Traffic and Security using Randomized Dispersive Routes in Heterogeneous Sensor Network

Abstract: Generally traffic and the sensor network security have many challenges in the transmission of data in the network. The existing schemes consider homogeneous sensor networks which have poor performance and scalability. Due to many-to-one traffic pattern, sensors may communicate with small portion of its neighbours. Key management is the critical process in sensor nodes to secure the data. Most existing schemes establish shared keys for all the sensors no matter whether they are communicating or not. Hence it leads to large storage overhead. Another problem in sensor network is compromised node attack and denial of service attack which occurs because of its wireless nature. Existing multi path routing algorithms are vulnerable to these attacks. So once an adversary acquires the routing algorithm, it can compute the same routes known to the source, and hence endanger all information sent over these routes. If an adversary performs node compromise attack, they can easily get the encryption/ decryption keys used by that node and hence they can intercept the information easily. In this paper we are proposing a key management scheme which only establishes shared keys with their communicating neighbour and a mechanism to generate randomized multipath routes for secure transmission of data to the sink. Here we are adopting heterogeneous sensor networks and we are utilizing elliptic curve cryptography for efficient key management which is more efficient, scalable, and highly secure and reduces communication overhead. The routes generated by our mechanism are highly dispersive, energy efficient and making them quite capable of bypassing the back holes at low energy cost.


Packet Score based network security and Traffic Optimization

Abstract: One of the critical threat to internet security is Distributed Denial of Service (DDoS). This paper by the introduction of automated online attack classification and attack packet discarding helps to resolve the network security issue by certain level. The incoming packets are assigned scores based on the priority associated with the attributes and on comparison with probability distribution of arriving packets on per packet basis.


Informed Source Separation using Iterative Reconstruction

Abstract: This paper presents a technique for Informed Source Separation (ISS) of a single channel mixture, based on the Multiple Input Spectrogram Inversion method. The reconstruction of the source signals is iterative, alternating between a time- frequency consistency enforcement and a re-mixing constraint. A dual resolution technique is also proposed, for sharper transients reconstruction. The two algorithms are compared to a state-of-the-art Wiener-based ISS technique, on a database of fourteen monophonic mixtures, with standard source separation objective measures. Experimental results show that the proposed algorithms outperform both this reference technique and the oracle Wiener filter by up to 3dB in distortion, at the cost of a significantly heavier computation.


Typing Copyless Message Passing

Abstract: We present a calculus that models a form of process interaction based on copyless message passing, in the style of Singularity OS. The calculus is equipped with a type system ensuring that well-typed processes are free from memory faults, memory leaks, and communication errors. The type system is essentially linear, but we show that linearity alone is inadequate, because it leaves room for scenarios where well-typed processes leak significant amounts of memory. We address these problems basing the type system upon an original variant of session types.


Discovery through Gossip

Abstract: We study randomized gossip-based processes in dynamic networks that are motivated by discovery processes in large-scale distributed networks like peer-to-peer or social networks. A well-studied problem in peer-to-peer networks is the resource discovery problem. There, the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network - new edges are added to the network - and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes with a continuously self-changing topology remains a challenging problem with obvious applications. This paper studies and analyzes two natural gossip-based discovery processes. In the push process, each node repeatedly chooses two random neighbors and puts them in contact (i.e., "pushes" their mutual information to each other). In the pull discovery process, each node repeatedly requests or "pulls" a random contact from a random neighbor. Both processes are lightweight, local, and naturally robust due to their randomization. Our main result is an almost-tight analysis of the time taken for these two randomized processes to converge. We show that in any undirected n-node graph both processes take O(n log^2 n) rounds to connect every node to all other nodes with high probability, whereas Omega(n log n) is a lower bound. In the directed case we give an O(n^2 log n) upper bound and an Omega(n^2) lower bound for strongly connected directed graphs. A key technical challenge that we overcome is the analysis of a randomized process that itself results in a constantly changing network which leads to complicated dependencies in every round.


Truthful Mechanisms for Competing Submodular Processes

Abstract: Motivated by applications to word-of-mouth advertising, we consider a game-theoretic scenario in which competing advertisers want to target initial adopters in a social network. Each advertiser wishes to maximize the resulting cascade of influence, modeled by a general network diffusion process. However, competition between products may adversely impact the rate of adoption for any given firm. The resulting framework gives rise to complex preferences that depend on the specifics of the stochastic diffusion model and the network topology. We study this model from the perspective of a central mechanism, such as a social networking platform, that can optimize seed placement as a service for the advertisers. We ask: given the reported demands of the competing firms, how should a mechanism choose seeds to maximize overall efficiency? Beyond the algorithmic problem, competition raises issues of strategic behaviour: rational agents should not be incentivized to underreport their budget demands. We show that when there are two players, the social welfare can be $2$-approximated by a polynomial-time strategyproof mechanism. Our mechanism is defined recursively, randomizing the order in which advertisers are allocated seeds according to a particular greedy method. For three or more players, we demonstrate that under additional assumptions (satisfied by many existing models of influence spread) there exists a simpler strategyproof $\frac{e}{e-1}$-approximation mechanism; notably, this second mechanism is not necessarily strategyproof when there are only two players.


Predicting Contextual Sequences via Submodular Function Maximization

Abstract: Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple cost-sensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.


Temporal Analysis of Literary and Programming Prose

Abstract: Literary works reference a variety of globally shared themes including well-known people, events, and time periods. It is particularly interesting to locate patterns that are either invariant across time or exhibit a characteristic change across time, as they could imply something important about society that those works record. This paper suggests the use of Google n-gram viewer as a fast prototyping method for examining time-based properties over a rich sample of literary prose. Using this method, we find that some repeating periods of time, like Sunday, are referenced disproportionally, allowing us to pose questions such as why a day like Thursday is so unpopular. Furthermore, by treating software as a work of prose, we can apply a similar analysis to open-source software repositories and explore time-based relations in commit logs. Doing a simple statistical analysis on a few temporal keywords in the log records, we reinforce and weaken a few beliefs on how college students approach open source software. Finally, we help readers working on their own temporal analysis by comparing the fundamental differences between literary works and code repositories, and suggest blog or wiki as recently-emerging works.


Caracteriza\c{c}\~ao de tempos de ida-e-volta na Internet

Abstract: Round-trip times (RTTs) are an important metric for the operation of many applications in the Internet. For instance, they are taken into account when choosing servers or peers in streaming systems, and they impact the operation of fault detectors and congestion control algorithms. Therefore, detailed knowledge about RTTs is important for application and protocol developers. In this work we present results on measuring RTTs between 81 PlanetLab nodes every ten seconds, for ten days. The resulting dataset has over 550 million measurements. Our analysis gives us a profile of delays in the network and identifies a Gamma distribution as the model that best fits our data. The average times observed are below 500 ms in more than 99% of the pairs, but there is significant variation, not only when we compare different pairs of hosts during the experiment, but also considering any given pair of hosts over time. By using a clustering technique, we observe that links can be divided in five distinct groups based on the distribution of RTTs over time and the losses observed, ranging from groups of near, well-connected pairs, to groups of distant hosts, with lower quality links between them.


The Impact of Visual Appearance on User Response in Online Display Advertising

Abstract: Display advertising has been a significant source of revenue for publishers and ad networks in online advertising ecosystem. One of the main goals in display advertising is to maximize user response rate for advertising campaigns, such as click through rates (CTR) or conversion rates. Although in the online advertising industry we believe that the visual appearance of ads (creatives) matters for propensity of user response, there is no published work so far to address this topic via a systematic data-driven approach. In this paper we quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia. We designed a set of 43 visual features, some of which are novel and some are inspired by related work. We extracted these features from real creatives served on RightMedia. We also designed and conducted a series of experiments to evaluate the effectiveness of visual features for CTR prediction, ranking and performance classification. Based on the evaluation results, we selected a subset of features that have the most important impact on CTR. We believe that the findings presented in this paper will be very useful for the online advertising industry in designing high-performance creatives. It also provides the research community with the first ever data set, initial insights into visual appearance's effect on user response propensity, and evaluation benchmarks for further study.


Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

Abstract: Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 \times 240 image labeling in less than 1 second.


Symbolic Protocol Analysis for Diffie-Hellman

Abstract: We extend symbolic protocol analysis to apply to protocols using Diffie-Hellman operations. Diffie-Hellman operations act on a cyclic group of prime order, together with an exponentiation operator. The exponents form a finite field. This rich algebraic structure has resisted previous symbolic approaches. We work in an algebra defined by the normal forms of a rewriting theory (modulo associativity and commutativity). These normal forms allow us to define our crucial notion of indicator, a vector of integers that summarizes how many times each secret exponent appears in a message. We prove that the adversary can never construct a message with a new indicator in our adversary model. Using this invariant, we prove the main security goals achieved by several different protocols that use Diffie-Hellman operators in subtle ways. We also give a model-theoretic justification of our rewriting theory: the theory proves all equations that are uniformly true as the order of the cyclic group varies.


