Derivatives of Approximate Regular Expressions

Abstract: Our aim is to construct a finite automaton recognizing the set of words that are at a bounded distance from some word of a given regular language. We define new regular operators, the similarity operators, based on a generalization of the notion of distance and we introduce the family of regular expressions extended to similarity operators, that we call AREs (Approximate Regular Expressions). We set formulae to compute the Brzozowski derivatives and the Antimirov derivatives of an ARE, which allows us to give a solution to the ARE membership problem and to provide the construction of two recognizers for the language denoted by an ARE. As far as we know, the family of approximative regular expressions is introduced for the first time in this paper. Classical approximate regular expression matching algorithms are approximate matching algorithms on regular expressions. Our approach is rather to process an exact matching on approximate regular expressions.


Goal Directed Relative Skyline Queries in Time Dependent Road Networks

Abstract: The Wireless GIS technology is progressing rapidly in the area of mobile communications. Location-based spatial queries are becoming an integral part of many new mobile applications. The Skyline queries are latest apps under Location-based services. In this paper we introduce Goal Directed Relative Skyline queries on Time dependent (GD-RST) road networks. The algorithm uses travel time as a metric in finding the data object by considering multiple query points (multi-source skyline) relative to user location and in the user direction of travelling. We design an efficient algorithm based on Filter phase, Heap phase and Refine Skyline phases. At the end, we propose a dynamic skyline caching (DSC) mechanism which helps to reduce the computation cost for future skyline queries. The experimental evaluation reflects the performance of GD-RST algorithm over the traditional branch and bound algorithm for skyline queries in real road networks.


Text Steganography using LSB insertion method along with Chaos Theory

Abstract: The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.


Wishbone bus Architecture - A Survey and Comparison

Abstract: The performance of an on-chip interconnection architecture used for communication between IP cores depends on the efficiency of its bus architecture. Any bus architecture having advantages of faster bus clock speed, extra data transfer cycle, improved bus width and throughput is highly desirable for a low cost, reduced time-to-market and efficient System-on-Chip (SoC). This paper presents a survey of WISHBONE bus architecture and its comparison with three other on-chip bus architectures viz. Advanced Micro controller Bus Architecture (AMBA) by ARM, CoreConnect by IBM and Avalon by Altera. The WISHBONE Bus Architecture by Silicore Corporation appears to be gaining an upper edge over the other three bus architecture types because of its special performance parameters like the use of flexible arbitration scheme and additional data transfer cycle (Read-Modify-Write cycle). Moreover, its IP Cores are available free for use requiring neither any registration nor any agreement or license.


Microcontroller Based Testing of Digital IP-Core

Abstract: Testing core based System on Chip is a challenge for the test engineers. To test the complete SOC at one time with maximum fault coverage, test engineers prefer to test each IP-core separately. At speed testing using external testers is more expensive because of gigahertz processor. The purpose of this paper is to develop cost efficient and flexible test methodology for testing digital IP-cores . The prominent feature of the approach is to use microcontroller to test IP-core. The novel feature is that there is no need of test pattern generator and output response analyzer as microcontroller performs the function of both. This approach has various advantages such as at speed testing, low cost, less area overhead and greater flexibility since most of the testing process is based on software.


Improving Network Performance with Affinity based Mobility Model in Opportunistic Network

Abstract: Opportunistic network is a type of Delay Tolerant Network which is characterized by intermittent connectivity amongst the nodes and communication largely depends upon the mobility of the participating nodes. The network being highly dynamic, traditional MANET protocols cannot be applied and the nodes must adhere to store-carry-forward mechanism. Nodes do not have the information about the network topology, number of participating nodes and the location of the destination node. Hence, message transfer reliability largely depends upon the mobility pattern of the nodes. In this paper we have tried to find the impact of RWP (Random Waypoint) mobility on packet delivery ratio. We estimate mobility factors like number of node encounters, contact duration(link time) and inter-contact time which in turn depends upon parameters like playfield area (total network area), number of nodes, node velocity, bit-rate and RF range of the nodes. We also propose a restricted form of RWP mobility model, called the affinity based mobility model. The network scenario consists of a source and a destination node that are located at two extreme corners of the square playfield (to keep a maximum distance between them) and exchange data packets with the aid of mobile 'helper' nodes. The source node and the destination node are static. The mobile nodes only help in relaying the message. We prove how affinity based mobility model helps in augmenting the network reliability thereby increasing the message delivery ratio and reduce message delivery latency.


