modeling (meta-modeling, surface modeling) has been devised which aimed at answering new needs of modeling realm. This short literature survey is on to introduce surrogate modeling to whom is familiar with the concepts of supervised learning. Necessity, challenges and visions of the topic are considered.


Isomorphism of graph classes related to the circular-ones property

Abstract: We give a linear-time algorithm that checks for isomorphism between two 0-1 matrices that obey the circular-ones property. This algorithm leads to linear-time isomorphism algorithms for related graph classes, including Helly circular-arc graphs, \Gamma-circular-arc graphs, proper circular-arc graphs and convex-round graphs.


UML modelling of geographic routing protocol "Greedy Perimeter Stateless Routing" for its integration into the "Java Network Simulator"

Abstract: In this work we propose an UML modeling of the "Greedy Perimeter Stateless Routing" (GPSR) protocol that integrate this geographic routing protocol, into "JavaNetwork Simulator" to simulate and study this protocol in a first time and offer some improvement in these features. Java Network Simulator (JNS) is a project of "translation" of Network Simulator (NS) in Java initiated by "the UCL Department of Computer Science". This simulator is not as complete as ns-2, but it is much more accessible to programmers unfamiliar with Tcl. Java Network Simulator does not support so far, no routing protocol for vehicular ad hoc networks and all the routing decisions are made statically or using RIP and OSPF. By modeling and integrating the routing protocol GPSR to JNS, users will be able to understand the concept of the geographic routing and how the routing information is transmitted and updated between nodes in vehicular ad hoc network. The article first examines the architecture of the Java Network Simulator, then gives a brief review of the routing protocol GPSR and finally presents our UML modeling incorporating GPSR in the Java Network Simulator.


On a New Method of Storing a Variable Size Array

Abstract: This paper introduces a new data structure, log_vector, with the following properties: constant time random access to individual elements; constant time element addition to the end; constant time element removal from the end; constant time empty data structure creation; amortized constant space per individual elements; constant additional space used.


Achieving Congestion Diversity in Multi-hop Wireless Mesh Networks

Abstract: This paper reports on the first systematic study of congestion-aware routing algorithms for wireless mesh networks to achieve an improved end-end delay performance. In particular, we compare 802.11 compatible implementations of a set of congestion-aware routing protocols against our implementation of state of the art shortest path routing protocol (SRCR). We implement congestion-aware routing algorithms Backpressure (BP), Enhanced-Backpressure (E-BP) adapted from [1], [2] suitably adjusted for 802.11 implementation. We then propose and implement Congestion Diversity Protocol (CDP) adapted from [3] recognizing the limitations of BP and E-BP for 802.11-based wireless networks. SRCR solely utilizes link qualities, while BP relies on queue differential to route packets. CDP and E-BP rely on distance metrics which take into account queue backlogs and link qualities in the network. E-BP computes its metric by summing the ETX and queue differential, while CDP determines its metric by calculating the least draining time to the destination. Our small testbed consisting of twelve 802.11g nodes enables us to empirically compare the performance of congestion-aware routing protocols (BP, E-BP and CDP) against benchmark SRCR. For medium to high load UDP traffic, we observe that CDP exhibits significant improvement with respect to both end-end delay and throughput over other protocols with no loss of performance for TCP traffic. Backpressure-based routing algorithms (BP and E-BP) show poorer performance for UDP and TCP traffic. Finally, we carefully study the effects of the modular approach to congestion-aware routing design in which the MAC layer is left intact


Texture Classification Approach Based on Combination of Edge & Co-occurrence and Local Binary Pattern

Abstract: Texture classification is one of the problems which has been paid much attention on by computer scientists since late 90s. If texture classification is done correctly and accurately, it can be used in many cases such as Pattern recognition, object tracking, and shape recognition. So far, there have been so many methods offered to solve this problem. Near all these methods have tried to extract and define features to separate different labels of textures really well. This article has offered an approach which has an overall process on the images of textures based on Local binary pattern and Gray Level Co-occurrence matrix and then by edge detection, and finally, extracting the statistical features from the images would classify them. Although, this approach is a general one and is could be used in different applications, the method has been tested on the stone texture and the results have been compared with some of the previous approaches to prove the quality of proposed approach.


A Co-Prime Blur Scheme for Data Security in Video Surveillance

Abstract: This paper presents a novel Coprime Blurred Pair (CBP) model for visual data-hiding for security in camera surveillance. While most previous approaches have focused on completely encrypting the video stream, we introduce a spatial encryption scheme by blurring the image/video contents to create a CBP. Our goal is to obscure detail in public video streams by blurring while allowing behavior to be recognized and to quickly deblur the stream so that details are available if behavior is recognized as suspicious. We create a CBP by blurring the same latent image with two unknown kernels. The two kernels are coprime when mapped to bivariate polynomials in the z domain. To deblur the CBP we first use the coprime constraint to approximate the kernels and sample the bivariate CBP polynomials in one dimension on the unit circle. At each sample point, we factor the 1D polynomial pair and compose the results into a 2D kernel matrix. Finally, we compute the inverse Fast Fourier Transform (FFT) of the kernel matrices to recover the coprime kernels and then the latent video stream. It is therefore only possible to deblur the video stream if a user has access to both streams. To improve the practicability of our algorithm, we implement our algorithm using a graphics processing unit (GPU) to decrypt the blurred video streams in real-time, and extensive experimental results demonstrate that our new scheme can effectively protect sensitive identity information in surveillance videos and faithfully reconstruct the unblurred video stream when two blurred sequences are available.