A security suite for wireless body area networks

Abstract: Wireless Body Area Networks (WBANs) have gained a lot of research attention in recent years since they offer tremendous benefits for remote health monitoring and continuous, real-time patient care. However, as with any wireless communication, data security in WBANs is a challenging design issue. Since such networks consist of small sensors placed on the human body, they impose resource and computational restrictions, thereby making the use of sophisticated and advanced encryption algorithms infeasible. This calls for the design of algorithms with a robust key generation / management scheme, which are reasonably resource optimal. This paper presents a security suite for WBANs, comprised of IAMKeys, an independent and adaptive key management scheme for improving the security of WBANs, and KEMESIS, a key management scheme for security in inter-sensor communication. The novelty of these schemes lies in the use of a randomly generated key for encrypting each data frame that is generated independently at both the sender and the receiver, eliminating the need for any key exchange. The simplicity of the encryption scheme, combined with the adaptability in key management makes the schemes simple, yet secure. The proposed algorithms are validated by performance analysis.


Museum: Multidimensional web page segment evaluation model

Abstract: The evaluation of a web page with respect to a query is a vital task in the web information retrieval domain. This paper proposes the evaluation of a web page as a bottom-up process from the segment level to the page level. A model for evaluating the relevancy is proposed incorporating six different dimensions. An algorithm for evaluating the segments of a web page, using the above mentioned six dimensions is proposed. The benefits of fine-granining the evaluation process to the segment level instead of the page level are explored. The proposed model can be incorporated for various tasks like web page personalization, result re-ranking, mobile device page rendering etc.


Wavelength Assignment in Design DWDM Transport Network Using Algorithm BCO-RWA

Abstract: The main problem in designing DWDM transport networks is the wavelength assignment of light paths. One way of solving this problem is to use the algorithm BCO-RWA. However, BCO-RWA has the following disadvantages: algorithm not solved the problem of choosing the location of the optical convector in the network; base algorithm ignores placement optical convector during calculating route selecting probability; base algorithm do not take into account the nonlinear four-wave mixing phenomenon. In this paper we present an algorithm which takes into account a number of disadvantages due to modifications introduced in the algorithm.


Multi-robot coverage to locate fixed targets using formation structures

Abstract: This paper develops an algorithm that guides a multi-robot system in an unknown environment in search of fixed targets. The area to be scanned contains an unknown number of convex obstacles of unknown size and shape. The algorithm covers the entire free space in a sweeping fashion and as such relies on the use of robot formations. The geometry of the robot group is a lateral line formation, which is allowed to split and rejoin when passing obstacles. It is our main goal to exploit this formation structure in order to reduce robot resources to a minimum. Each robot has a limited and finite amount of memory available. No information of the topography is recorded. Communication between two robots is only possible up to a maximum inter-robot distance, and if the line-of-sight between both robots is not obstructed. Broadcasting capabilities and indirect communication are not allowed. Supervisory control is prohibited. The number of robots equipped with GPS is kept as small as possible. Applications of the algorithm are mine field clearance, search-and-rescue missions, and intercept missions. Simulations are included and made available on the internet, demonstrating the flexibility of the algorithm.


A visual programming language for drawing and executing flowcharts

Abstract: With recent advances in graphical user interfaces, more and more tasks on computers have become easier to perform. Out of the belief that creating computer programs can also be one of them, visual programming languages (VPLs) have emerged. The goal of VPLs is to shift a part of work from the programmer to the IDE so that the programmer can focus more on algorithm logic than the syntax of the implementation programming language. In this article, the methods required to build a VPL are presented, with an emphasis on a novel method of code generation in a WHILE language. Also, the methods for achieving basic principles of VPLs will be shown - suitable visual presentation of information and guiding the programmer in the right direction using constraints. These methods are demonstrated on an example of vIDE, a VPL based on the Eclipse integrated development environment (IDE). The design of vIDE with respect to the Eclipse Graphical Modeling Framework (GMF) is described. The concept of a flowchart graphical notation is examined in contrast with the algorithm model it maps to. Finally, the disambiguity of the model representation of an algorithm is discussed and the methods for transforming it to an actual implementation in a programming language.


QRB-Domains and the Probabilistic Powerdomain

Abstract: Is there any Cartesian-closed category of continuous domains that would be closed under Jones and Plotkin's probabilistic powerdomain construction? This is a major open problem in the area of denotational semantics of probabilistic higher-order languages. We relax the question, and look for quasi-continuous dcpos instead. We introduce a natural class of such quasi-continuous dcpos, the omega-QRB-domains. We show that they form a category omega-QRB with pleasing properties: omega-QRB is closed under the probabilistic powerdomain functor, under finite products, under taking bilimits of expanding sequences, under retracts, and even under so-called quasi-retracts. But... omega-QRB is not Cartesian closed. We conclude by showing that the QRB domains are just one half of an FS-domain, merely lacking control.


Getting It All from the Crowd

Abstract: Hybrid human/computer systems promise to greatly expand the usefulness of query processing by incorporating the crowd for data gathering and other tasks. Such systems raise many database system implementation questions. Perhaps most fundamental is that the closed world assumption underlying relational query semantics does not hold in such systems. As a consequence the meaning of even simple queries can be called into question. Furthermore query progress monitoring becomes difficult due to non-uniformities in the arrival of crowdsourced data and peculiarities of how people work in crowdsourcing systems. To address these issues, we develop statistical tools that enable users and systems developers to reason about tradeoffs between time/cost and completeness. These tools can also help drive query execution and crowdsourcing strategies. We evaluate our techniques using experiments on a popular crowdsourcing platform.


Approximate Distance Oracles with Improved Query Time

Abstract: Given an undirected graph $G$ with $m$ edges, $n$ vertices, and non-negative edge weights, and given an integer $k\geq 2$, we show that a $(2k-1)$-approximate distance oracle for $G$ of size $O(kn^{1 + 1/k})$ and with $O(\log k)$ query time can be constructed in $O(\min\{kmn^{1/k},\sqrt km + kn^{1 + c/\sqrt k}\})$ time for some constant $c$. This improves the $O(k)$ query time of Thorup and Zwick. Furthermore, for any $0 < \epsilon \leq 1$, we give an oracle of size $O(kn^{1 + 1/k})$ that answers $((2 + \epsilon)k)$-approximate distance queries in $O(1/\epsilon)$ time. At the cost of a $k$-factor in size, this improves the $128k$ approximation achieved by the constant query time oracle of Mendel and Naor and approaches the best possible tradeoff between size and stretch, implied by a widely believed girth conjecture of Erd\H{o}s. We can match the $O(n^{1 + 1/k})$ size bound of Mendel and Naor for any constant $\epsilon > 0$ and $k = O(\log n/\log\log n)$.


Streaming an image through the eye: The retina seen as a dithered scalable image coder

Abstract: We propose the design of an original scalable image coder/decoder that is inspired from the mammalians retina. Our coder accounts for the time-dependent and also nondeterministic behavior of the actual retina. The present work brings two main contributions: As a first step, (i) we design a deterministic image coder mimicking most of the retinal processing stages and then (ii) we introduce a retinal noise in the coding process, that we model here as a dither signal, to gain interesting perceptual features. Regarding our first contribution, our main source of inspiration will be the biologically plausible model of the retina called Virtual Retina. The main novelty of this coder is to show that the time-dependent behavior of the retina cells could ensure, in an implicit way, scalability and bit allocation. Regarding our second contribution, we reconsider the inner layers of the retina. We emit a possible interpretation for the non-determinism observed by neurophysiologists in their output. For this sake, we model the retinal noise that occurs in these layers by a dither signal. The dithering process that we propose adds several interesting features to our image coder. The dither noise whitens the reconstruction error and decorrelates it from the input stimuli. Furthermore, integrating the dither noise in our coder allows a faster recognition of the fine details of the image during the decoding process. Our present paper goal is twofold. First, we aim at mimicking as closely as possible the retina for the design of a novel image coder while keeping encouraging performances. Second, we bring a new insight concerning the non-deterministic behavior of the retina.