Design Space Exploration to Find the Optimum Cache and Register File Size for Embedded Applications

Abstract: In the future, embedded processors must process more computation-intensive network applications and internet traffic and packet-processing tasks become heavier and sophisticated. Since the processor performance is severely related to the average memory access delay and also the number of processor registers affects the performance, cache and register file are two major parts in designing embedded processor architecture. Although increasing cache and register file size leads to performance improvement in embedded applications and packet-processing tasks in high traffic networks with too much packets, the increased area, power consumption and memory hierarchy delay are the overheads of these techniques. Therefore, implementing these components in the optimum size is of significant interest in the design of embedded processors. This paper explores the effect of cache and register file size on the processor performance to calculate the optimum size of these components for embedded applications. Experimental results show that although having bigger cache and register file is one of the performance improvement approaches in embedded processors, however, by increasing the size of these parameters over a threshold level, performance improvement is saturated and then, decreased.


Structured Grammars are Effective

Abstract: Top-down parsing has received much attention recently. Parsing expression grammars (PEG) allows construction of linear time parsers using packrat algorithm. These techniques however suffer from problem of prefix hiding. We use alternative formalism of relativized regular expressions REGREG for which top-down backtracking parser runs in linear time. This formalism allows to construct fast parsers with modest memory requirements for practical grammars. We show that our formalism is equivalent to PEG.


The 1-Center and 1-Highway problem

Abstract: We study a variation of the 1-center problem in which, in addition to a single supply facility, we are allowed to locate a highway. This highway increases the transportation speed between any demand point and the facility. That is, given a set $S$ of points and $v>1$, we are interested in locating the facility point $f$ and the highway $h$ that minimize the expression $\max_{p\in S}d_{h}(p,f)$, where $d_h$ is the time needed to travel between $p$ and $f$. We consider two types of highways ({\em freeways} and {\em turnpikes}) and study the cases in which the highway's length is fixed by the user (or can be modified to further decrease the transportation time).


A low power high bandwidth four quadrant analog multiplier in 32 nm CNFET technology

Abstract: Carbon Nanotube Field Effect Transistor (CNFET) is a promising new technology that overcomes several limitations of traditional silicon integrated circuit technology. In recent years, the potential of CNFET for analog circuit applications has been explored. This paper proposes a novel four quadrant analog multiplier design using CNFETs. The simulation based on 32nm CNFET technology shows that the proposed multiplier has very low harmonic distortion (<0.45%), large input range ({\pm}400mV), large bandwidth (~50GHz) and low power consumption (~247{\mu}W), while operating at a supply voltage of {\pm}0.9V.


Strong Conflict-Free Coloring of Intervals

Abstract: We consider the k-strong conflict-free coloring of a set of points on a line with respect to a family of intervals: Each point on the line must be assigned a color so that the coloring has to be conflict-free, in the sense that in every interval I there are at least k colors each appearing exactly once in I. In this paper, we present a polynomial algorithm for the general problem; the algorithm has an approximation factor 5-2/k when k\geq2 and approximation factor 2 for k=1. In the special case the family contains all the possible intervals on the given set of points, we show that a 2 approximation algorithm exists, for any k\geq1.


Factors affecting acceptance of web-based training system: Using extended UTAUT and structural equation modeling

Abstract: Advancement in information system leads organizations to apply e-learning system to train their employees in order to enhance its performance. In this respect, applying web based training will enable the organization to train their employees quickly, efficiently and effectively anywhere at any time. This research aims to extend Unified Theory of Acceptance and Use Technology (UTAUT) using some factors such flexibility of web based training system, system interactivity and system enjoyment, in order to explain the employees' intention to use web based training system. A total of 290 employees have participated in this study. The findings of the study revealed that performance expectancy, facilitating conditions, social influence and system flexibility have direct effect on the employees' intention to use web based training system, while effort expectancy, system enjoyment and system interactivity have indirect effect on employees' intention to use the system.


Using data mining techniques for diagnosis and prognosis of cancer disease

Abstract: Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis also we focus on current research being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.


Distributed Algorithms for Scheduling on Line and Tree Networks

Abstract: We have a set of processors (or agents) and a set of graph networks defined over some vertex set. Each processor can access a subset of the graph networks. Each processor has a demand specified as a pair of vertices $<u, v>$, along with a profit; the processor wishes to send data between $u$ and $v$. Towards that goal, the processor needs to select a graph network accessible to it and a path connecting $u$ and $v$ within the selected network. The processor requires exclusive access to the chosen path, in order to route the data. Thus, the processors are competing for routes/channels. A feasible solution selects a subset of demands and schedules each selected demand on a graph network accessible to the processor owning the demand; the solution also specifies the paths to use for this purpose. The requirement is that for any two demands scheduled on the same graph network, their chosen paths must be edge disjoint. The goal is to output a solution having the maximum aggregate profit. Prior work has addressed the above problem in a distibuted setting for the special case where all the graph networks are simply paths (i.e, line-networks). Distributed constant factor approximation algorithms are known for this case. The main contributions of this paper are twofold. First we design a distributed constant factor approximation algorithm for the more general case of tree-networks. The core component of our algorithm is a tree-decomposition technique, which may be of independent interest. Secondly, for the case of line-networks, we improve the known approximation guarantees by a factor of 5. Our algorithms can also handle the capacitated scenario, wherein the demands and edges have bandwidth requirements and capacities, respectively.


Dynamic Grouping of Web Users Based on Their Web Access Patterns using ART1 Neural Network Clustering Algorithm

Abstract: In this paper, we propose ART1 neural network clustering algorithm to group users according to their Web access patterns. We compare the quality of clustering of our ART1 based clustering technique with that of the K-Means and SOM clustering algorithms in terms of inter-cluster and intra-cluster distances. The results show the average inter-cluster distance of ART1 is high compared to K-Means and SOM when there are fewer clusters. As the number of clusters increases, average inter-cluster distance of ART1 is low compared to K-Means and SOM which indicates the high quality of clusters formed by our approach.


A Note on the PageRank of Undirected Graphs

Abstract: The PageRank is a widely used scoring function of networks in general and of the World Wide Web graph in particular. The PageRank is defined for directed graphs, but in some special cases applications for undirected graphs occur. In the literature it is widely noted that the PageRank for undirected graphs are proportional to the degrees of the vertices of the graph. We prove that statement for a particular personalization vector in the definition of the PageRank, and we also show that in general, the PageRank of an undirected graph is not exactly proportional to the degree distribution of the graph: our main theorem gives an upper and a lower bound to the L_1 norm of the difference of the PageRank and the degree distribution vectors.


Expressivity of Time-Varying Graphs and the Power of Waiting in Dynamic Networks

Abstract: In infrastructure-less highly dynamic networks, computing and performing even basic tasks (such as routing and broadcasting) is a very challenging activity due to the fact that connectivity does not necessarily hold, and the network may actually be disconnected at every time instant. Clearly the task of designing protocols for these networks is less difficult if the environment allows waiting (i.e., it provides the nodes with store-carry-forward-like mechanisms such as local buffering) than if waiting is not feasible. No quantitative corroborations of this fact exist (e.g., no answer to the question: how much easier?). In this paper, we consider these qualitative questions about dynamic networks, modeled as time-varying (or evolving) graphs, where edges exist only at some times. We examine the difficulty of the environment in terms of the expressivity of the corresponding time-varying graph; that is in terms of the language generated by the feasible journeys in the graph. We prove that the set of languages $L_{nowait}$ when no waiting is allowed contains all computable languages. On the other end, using algebraic properties of quasi-orders, we prove that $L_{wait}$ is just the family of regular languages. In other words, we prove that, when waiting is no longer forbidden, the power of the accepting automaton (difficulty of the environment) drops drastically from being as powerful as a Turing machine, to becoming that of a Finite-State machine. This (perhaps surprisingly large) gap is a measure of the computational power of waiting. We also study bounded waiting; that is when waiting is allowed at a node only for at most $d$ time units. We prove the negative result that $L_{wait[d]} = L_{nowait}$; that is, the expressivity decreases only if the waiting is finite but unpredictable (i.e., under the control of the protocol designer and not of the environment).


Fast Optimal Joint Tracking-Registration for Multi-Sensor Systems

Abstract: Sensor fusion of multiple sources plays an important role in vehicular systems to achieve refined target position and velocity estimates. In this article, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration-tracking (JRT) is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cram\`{e}r-Rao lower bound theory is presented to show asymptotical optimality of FMAP. Also, Givens rotation is used to derive a fast implementation with complexity O(n) with $n$ the number of tracked targets. Simulations and experiments are presented to demonstrate the promise and effectiveness of FMAP.