An evaluation of local shape descriptors for 3D shape retrieval

Abstract: As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. A common approach is to calculate a shape descriptor for each object, which can then be compared to determine two objects' similarity. However, these descriptors are often evaluated independently and on different datasets, making them difficult to compare. Using the SHREC 2011 Shape Retrieval Contest of Non-rigid 3D Watertight Meshes dataset, we systematically evaluate a collection of local shape descriptors. We apply each descriptor to the bag-of-words paradigm and assess the effects of varying the dictionary's size and the number of sample points. In addition, several salient point detection methods are used to choose sample points; these methods are compared to each other and to random selection. Finally, information from two local descriptors is combined in two ways and changes in performance are investigated. This paper presents results of these experiment


The Groupon Effect on Yelp Ratings: A Root Cause Analysis

Abstract: Daily deals sites such as Groupon offer deeply discounted goods and services to tens of millions of customers through geographically targeted daily e-mail marketing campaigns. In our prior work we observed that a negative side effect for merchants using Groupons is that, on average, their Yelp ratings decline significantly. However, this previous work was essentially observational, rather than explanatory. In this work, we rigorously consider and evaluate various hypotheses about underlying consumer and merchant behavior in order to understand this phenomenon, which we dub the Groupon effect. We use statistical analysis and mathematical modeling, leveraging a dataset we collected spanning tens of thousands of daily deals and over 7 million Yelp reviews. In particular, we investigate hypotheses such as whether Groupon subscribers are more critical than their peers, or whether some fraction of Groupon merchants provide significantly worse service to customers using Groupons. We suggest an additional novel hypothesis: reviews from Groupon subscribers are lower on average because such reviews correspond to real, unbiased customers, while the body of reviews on Yelp contain some fraction of reviews from biased or even potentially fake sources. Although we focus on a specific question, our work provides broad insights into both consumer and merchant behavior within the daily deals marketplace.


Statistical reliability and path diversity based PageRank algorithm improvements

Abstract: In this paper we present new improvement ideas of the original PageRank algorithm. The first idea is to introduce an evaluation of the statistical reliability of the ranking score of each node based on the local graph property and the second one is to introduce the notion of the path diversity. The path diversity can be exploited to dynamically modify the increment value of each node in the random surfer model or to dynamically adapt the damping factor. We illustrate the impact of such modifications through examples and simple simulations.


Proceedings Fourth Workshop on Mathematically Structured Functional Programming

Abstract: This volume contains the proceedings of the Fourth Workshop on Mathematically Structured Functional Programming (MSFP 2012), taking place on 25 March, 2012 in Tallinn, Estonia, as a satellite event of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012. MSFP is devoted to the derivation of functionality from structure. It highlights concepts from algebra, semantics and type theory as they are increasingly reflected in programming practice, especially functional programming. The workshop consists of two invited presentations and eight contributed papers on a range of topics at that interface.


A High Order Sliding Mode Control with PID Sliding Surface: Simulation on a Torpedo

Abstract: Position and speed control of the torpedo present a real problem for the actuators because of the high level of the system non linearity and because of the external disturbances. The non linear systems control is based on several different approaches, among it the sliding mode control. The sliding mode control has proved its effectiveness through the different studies. The advantage that makes such an important approach is its robustness versus the disturbances and the model uncertainties. However, this approach implies a disadvantage which is the chattering phenomenon caused by the discontinuous part of this control and which can have a harmful effect on the actuators. This paper deals with the basic concepts, mathematics, and design aspects of a control for nonlinear systems that make the chattering effect lower. As solution to this problem we will adopt as a starting point the high order sliding mode approaches then the PID sliding surface. Simulation results show that this control strategy can attain excellent control performance with no chattering problem.


Cyber threats in social networking websites

Abstract: A social network is a social structure made up of individuals or organizations called nodes, which are connected by one or more specific types of interdependency, such as friendship, common interest, and exchange of finance, relationships of beliefs, knowledge or prestige. A cyber threat can be both unintentional and intentional, targeted or non targeted, and it can come from a variety of sources, including foreign nations engaged in espionage and information warfare, criminals, hackers, virus writers, disgruntled employees and contractors working within an organization. Social networking sites are not only to communicate or interact with other people globally, but also one effective way for business promotion. In this paper, we investigate and study the cyber threats in social networking websites. We go through the amassing history of online social websites, classify their types and also discuss the cyber threats, suggest the anti-threats strategies and visualize the future trends of such hoppy popular websites.


The artifacts of component-based development

Abstract: Component based development idea was floated in a conference name "Mass Produced Software Components" in 1968 [1]. Since then engineering and scientific libraries are developed to reuse the previously developed functions. This concept is now widely used in SW development as component based development (CBD). Component-based software engineering (CBSE) is used to develop/ assemble software from existing components [2]. Software developed using components is called component ware [3]. This paper presents different architectures of CBD such as ActiveX, common object request broker architecture (CORBA), remote method invocation (RMI) and simple object access protocol (SOAP). The overall objective of this paper is to support the practice of CBD by comparing its advantages and disadvantages. This paper also evaluates object oriented process model to adapt it for CBD.


Autonomic html interface generator for web applications

Abstract: Recent advances in computing systems have led to a new digital era in which every area of life is nearly interrelated with information technology. However, with the trend towards large-scale IT systems, a new challenge has emerged. The complexity of IT systems is becoming an obstacle that hampers the manageability, operability, and maintainability of modern computing infrastructures. Autonomic computing popped up to provide an answer to these ever-growing pitfalls. Fundamentally, autonomic systems are self-configuring, self-healing, self-optimizing, and self-protecting; hence, they can automate all complex IT processes without human intervention. This paper proposes an autonomic HTML web-interface generator based on XML Schema and Style Sheet specifications for self-configuring graphical user interfaces of web applications. The goal of this autonomic generator is to automate the process of customizing GUI web-interfaces according to the ever-changing business rules, policies, and operating environment with the least IT labor involvement. The conducted experiments showed a successful automation of web interfaces customization that dynamically self-adapts to keep with the always-changing business requirements. Future research can improve upon the proposed solution so that it supports the selfconfiguring of not only web applications but also desktop applications.


Building sustainable ecosystem-oriented architectures

Abstract: Currently, organizations are transforming their business processes into e-services and service-oriented architectures to improve coordination across sales, marketing, and partner channels, to build flexible and scalable systems, and to reduce integration-related maintenance and development costs. However, this new paradigm is still fragile and lacks many features crucial for building sustainable and progressive computing infrastructures able to rapidly respond and adapt to the always-changing market and environmental business. This paper proposes a novel framework for building sustainable Ecosystem- Oriented Architectures (EOA) using e-service models. The backbone of this framework is an ecosystem layer comprising several computing units whose aim is to deliver universal interoperability, transparent communication, automated management, self-integration, self-adaptation, and security to all the interconnected services, components, and devices in the ecosystem. Overall, the proposed model seeks to deliver a comprehensive and a generic sustainable business IT model for developing agile e-enterprises that are constantly up to new business constraints, trends, and requirements. Future research can improve upon the proposed model so much so that it supports computational intelligence to help in decision making and problem solving.


Efficient Web-based Facial Recognition System Employing 2DHOG

Abstract: In this paper, a system for facial recognition to identify missing and found people in Hajj and Umrah is described as a web portal. Explicitly, we present a novel algorithm for recognition and classifications of facial images based on applying 2DPCA to a 2D representation of the Histogram of oriented gradients (2D-HOG) which maintains the spatial relation between pixels of the input images. This algorithm allows a compact representation of the images which reduces the computational complexity and the storage requirments, while maintaining the highest reported recognition accuracy. This promotes this method for usage with very large datasets. Large dataset was collected for people in Hajj. Experimental results employing ORL, UMIST, JAFFE, and HAJJ datasets confirm these excellent properties.


Self-healing systems and virtual structures

Abstract: Modern networks are large, highly complex and dynamic. Add to that the mobility of the agents comprising many of these networks. It is difficult or even impossible for such systems to be managed centrally in an efficient manner. It is imperative for such systems to attain a degree of self-management. Self-healing i.e. the capability of a system in a good state to recover to another good state in face of an attack, is desirable for such systems. In this paper, we discuss the self-healing model for dynamic reconfigurable systems. In this model, an omniscient adversary inserts or deletes nodes from a network and the algorithm responds by adding a limited number of edges in order to maintain invariants of the network. We look at some of the results in this model and argue for their applicability and further extensions of the results and the model. We also look at some of the techniques we have used in our earlier work, in particular, we look at the idea of maintaining virtual graphs mapped over the existing network and assert that this may be a useful technique to use in many problem domains.


Subtyping for F-Bounded Quantifiers and Equirecursive Types (Extended Version)

Abstract: This paper defines a notion of binding trees that provide a suitable model for second-order type systems with F-bounded quantifiers and equirecursive types. It defines a notion of regular binding trees that correspond in the right way to notions of regularity in the first-order case. It defines a notion of subtyping on these trees and proves various properties of the subtyping relation. It defines a mapping from types to trees and shows that types produce regular binding trees. It presents a set of type equality and subtyping rules, and proves them sound and complete with respect to the tree interpretation. It defines a notion of binding-tree automata and how these generate regular binding trees. It gives a polynomial-time algorithm for deciding when two automata's trees are in the subtyping relation.


Survey-Based Analysis of the Proposed Component-Based Development Process

Abstract: The concept of component-based development (CBD) is widely practiced in software (SW) development. CBD is based on reuse of the existing components with the new ones. The objective of this paper is to propose a novel process model for CBD. Importance of repository has also been discussed. A survey has been conducted to evaluate the proposed model. The results of the survey show that proposed process model can be efficiently implemented for CBD projects.


Evaluation of the Improved XP Software Development Model

Abstract: The concept of agile process models has attained great popularity in software (SW) development community in last few years. Agile models promote fast development. Fast development has certain drawbacks, such as weak documentation and performance for medium and large development projects. Fast development also promotes use of agile process models in small-scale projects. This paper modifies and evaluates Extreme Programming (XP) process model and proposes a novel process model based on these modifications.


An Improved XP Software Development Model


Seamless Long Term Learning in Agile Teams for Sustainable Leadership

Abstract: Seamless and continuous support for long term organizational learning needs is essential for long lasting progress of the organization. Agile process model provides an excellent opportunity to cater that specific problem and also helps in motivation, satisfaction, coordination, presentation and technical skills enhancement of agile teams. This long term learning process makes organization to sustain their current successes and lead both organization and team members to successful and dynamic market leaders.


Improvement of Key Problems of Software Testing in Quality Assurance

Abstract: Quality assurance makes sure the project will be completed based on the previously approved specifications, standards and functionality. It is required without defects and possible problems. It monitors and tries to progress the development process from the start of the project. Software Quality Assurance (SQA) is the combination of the entire software development process, which includes software design, coding, source code control, code review, change management, configuration management and release management. In this paper we describe the solution for the key problems of software testing in quality assurance. The existing software practices have some problems such as testing practices, attitude of users and culture of organizations. All these tree problems have some combined problems such as shortcuts in testing, reduction in testing time, poor documentation etc. In this paper we are recommending strategies to provide solution of the said problems mentioned above.


A Validation of the Proposed Component-Based Development Process

Abstract: Component-based development (CBD) is a name, with which software development professionals are quite familiar. There are several models which have been proposed for CBD in last few years. They contain good features but there are some improvement possibilities in them. The objective of this paper is to propose a process for CBD and to evaluate the effects of quality parameters on reusability. The validations of the proposed CBD model provide positive indication for software (SW) industry that it can be successfully implemented for CBD projects.


DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

Abstract: The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.


A New Teaching Model For The Subject Of Software Project Management

Abstract: Software (SW) development is a very tough task which requires a skilled project leader for its success. If the project leader is not skilled enough then project may fail. In the real world of SW engineering 65% of the SW projects fail to meet their objectives as in [1]. The main reason is lack of training of the project mangers. This extreme ratio of failure can be reduced by teaching SW project management (SPM) to the future project managers in the practical manner, so that they may be skillful enough to handle the project in a better way. This paper intends to propose a model to be used to teach SPM to the student of SW engineering to reduce the failure rate of projects.


A Step Forward To Component-based Software Cost Estimation in Object-oriented Environment

Abstract: Software cost estimation (SCE) of a project is pivotal to the acceptance or rejection of the development of software project. Various SCE techniques have been in practice with their own strengths and limitations. The latest of these is object-oriented one. Currently object-oriented approach for SCE is based on Line of Code (LOC), function points, functions and classes etc. Relatively less attention has been paid to the SCE in component-based software engineering (CBSE). So there is a pressing need to search parameters/variables that have a vital role for the SCE using CBSE which is taken up in this paper. This paper further looks at level of significance of all the parameters/variables thus searched. The time is being used as an independent variable because time is a parameter which is almost, all previous in one. Therefore this approach may be in a way an alternate of all previous approaches. Infact the underlying research ultimately may lead towards SCE of complex systems, using CBSE, in a scientific, systematic and comprehensive way.


Empirical Evaluation of the Proposed eXScrum Model: Results of a Case Study

Abstract: Agile models promote fast development. XP and Scrum are the most widely used agile models. This paper investigates the phases of XP and Scrum models in order to identify their potentials and drawbacks. XP model has certain drawbacks, such as not suitable for maintenance projects and poor performance for medium and large-scale development projects. Scrum model has certain limitations, such as lacked in engineering practices. Since, both XP and Scrum models contain good features and strengths but still there are improvement possibilities in these models. Majority of the software development companies are reluctant to switch from traditional methodologies to agile methodologies for development of industrial projects. A fine integration, of software management of the Scrum model and engineering practices of XP model, is very much required to accumulate the strengths and remove the limitations of both models. This is achieved by proposing an eXScrum model. The proposed model is validated by conducting a controlled case study. The results of case study show that the proposed integrated eXScrum model enriches the potentials of both XP and Scrum models and eliminates their drawbacks.


A Comprehensive Study of Commonly Practiced Heavy & Light Weight Software Methodologies

Abstract: Software has been playing a key role in the development of modern society. Software industry has an option to choose suitable methodology/process model for its current needs to provide solutions to give problems. Though some companies have their own customized methodology for developing their software but majority agrees that software methodologies fall under two categories that are heavyweight and lightweight. Heavyweight methodologies (Waterfall Model, Spiral Model) are also known as the traditional methodologies, and their focuses are detailed documentation, inclusive planning, and extroverted design. Lightweight methodologies (XP, SCRUM) are, referred as agile methodologies. Light weight methodologies focused mainly on short iterative cycles, and rely on the knowledge within a team. The aim of this paper is to describe the characteristics of popular heavyweight and lightweight methodologies that are widely practiced in software industries. We have discussed the strengths and weakness of the selected models. Further we have discussed the strengths and weakness between the two opponent methodologies and some criteria is also illustrated that help project managers for the selection of suitable model for their projects.


A Framework for Next Generation Mobile and Wireless Networks Application Development using Hybrid Component Based Development Model

Abstract: The IP Multimedia Subsystems (IMS) that features in Next Generation Networks (NGN) offers the application developer (third party) abilities to map out applications over mobile telecommunication infrastructure. The IMS comes about with APIs useful for mobile application developers to create applications to meet end-users' demands and comply with the provider's infrastructure set up at the same time. Session Initiation Protocol (SIP) is a signaling protocol for this architecture. It is used for establishing sessions in IP network, making it an ideal candidate for supporting terminal mobility in to deliver the services with improved Quality of Services (QOS). The realization of IMS's virtues as far as software design is concerned is faced by lack of standardizations and methodologies throughout application development process. In this paper, we report on progress on ongoing research by our group toward putting together a platform as a testbed used for NGN application development. We examine a novel component based development model used for SIP based mobile applications. The developed model is to be used as framework for general purpose application development over the testbed. We apply this model on MObile Mass EXamination (MOMEX) system that is an application attracting the interest of educational authorities around the world due to its potential convenience.


Novel Component Based Development Model For Sip-Based Mobile Application

Abstract: Universities and Institutions these days' deals with issues related to with assessment of large number of students. Various evaluation methods have been adopted by examiners in different institutions to examining the ability of an individual, starting from manual means of using paper and pencil to electronic, from oral to written, practical to theoretical and many others. There is a need to expedite the process of examination in order to meet the increasing enrolment of students at the universities and institutes. Sip Based Mass Mobile Examination System (SiBMMES) expedites the examination process by automating various activities in an examination such as exam paper setting, Scheduling and allocating examination time and evaluation (auto-grading for objective questions) etc. SiBMMES uses the IP Multimedia Subsystem (IMS) that is an IP communications framework providing an environment for the rapid development of innovative and reusable services Session Initial Protocol (SIP) is a signalling (request-response) protocol for this architecture and it is used for establishing sessions in an IP network, making it an ideal candidate for supporting terminal mobility in the IMS to deliver the services, with the extended services available in IMS like open APIs, common network services, Quality of Services (QoS) like multiple sessions per call, Push to Talk etc often requiring multiple types of media (including voice, video, pictures, and text). SiBMMES is an effective solution for mass education evaluation using mobile and web technology. In this paper, a novel hybrid component based development (CBD) model is proposed for SiBMMES.


Using Covariance Matrices as Feature Descriptors for Vehicle Detection from a Fixed Camera

Abstract: A method is developed to distinguish between cars and trucks present in a video feed of a highway. The method builds upon previously done work using covariance matrices as an accurate descriptor for regions. Background subtraction and other similar proven image processing techniques are used to identify the regions where the vehicles are most likely to be, and a distance metric comparing the vehicle inside the region to a fixed library of vehicles is used to determine the class of vehicle.