Mixed-mode implementation of PETSc for scalable linear algebra on multi-core processors

Abstract: With multi-core processors a ubiquitous building block of modern supercomputers, it is now past time to enable applications to embrace these developments in processor design. To achieve exascale performance, applications will need ways of exploiting the new levels of parallelism that are exposed in modern high-performance computers. A typical approach to this is to use shared-memory programming techniques to best exploit multi-core nodes along with inter-node message passing. In this paper, we describe the addition of OpenMP threaded functionality to the PETSc library. We highlight some issues that hinder good performance of threaded applications on modern processors and describe how to negate them. The OpenMP branch of PETSc was benchmarked using matrices extracted from Fluidity, a CFD application code, which uses the library as its linear solver engine. The overall performance of the mixed-mode implementation is shown to be superior to that of the pure-MPI version.


Deploying SIP-based Mobile Exam Application onto Next Generation Network testbed

Abstract: Over the past few years, mobile operators are faced with enormous challenges. Of such challenges, evolved user demands on personalized applications. Telecommunications industry as well as research community have paid enormous attention to Next Generation Networks (NGN) to address this challenge. NGN is perceived as a sophisticated platform where both application developers and mobile operators cooperate to develop user applications with enhanced quality of experience. The objective of this paper is twofold: first we present an introduction to state-of-the-art NGN testbed to be developed at KAU, and second we provide initial analysis for deploying a mobile application on top of the testbed.


M-FISH Karyotyping - A New Approach Based on Watershed Transform

Abstract: Karyotyping is a process in which chromosomes in a dividing cell are properly stained, identified and displayed in a standard format, which helps geneticist to study and diagnose genetic factors behind various genetic diseases and for studying cancer. M-FISH (Multiplex Fluorescent In-Situ Hybridization) provides color karyotyping. In this paper, an automated method for M-FISH chromosome segmentation based on watershed transform followed by naive Bayes classification of each region using the features, mean and standard deviation, is presented. Also, a post processing step is added to re-classify the small chromosome segments to the neighboring larger segment for reducing the chances of misclassification. The approach provided improved accuracy when compared to the pixel-by-pixel approach. The approach was tested on 40 images from the dataset and achieved an accuracy of 84.21 %.


Unfolding Prismatoids as Convex Patches: Counterexamples and Positive Results

Abstract: We address the unsolved problem of unfolding prismatoids in a new context, viewing a "topless prismatoid" as a convex patch---a polyhedral subset of the surface of a convex polyhedron homeomorphic to a disk. We show that several natural strategies for unfolding a prismatoid can fail, but obtain a positive result for "petal unfolding" topless prismatoids. We also show that the natural extension to a convex patch consisting of a face of a polyhedron and all its incident faces, does not always have a nonoverlapping petal unfolding. However, we obtain a positive result by excluding the problematical patches. This then leads a positive result for restricted prismatoids. Finally, we suggest suggest studying the unfolding of convex patches in general, and offer some possible lines of investigation.


Weak Models of Distributed Computing, with Connections to Modal Logic

Abstract: This work presents a classification of weak models of distributed computing. We focus on deterministic distributed algorithms, and study models of computing that are weaker versions of the widely-studied port-numbering model. In the port-numbering model, a node of degree d receives messages through d input ports and sends messages through d output ports, both numbered with 1,2,...,d. In this work, VVc is the class of all graph problems that can be solved in the standard port-numbering model. We study the following subclasses of VVc: VV: Input port i and output port i are not necessarily connected to the same neighbour. MV: Input ports are not numbered; algorithms receive a multiset of messages. SV: Input ports are not numbered; algorithms receive a set of messages. VB: Output ports are not numbered; algorithms send the same message to all output ports. MB: Combination of MV and VB. SB: Combination of SV and VB. Now we have many trivial containment relations, such as SB \subseteq MB \subseteq VB \subseteq VV \subseteq VVc, but it is not obvious if, for example, either of VB \subseteq SV or SV \subseteq VB should hold. Nevertheless, it turns out that we can identify a linear order on these classes. We prove that SB \subsetneq MB = VB \subsetneq SV = MV = VV \subsetneq VVc. The same holds for the constant-time versions of these classes. We also show that the constant-time variants of these classes can be characterised by a corresponding modal logic. Hence the linear order identified in this work has direct implications in the study of the expressibility of modal logic. Conversely, one can use tools from modal logic to study these classes.