A Simulation Model for Evaluating Distributed Systems Dependability

Abstract: In this paper we present a new simulation model designed to evaluate the dependability in distributed systems. This model extends the MONARC simulation model with new capabilities for capturing reliability, safety, availability, security, and maintainability requirements. The model has been implemented as an extension of the multithreaded, process oriented simulator MONARC, which allows the realistic simulation of a wide-range of distributed system technologies, with respect to their specific components and characteristics. The extended simulation model includes the necessary components to inject various failure events, and provides the mechanisms to evaluate different strategies for replication, redundancy procedures, and security enforcement mechanisms, as well. The results obtained in simulation experiments presented in this paper probe that the use of discrete-event simulators, such as MONARC, in the design and development of distributed systems is appealing due to their efficiency and scalability.


Data Dissemination in Opportunistic Networks

Abstract: Mobile devices integrating wireless short-range communication technologies make possible new applications for spontaneous communication, interaction and collaboration. An interesting approach is to use collaboration to facilitate communication when mobile devices are not able to establish direct communication paths. Opportunistic networks, formed when mobile devices communicate with each other while users are in close proximity, can help applications still exchange data in such cases. In opportunistic networks routes are built dynamically, as each mobile device acts according to the store-carry-and-forward paradigm. Thus, contacts between mobile devices are seen as opportunities to move data towards destination. In such networks data dissemination is done using forwarding and is usually based on a publish/subscribe model. Opportunistic data dissemination also raises questions concerning user privacy and incentives. Such problems are addressed differently by various opportunistic data dissemination techniques. In this paper we analyze existing relevant work in the area of data dissemination in opportunistic networks. We present the categories of a proposed taxonomy that captures the capabilities of data dissemination techniques used in such networks. Moreover, we survey relevant data dissemination techniques and analyze them using the proposed taxonomy.


Abstract: Please cite this as "Lorina Negreanu, Cristian Giumale, Alexandru Agache, Mihnea Muraru, Matei Popovici, Ciprian Dobre, A Formal Approach for the Development of Service-Oriented Applications, in Proc. of 18th International Conference on Control Systems and Computer Science (CSCS-18), Bucharest, Romania, 2011, pp. 804-810, ISSN: 2066-4451, Politehnica Press"


Mobile Advertisement in Vehicular Ad-Hoc Networks

Abstract: Mobile Advertisement is a location-aware dissemination solution built on top of a vehicular ad-hoc network. We envision a network of WiFi access points that dynamically disseminate data to clients running on the car's smart device. The approach can be considered an alternative to the static advertisement billboards and can be useful to business companies wanting to dynamically advertise their products and offers to people driving their car. The clients can subscribe to information based on specific topics. We present design solutions that use access points as emitters for transmitting messages to wireless-enabled devices equipped on vehicles. We also present implementation details for the evaluation of the proposed solution using a simulator designed for VANET application. The results show that the application can be used for transferring a significant amount of data even under difficult conditions, such as when cars are moving at increased speeds, or the congested Wi-Fi network causes significant packet loss.


Gossip-based Information Spreading in Mobile Networks

Abstract: Mobile networks receive increasing research interest recently due to their increasingly wide applications in various areas; mobile ad hoc networks (MANET) and Vehicular ad hoc networks (VANET) are two prominent examples. Mobility introduces challenges as well as opportunities: it is known to improve the network throughput as shown in [1]. In this paper, we analyze the effect of mobility on the information spreading based on gossip algorithms. Our contributions are twofold. Firstly, we propose a new performance metric, mobile conductance, which allows us to separate the details of mobility models from the study of mobile spreading time. Secondly, we explore the mobile conductances of several popular mobility models, and offer insights on the corresponding results. Large scale network simulation is conducted to verify our analysis.


Semantic snippet construction for search engine results based on segment evaluation

Abstract: The result listing from search engines includes a link and a snippet from the web page for each result item. The snippet in the result listing plays a vital role in assisting the user to click on it. This paper proposes a novel approach to construct the snippets based on a semantic evaluation of the segments in the page. The target segment(s) is/are identified by applying a model to evaluate segments present in the page and selecting the segments with top scores. The proposed model makes the user judgment to click on a result item easier since the snippet is constructed semantically after a critical evaluation based on multiple factors. A prototype implementation of the proposed model confirms the empirical validation.


Live-marker: A personalized web page content marking tool

Abstract: The tremendous amount of increase in the quantity of information resources available on the web has made the total time that the user spends on a single page very minimal. Users revisiting the same page would be able to fetch the required information much faster if the information that they consumed during the previous visit(s) gets presented to them with a special style. This paper proposes a model which empowers the users to mark the content interesting to them, so that it can be identified easily during successive visits. In addition to the explicit marking by the users, the model facilitates implicit marking based on the user preferences. The prototype implementation based on proposed model validates the model's efficiency.


Segmentation Based Approach to Dynamic Page Construction from Search Engine Results

Abstract: The results rendered by the search engines are mostly a linear snippet list. With the prolific increase in the dynamism of web pages there is a need for enhanced result lists from search engines in order to cope-up with the expectations of the users. This paper proposes a model for dynamic construction of a resultant page from various results fetched by the search engine, based on the web page segmentation approach. With the incorporation of personalization through user profile during the candidate segment selection, the enriched resultant page is constructed. The benefits of this approach include instant, one-shot navigation to relevant portions from various result items, in contrast to a linear page-by-page visit approach. The experiments conducted on the prototype model with various levels of users, quantifies the improvements in terms of amount of relevant information fetched.


We.I.Pe: Web Identification of People using e-mail ID

Abstract: With the phenomenal growth of content in the World Wide Web, the diversity of user supplied queries have become vivid. Searching for people on the web has become an important type of search activity in the web search engines. This paper proposes a model named "We.I.Pe" to identify people on the World Wide Web using e-mail Id as the primary input. The approach followed in this research work provides the collected information, based on the user supplied e-mail id, in an easier to navigate manner. The grouping of collected information based on various sources makes the result visualization process more effective. The proposed model is validated by a prototype implementation. Experiments conducted on the prototype implementation provide encouraging results


A Model for Web Page Usage Mining Based on Segmentation

Abstract: The web page usage mining plays a vital role in enriching the page's content and structure based on the feedbacks received from the user's interactions with the page. This paper proposes a model for micro-managing the tracking activities by fine-tuning the mining from the page level to the segment level. The proposed model enables the web-master to identify the segments which receives more focus from users comparing with others. The segment level analytics of user actions provides an important metric to analyse the factors which facilitate the increase in traffic for the page. The empirical validation of the model is performed through prototype implementation.


"Performance Evaluation of Wi-Fi comparison with WiMAX Networks"

Abstract: Wireless networking has become an important area of research in academic and industry. The main objectives of this paper is to gain in-depth knowledge about the Wi-Fi- WiMAX technology and how it works and understand the problems about the WiFi- WiMAX technology in maintaining and deployment. The challenges in wireless networks include issues like security, seamless handover, location and emergency services, cooperation, and QoS.The performance of the WiMAX is better than the Wi-Fi and also it provide the good response in the access. It's evaluated the Quality of Service (Qos) in Wi-Fi compare with WiMAX and provides the various kinds of security Mechanisms. Authentication to verify the identity of the authorized communicating client stations. Confidentiality (Privacy) to secure that the wirelessly conveyed information will remain private and protected. Take necessary actions and configurations that are needed in order to deploy Wi-Fi -WiMAX with increased levels of security and privacy


Scienceography: the study of how science is written

Abstract: Scientific literature has itself been the subject of much scientific study, for a variety of reasons: understanding how results are communicated, how ideas spread, and assessing the influence of areas or individuals. However, most prior work has focused on extracting and analyzing citation and stylistic patterns. In this work, we introduce the notion of 'scienceography', which focuses on the writing of science. We provide a first large scale study using data derived from the arXiv e-print repository. Crucially, our data includes the "source code" of scientific papers-the LaTEX source-which enables us to study features not present in the "final product", such as the tools used and private comments between authors. Our study identifies broad patterns and trends in two example areas-computer science and mathematics-as well as highlighting key differences in the way that science is written in these fields. Finally, we outline future directions to extend the new topic of scienceography.


Risk Assessment Techniques and Survey Method for COTS Components

Abstract: The Rational Unified Process a software engineering process is gaining popularity nowadays. RUP delivers best software practices for component software Development life cycle It supports component based software development. Risk is involved in every component development phase .neglecting those risks sometimes hampers the software growth and leads to negative outcome. In Order to provide appropriate security and protection levels, identifying various risks is very vital. Therefore Risk identification plays a very crucial role in the component based software development This report addresses incorporation of component based software development cycle into RUP phases, assess several category of risk encountered in the component based software. It also entails a survey method to identify the risk factor and evaluating the overall severity of the component software development in terms of the risk. Formula for determining risk prevention cost and finding the risk probability is also been included. The overall goal of the paper is to provide a theoretical foundation that facilitates a good understanding of risk in relation to componentbased system development


Multi-column Deep Neural Networks for Image Classification

Abstract: Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.


Multi-Scale Matrix Sampling and Sublinear-Time PageRank Computation

Abstract: A fundamental problem arising in many applications in Web science and social network analysis is, given an arbitrary approximation factor $c>1$, to output a set $S$ of nodes that with high probability contains all nodes of PageRank at least $\Delta$, and no node of PageRank smaller than $\Delta/c$. We call this problem {\sc SignificantPageRanks}. We develop a nearly optimal, local algorithm for the problem with runtime complexity $\tilde{O}(n/\Delta)$ on networks with $n$ nodes. We show that any algorithm for solving this problem must have runtime of ${\Omega}(n/\Delta)$, rendering our algorithm optimal up to logarithmic factors. Our algorithm comes with two main technical contributions. The first is a multi-scale sampling scheme for a basic matrix problem that could be of interest on its own. In the abstract matrix problem it is assumed that one can access an unknown {\em right-stochastic matrix} by querying its rows, where the cost of a query and the accuracy of the answers depend on a precision parameter $\epsilon$. At a cost propositional to $1/\epsilon$, the query will return a list of $O(1/\epsilon)$ entries and their indices that provide an $\epsilon$-precision approximation of the row. Our task is to find a set that contains all columns whose sum is at least $\Delta$, and omits any column whose sum is less than $\Delta/c$. Our multi-scale sampling scheme solves this problem with cost $\tilde{O}(n/\Delta)$, while traditional sampling algorithms would take time $\Theta((n/\Delta)^2)$. Our second main technical contribution is a new local algorithm for approximating personalized PageRank, which is more robust than the earlier ones developed in \cite{JehW03,AndersenCL06} and is highly efficient particularly for networks with large in-degrees or out-degrees. Together with our multiscale sampling scheme we are able to optimally solve the {\sc SignificantPageRanks} problem.


Decentralized Multi-agent Plan Repair in Dynamic Environments

Abstract: Achieving joint objectives by teams of cooperative planning agents requires significant coordination and communication efforts. For a single-agent system facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where coordination is enforced to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. The contribution of the presented paper is threefold. Firstly, we formally introduce the multi-agent plan repair problem and formally present the core hypothesis underlying our work. Secondly, we propose three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present results of experimental validation confirming the core hypothesis of the paper.


The Computational Complexity of Truthfulness in Combinatorial Auctions

Abstract: One of the fundamental questions of Algorithmic Mechanism Design is whether there exists an inherent clash between truthfulness and computational tractability: in particular, whether polynomial-time truthful mechanisms for combinatorial auctions are provably weaker in terms of approximation ratio than non-truthful ones. This question was very recently answered for universally truthful mechanisms for combinatorial auctions \cite{D11}, and even for truthful-in-expectation mechanisms \cite{DughmiV11}. However, both of these results are based on information-theoretic arguments for valuations given by a value oracle, and leave open the possibility of polynomial-time truthful mechanisms for succinctly described classes of valuations. This paper is the first to prove {\em computational hardness} results for truthful mechanisms for combinatorial auctions with succinctly described valuations. We prove that there is a class of succinctly represented submodular valuations for which no deterministic truthful mechanism provides an $m^{1/2-\epsilon}$-approximation for a constant $\epsilon>0$, unless $NP=RP$ ($m$ denotes the number of items). Furthermore, we prove that even truthful-in-expectation mechanisms cannot approximate combinatorial auctions with certain succinctly described submodular valuations better than within $n^\gamma$, where $n$ is the number of bidders and $\gamma>0$ some absolute constant, unless $NP \subseteq P/poly$. In addition, we prove computational hardness results for two related problems.


On the Hardness of Welfare Maximization in Combinatorial Auctions with Submodular Valuations

Abstract: We present a new type of monotone submodular functions: \emph{multi-peak submodular functions}. Roughly speaking, given a family of sets $\cF$, we construct a monotone submodular function $f$ with a high value $f(S)$ for every set $S \in {\cF}$ (a "peak"), and a low value on every set that does not intersect significantly any set in $\cF$. We use this construction to show that a better than $(1-\frac{1}{2e})$-approximation ($\simeq 0.816$) for welfare maximization in combinatorial auctions with submodular valuations is (1) impossible in the communication model, (2) NP-hard in the computational model where valuations are given explicitly. Establishing a constant approximation hardness for this problem in the communication model was a long-standing open question. The valuations we construct for the hardness result in the computational model depend only on a constant number of items, and hence the result holds even if the players can answer arbitrary queries about their valuation, including demand queries. We also study two other related problems that received some attention recently: max-min allocation (for which we also get hardness of $(1-\frac 1 {2e}+\epsilon)$-approximation, in both models), and combinatorial public projects (for which we prove hardness of $(3/4+\epsilon)$-approximation in the communication model, and hardness of $(1 -\frac 1 e+\epsilon)$-approximation in the computational model, using constant size valuations).


On Approximating String Selection Problems with Outliers

Abstract: Many problems in bioinformatics are about finding strings that approximately represent a collection of given strings. We look at more general problems where some input strings can be classified as outliers. The Close to Most Strings problem is, given a set S of same-length strings, and a parameter d, find a string x that maximizes the number of "non-outliers" within Hamming distance d of x. We prove this problem has no PTAS unless ZPP=NP, correcting a decade-old mistake. The Most Strings with Few Bad Columns problem is to find a maximum-size subset of input strings so that the number of non-identical positions is at most k; we show it has no PTAS unless P=NP. We also observe Closest to k Strings has no EPTAS unless W[1]=FPT. In sum, outliers help model problems associated with using biological data, but we show the problem of finding an approximate solution is computationally difficult.


Geometric Pricing: How Low Dimensionality Helps in Approximability

Abstract: Consider the following toy problem. There are $m$ rectangles and $n$ points on the plane. Each rectangle $R$ is a consumer with budget $B_R$, who is interested in purchasing the cheapest item (point) inside R, given that she has enough budget. Our job is to price the items to maximize the revenue. This problem can also be defined on higher dimensions. We call this problem the geometric pricing problem. In this paper, we study a new class of problems arising from a geometric aspect of the pricing problem. It intuitively captures typical real-world assumptions that have been widely studied in marketing research, healthcare economics, etc. It also helps classify other well-known pricing problems, such as the highway pricing problem and the graph vertex pricing problem on planar and bipartite graphs. Moreover, this problem turns out to have close connections to other natural geometric problems such as the geometric versions of the unique coverage and maximum feasible subsystem problems. We show that the low dimensionality arising in this pricing problem does lead to improved approximation ratios, by presenting sublinear-approximation algorithms for two central versions of the problem: unit-demand uniform-budget min-buying and single-minded pricing problems. Our algorithm is obtained by combining algorithmic pricing and geometric techniques. These results suggest that considering geometric aspect might be a promising research direction in obtaining improved approximation algorithms for such pricing problems. To the best of our knowledge, this is one of very few problems in the intersection between geometry and algorithmic pricing areas. Thus its study may lead to new algorithmic techniques that could benefit both areas.


Visual definition of procedures for automatic virtual scene generation

Abstract: With more and more digital media, especially in the field of virtual reality where detailed and convincing scenes are much required, procedural scene generation is a big helping tool for artists. A problem is that defining scene descriptions through these procedures usually requires a knowledge in formal language grammars, programming theory and manually editing textual files using a strict syntax, making it less intuitive to use. Luckily, graphical user interfaces has made a lot of tasks on computers easier to perform and out of the belief that creating computer programs can also be one of them, visual programming languages (VPLs) have emerged. The goal in VPLs is to shift more work from the programmer to the integrated development environment (IDE), making programming an user-friendlier task. In this thesis, an approach of using a VPL for defining procedures that automatically generate virtual scenes is presented. The methods required to build a VPL are presented, including a novel method of generating readable code in a structured programming language. Also, the methods for achieving basic principles of VPLs will be shown -- suitable visual presentation of information and guiding the programmer in the right direction using constraints. On the other hand, procedural generation methods are presented in the context of visual programming -- adapting the application programming interface (API) of these methods to better serve the user. The main focus will be on the methods for urban modeling, such as building, city layout and details generation with random number generation used to create non-deterministic scenes.


Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms

Abstract: We reconsider the well-studied Selfish Routing game with affine latency functions. The Price of Anarchy for this class of games takes maximum value 4/3; this maximum is attained already for a simple network of two parallel links, known as Pigou's network. We improve upon the value 4/3 by means of Coordination Mechanisms. We increase the latency functions of the edges in the network, i.e., if $\ell_e(x)$ is the latency function of an edge $e$, we replace it by $\hat{\ell}_e(x)$ with $\ell_e(x) \le \hat{\ell}_e(x)$ for all $x$. Then an adversary fixes a demand rate as input. The engineered Price of Anarchy of the mechanism is defined as the worst-case ratio of the Nash social cost in the modified network over the optimal social cost in the original network. Formally, if $\CM(r)$ denotes the cost of the worst Nash flow in the modified network for rate $r$ and $\Copt(r)$ denotes the cost of the optimal flow in the original network for the same rate then [\ePoA = \max_{r \ge 0} \frac{\CM(r)}{\Copt(r)}.] We first exhibit a simple coordination mechanism that achieves for any network of parallel links an engineered Price of Anarchy strictly less than 4/3. For the case of two parallel links our basic mechanism gives 5/4 = 1.25. Then, for the case of two parallel links, we describe an optimal mechanism; its engineered Price of Anarchy lies between 1.191 and 1.192.


Approximate Recall Confidence Intervals

Abstract: Recall, the proportion of relevant documents retrieved, is an important measure of effectiveness in information retrieval, particularly in the legal, patent, and medical domains. Where document sets are too large for exhaustive relevance assessment, recall can be estimated by assessing a random sample of documents; but an indication of the reliability of this estimate is also required. In this article, we examine several methods for estimating two-tailed recall confidence intervals. We find that the normal approximation in current use provides poor coverage in many circumstances, even when adjusted to correct its inappropriate symmetry. Analytic and Bayesian methods based on the ratio of binomials are generally more accurate, but are inaccurate on small populations. The method we recommend derives beta-binomial posteriors on retrieved and unretrieved yield, with fixed hyperparameters, and a Monte Carlo estimate of the posterior distribution of recall. We demonstrate that this method gives mean coverage at or near the nominal level, across several scenarios, while being balanced and stable. We offer advice on sampling design, including the allocation of assessments to the retrieved and unretrieved segments, and compare the proposed beta-binomial with the officially reported normal intervals for recent TREC Legal Track iterations.


Exploiting the `Web of Trust' to improve efficiency in collaborative networks

Abstract: Maintaining high quality content is one of the foremost objectives of any web-based collaborative service that depends on a large number of users. In such systems, it is nearly impossible for automated scripts to judge semantics as it is to expect all editors to review the content. This catalyzes the need for trust-based mechanisms to ensure quality of an article immediately after an edit. In this paper, we build on previous work and develop a framework based on the `web of trust' concept to calculate satisfaction scores for all users without the need for perusing the article. We derive some bounds for systems based on our mechanism and show that the optimization problem of selecting the best users to review an article is NP-Hard. Extensive simulations validate our model and results, and show that trust-based mechanisms are essential to improve efficiency in any online collaborative editing platform.


On Effectiveness of Backlog Bounds Using Stochastic Network Calculus in 802.11

Abstract: Network calculus is a powerful methodology of characterizing queueing processes and has wide applications, but few works on applying it to 802.11 by far. In this paper, we take one of the first steps to analyze the backlog bounds of an 802.11 wireless LAN using stochastic network calculus. In particular, we want to address its effectiveness on bounding backlogs. We model a wireless node as a single server with impairment service based on two best-known models in stochastic network calculus: Jiang's and Ciucu's. Interestingly, we find that the two models can derive equivalent stochastic service curves and backlog bounds in our studied case. We prove that the network-calculus bounds imply stable backlogs as long as the average rate of traffic arrival is less than that of service, indicating the theoretical effectiveness of stochastic network calculus in bounding backlogs. From A. Kumar's 802.11 model, we derive the concrete stochastic service curve of an 802.11 node and its backlog bounds. We compare the derived bounds with ns-2 simulations and find that the former are very loose and we discuss the reasons. And we show that the martingale and independent case analysis techniques can improve the bounds significantly. Our work offers a good reference to applying stochastic network calculus to practical scenarios.


Parametric Compositional Data Types

Abstract: In previous work we have illustrated the benefits that compositional data types (CDTs) offer for implementing languages and in general for dealing with abstract syntax trees (ASTs). Based on Swierstra's data types \'a la carte, CDTs are implemented as a Haskell library that enables the definition of recursive data types and functions on them in a modular and extendable fashion. Although CDTs provide a powerful tool for analysing and manipulating ASTs, they lack a convenient representation of variable binders. In this paper we remedy this deficiency by combining the framework of CDTs with Chlipala's parametric higher-order abstract syntax (PHOAS). We show how a generalisation from functors to difunctors enables us to capture PHOAS while still maintaining the features of the original implementation of CDTs, in particular its modularity. Unlike previous approaches, we avoid so-called exotic terms without resorting to abstract types: this is crucial when we want to perform transformations on CDTs that inspect the recursively computed CDTs, e.g. constant folding.


Step-Indexed Normalization for a Language with General Recursion

Abstract: The Trellys project has produced several designs for practical dependently typed languages. These languages are broken into two fragments-a_logical_fragment where every term normalizes and which is consistent when interpreted as a logic, and a_programmatic_fragment with general recursion and other convenient but unsound features. In this paper, we present a small example language in this style. Our design allows the programmer to explicitly mention and pass information between the two fragments. We show that this feature substantially complicates the metatheory and present a new technique, combining the traditional Girard-Tait method with step-indexed logical relations, which we use to show normalization for the logical fragment.


An Investigation of the Laws of Traversals

Abstract: Traversals of data structures are ubiquitous in programming. Consequently, it is important to be able to characterise those structures that are traversable and understand their algebraic properties. Traversable functors have been characterised by McBride and Paterson as those equipped with a distributive law over arbitrary applicative functors; however, laws that fully capture the intuition behind traversals are missing. This article is an attempt to remedy this situation by proposing laws for characterising traversals that capture the intuition behind them. To support our claims, we prove that finitary containers are traversable in our sense and argue that elements in a traversable structure are visited exactly once.


A Formal Comparison of Approaches to Datatype-Generic Programming

Abstract: Datatype-generic programming increases program abstraction and reuse by making functions operate uniformly across different types. Many approaches to generic programming have been proposed over the years, most of them for Haskell, but recently also for dependently typed languages such as Agda. Different approaches vary in expressiveness, ease of use, and implementation techniques. Some work has been done in comparing the different approaches informally. However, to our knowledge there have been no attempts to formally prove relations between different approaches. We thus present a formal comparison of generic programming libraries. We show how to formalise different approaches in Agda, including a coinductive representation, and then establish theorems that relate the approaches to each other. We provide constructive proofs of inclusion of one approach in another that can be used to convert between approaches, helping to reduce code duplication across different libraries. Our formalisation also helps in providing a clear picture of the potential of each approach, especially in relating different generic views and their expressiveness.


Evaluation strategies for monadic computations

Abstract: Monads have become a powerful tool for structuring effectful computations in functional programming, because they make the order of effects explicit. When translating pure code to a monadic version, we need to specify evaluation order explicitly. Two standard translations give call-by-value and call-by-name semantics. The resulting programs have different structure and types, which makes revisiting the choice difficult. In this paper, we translate pure code to monadic using an additional operation malias that abstracts out the evaluation strategy. The malias operation is based on computational comonads; we use a categorical framework to specify the laws that are required to hold about the operation. For any monad, we show implementations of malias that give call-by-value and call-by-name semantics. Although we do not give call-by-need semantics for all monads, we show how to turn certain monads into an extended monad with call-by-need semantics, which partly answers an open question. Moreover, using our unified translation, it is possible to change the evaluation strategy of functional code translated to the monadic form without changing its structure or types.


Tracing monadic computations and representing effects

Abstract: In functional programming, monads are supposed to encapsulate computations, effectfully producing the final result, but keeping to themselves the means of acquiring it. For various reasons, we sometimes want to reveal the internals of a computation. To make that possible, in this paper we introduce monad transformers that add the ability to automatically accumulate observations about the course of execution as an effect. We discover that if we treat the resulting trace as the actual result of the computation, we can find new functionality in existing monads, notably when working with non-terminating computations.


Irrelevance, Heterogeneous Equality, and Call-by-value Dependent Type Systems

Abstract: We present a full-spectrum dependently typed core language which includes both nontermination and computational irrelevance (a.k.a. erasure), a combination which has not been studied before. The two features interact: to protect type safety we must be careful to only erase terminating expressions. Our language design is strongly influenced by the choice of CBV evaluation, and by our novel treatment of propositional equality which has a heterogeneous, completely erased elimination form.


From Mathematics to Abstract Machine: A formal derivation of an executable Krivine machine

Abstract: This paper presents the derivation of an executable Krivine abstract machine from a small step interpreter for the simply typed lambda calculus in the dependently typed programming language Agda.


Detection of Calendar-Based Periodicities of Interval-Based Temporal Patterns

Abstract: We present a novel technique to identify calendar-based (annual, monthly and daily) periodicities of an interval-based temporal pattern. An interval-based temporal pattern is a pattern that occurs across a time-interval, then disappears for some time, again recurs across another time-interval and so on and so forth. Given the sequence of time-intervals in which an interval-based temporal pattern has occurred, we propose a method for identifying the extent to which the pattern is periodic with respect to a calendar cycle. In comparison to previous work, our method is asymptotically faster. We also show an interesting relationship between periodicities across different levels of any hierarchical timestamp (year/month/day, hour/minute/second etc.).


Theoretical Analysis and Tuning of Decentralized Probabilistic Auto-Scaling

Abstract: A major impediment towards the industrial adoption of decentralized distributed systems comes from the difficulty to theoretically prove that these systems exhibit the required behavior. In this paper, we use probability theory to analyze a decentralized auto-scaling algorithm in which each node probabilistically decides to scale in or out. We prove that, in the context of dynamic workloads, the average load of the system is maintained within a variation interval with a given probability, provided that the number of nodes and the variation interval length are higher than certain bounds. The paper also proposes numerical algorithms for approximating these minimum bounds.


Using TV Receiver Information to Increase Cognitive White Space Spectrum

Abstract: In this paper we investigate the usage of cognitive radio devices within the service area of TV broadcast stations. Until now the main approach for a cognitive radio to operate in the TV bands has been to register TV broadcast stations locations and thus protecting the broadcast stations service area. Through information about TV receivers location, we show that a cognitive radio should be able to operate within this service area without causing harmful interference to the TV receivers as defined by Ofcom and FCC. We provide simulations based on real statistics from Norway that show that especially in rural areas TV receiver registration can provide a substantial gain in terms of exploitable frequencies for a cognitive radio.


No-reference image quality assessment through the von Mises distribution

Abstract: An innovative way of calculating the von Mises distribution (VMD) of image entropy is introduced in this paper. The VMD's concentration parameter and some fitness parameter that will be later defined, have been analyzed in the experimental part for determining their suitability as a image quality assessment measure in some particular distortions such as Gaussian blur or additive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropy is calculated in four equally spaced orientations and used to determine the parameters of the von Mises distribution of the image entropy. Considering contextual images, experimental results after applying this model show that the best-in-focus noise-free images are associated with the highest values for the von Mises distribution concentration parameter and the highest approximation of image data to the von Mises distribution model. Our defined von Misses fitness parameter experimentally appears also as a suitable no-reference image quality assessment indicator for no-contextual images.


Segmentation of Offline Handwritten Bengali Script

Abstract: Character segmentation has long been one of the most critical areas of optical character recognition process. Through this operation, an image of a sequence of characters, which may be connected in some cases, is decomposed into sub-images of individual alphabetic symbols. In this paper, segmentation of cursive handwritten script of world's fourth popular language, Bengali, is considered. Unlike English script, Bengali handwritten characters and its components often encircle the main character, making the conventional segmentation methodologies inapplicable. Experimental results, using the proposed segmentation technique, on sample cursive handwritten data containing 218 ideal segmentation points show a success rate of 97.7%. Further feature-analysis on these segments may lead to actual recognition of handwritten cursive Bengali script.


Design and Fabrication of Micromachined Resonators

Abstract: Microelectromechanical system (MEMS) based on-chip resonators offer great potential for sensing and high frequency signal processing applications due to their exceptional features like small size, large frequency-quality factor product, integrability with CMOS ICs, low power consumption etc. This work is mainly aimed at the design, modeling, simulation, and fabrication of micromachined polysilicon disk resonators exhibiting radial-contour mode vibrations. A few other bulk mode modified resonator geometries are also being explored. The resonator structures have been designed and simulated in CoventorWare finite-element platform and fabricated by the PolyMUMPs surface micromachining process.


A New Approach to Practical Active-Secure Two-Party Computation

Abstract: We propose a new approach to practical two-party computation secure against an active adversary. All prior practical protocols were based on Yao's garbled circuits. We use an OT-based approach and get efficiency via OT extension in the random oracle model. To get a practical protocol we introduce a number of novel techniques for relating the outputs and inputs of OTs in a larger construction. We also report on an implementation of this approach, that shows that our protocol is more efficient than any previous one: For big enough circuits, we can evaluate more than 20000 Boolean gates per second. As an example, evaluating one oblivious AES encryption (~34000 gates) takes 64 seconds, but when repeating the task 27 times it only takes less than 3 seconds per instance.


On Dynamic Distributed Computing

Abstract: This paper shows for the first time that distributed computing can be both reliable and efficient in an environment that is both highly dynamic and hostile. More specifically, we show how to maintain clusters of size $O(\log N)$, each containing more than two thirds of honest nodes with high probability, within a system whose size can vary \textit{polynomially} with respect to its initial size. Furthermore, the communication cost induced by each node arrival or departure is polylogarithmic with respect to $N$, the maximal size of the system. Our clustering can be achieved despite the presence of a Byzantine adversary controlling a fraction $\bad \leq \{1}{3}-\epsilon$ of the nodes, for some fixed constant $\epsilon > 0$, independent of $N$. So far, such a clustering could only be performed for systems who size can vary constantly and it was not clear whether that was at all possible for polynomial variances.


Computing Resolution-Path Dependencies in Linear Time

Abstract: The alternation of existential and universal quantifiers in a quantified boolean formula (QBF) generates dependencies among variables that must be respected when evaluating the formula. Dependency schemes provide a general framework for representing such dependencies. Since it is generally intractable to determine dependencies exactly, a set of potential dependencies is computed instead, which may include false positives. Among the schemes proposed so far, resolution-path dependencies introduce the fewest spurious dependencies. In this work, we describe an algorithm that detects resolution-path dependencies in linear time, resolving a problem posed by Van Gelder (CP 2011).


D-iteration based asynchronous distributed computation

Abstract: The aim of this paper is to explain how the D-iteration can be used for an efficient asynchronous distributed computation. We present the main ideas of the method and illustrate them through very simple examples.


Scientific impact evaluation and the effect of self-citations: mitigating the bias by discounting h-index

Abstract: In this paper, we propose a measure to assess scientific impact that discounts self-citations and does not require any prior knowledge on the their distribution among publications. This index can be applied to both researchers and journals. In particular, we show that it fills the gap of h-index and similar measures that do not take into account the effect of self-citations for authors or journals impact evaluation. The paper provides with two real-world examples: in the former, we evaluate the research impact of the most productive scholars in Computer Science (according to DBLP); in the latter, we revisit the impact of the journals ranked in the 'Computer Science Applications' section of SCImago. We observe how self-citations, in many cases, affect the rankings obtained according to different measures (including h-index and ch-index), and show how the proposed measure mitigates this effect.


Proceedings 8th Workshop on Fixed Points in Computer Science

Abstract: This volume contains the proceedings of the Eighth Workshop on Fixed Points in Computer Science which took place on 24 March 2012 in Tallinn, Estonia as an ETAPS-affiliated workshop. Past workshops have been held in Brno (1998, MFCS/CSL workshop), Paris (2000, LC workshop), Florence (2001, PLI workshop), Copenhagen (2002, LICS (FLoC) workshop), Warsaw (2003, ETAPS workshop), Coimbra (2009, CSL workshop), and Brno (2010, MFCS-CSL workshop). Fixed points play a fundamental role in several areas of computer science and logic by justifying induction and recursive definitions. The construction and properties of fixed points have been investigated in many different frameworks such as: design and implementation of programming languages, program logics, and databases. The aim of this workshop is to provide a forum for researchers to present their results to those members of the computer science and logic communities who study or apply the theory of fixed points.


Randomization Resilient To Sensitive Reconstruction

Abstract: With the randomization approach, sensitive data items of records are randomized to protect privacy of individuals while allowing the distribution information to be reconstructed for data analysis. In this paper, we distinguish between reconstruction that has potential privacy risk, called micro reconstruction, and reconstruction that does not, called aggregate reconstruction. We show that the former could disclose sensitive information about a target individual, whereas the latter is more useful for data analysis than for privacy breaches. To limit the privacy risk of micro reconstruction, we propose a privacy definition, called (epsilon,delta)-reconstruction-privacy. Intuitively, this privacy notion requires that micro reconstruction has a large error with a large probability. The promise of this approach is that micro reconstruction is more sensitive to the number of independent trials in the randomization process than aggregate reconstruction is; therefore, reducing the number of independent trials helps achieve (epsilon,delta)-reconstruction-privacy while preserving the accuracy of aggregate reconstruction. We present an algorithm based on this idea and evaluate the effectiveness of this approach using real life data sets.