Analysis of WiMAX Physical Layer Using Spatial Multiplexing

Abstract: Broadband Wireless Access (BWA) has emerged as a promising solution for providing last mile internet access technology to provide high speed internet access to the users in the residential as well as in the small and medium sized enterprise sectors. IEEE 802.16e is one of the most promising and attractive candidate among the emerging technologies for broadband wireless access. The emergence of WiMAX protocol has attracted various interests from almost all the fields of wireless communications. MIMO systems which are created according to the IEEE 802.16-2005 standard (WiMAX) under different fading channels can be implemented to get the benefits of both the MIMO and WiMAX technologies. In this paper analysis of higher level of modulations (i.e. M-PSK and M-QAM for different values of M) with different code rates and on WiMAX-MIMO system is presented for Rayleigh channel by focusing on spatial multiplexing MIMO technique. Signal-to Noise Ratio (SNR) vs Bit Error Rate (BER) analysis has been done.


High-Performance Solvers for Dense Hermitian Eigenproblems

Abstract: We introduce a new collection of solvers - subsequently called EleMRRR - for large-scale dense Hermitian eigenproblems. EleMRRR solves various types of problems: generalized, standard, and tridiagonal eigenproblems. Among these, the last is of particular importance as it is a solver on its own right, as well as the computational kernel for the first two; we present a fast and scalable tridiagonal solver based on the Algorithm of Multiple Relatively Robust Representations - referred to as PMRRR. Like the other EleMRRR solvers, PMRRR is part of the freely available Elemental library, and is designed to fully support both message-passing (MPI) and multithreading parallelism (SMP). As a result, the solvers can equally be used in pure MPI or in hybrid MPI-SMP fashion. We conducted a thorough performance study of EleMRRR and ScaLAPACK's solvers on two supercomputers. Such a study, performed with up to 8,192 cores, provides precise guidelines to assemble the fastest solver within the ScaLAPACK framework; it also indicates that EleMRRR outperforms even the fastest solvers built from ScaLAPACK's components.


Unique Parallel Decomposition in Branching and Weak Bisimulation Semantics

Abstract: We consider the property of unique parallel decomposition modulo branching and weak bisimilarity. First, we show that infinite behaviours may fail to have parallel decompositions at all. Then, we prove that totally normed behaviours always have parallel decompositions, but that these are not necessarily unique. Finally, we establish that weakly bounded behaviours have unique parallel decompositions. We derive the latter result from a general theorem about unique decompositions in partial commutative monoids.


A Converged Algorithm for Tikhonov Regularized Nonnegative Matrix Factorization with Automatic Regularization Parameters Determination

Abstract: We present a converged algorithm for Tikhonov regularized nonnegative matrix factorization (NMF). We specially choose this regularization because it is known that Tikhonov regularized least square (LS) is the more preferable form in solving linear inverse problems than the conventional LS. Because an NMF problem can be decomposed into LS subproblems, it can be expected that Tikhonov regularized NMF will be the more appropriate approach in solving NMF problems. The algorithm is derived using additive update rules which have been shown to have convergence guarantee. We equip the algorithm with a mechanism to automatically determine the regularization parameters based on the L-curve, a well-known concept in the inverse problems community, but is rather unknown in the NMF research. The introduction of this algorithm thus solves two inherent problems in Tikhonov regularized NMF algorithm research, i.e., convergence guarantee and regularization parameters determination.


Design and implementation of real time AES-128 on real time operating system for multiple FPGA communication

Abstract: Security is the most important part in data communication system, where more randomization in secret keys increases the security as well as complexity of the cryptography algorithms. As a result in recent dates these algorithms are compensating with enormous memory spaces and large execution time on hardware platform. Field programmable gate arrays (FPGAs), provide one of the major alternative in hardware platform scenario due to its reconfiguration nature, low price and marketing speed. In FPGA based embedded system we can use embedded processor to execute particular algorithm with the inclusion of a real time operating System (RTOS), where threads may reduce resource utilization and time consumption. A process in the runtime is separated in different smaller tasks which are executed by the scheduler to meet the real time dead line using RTOS. In this paper we demonstrate the design and implementation of a 128-bit Advanced Encryption Standard (AES) both symmetric key encryption and decryption algorithm by developing suitable hardware and software design on Xilinx Spartan- 3E (XC3S500E-FG320) device using an Xilkernel RTOS, the implementation has been tested successfully The system is optimized in terms of execution speed and hardware utilization.


Discrimination of English to other Indian languages (Kannada and Hindi) for OCR system

Abstract: India is a multilingual multi-script country. In every state of India there are two languages one is state local language and the other is English. For example in Andhra Pradesh, a state in India, the document may contain text words in English and Telugu script. For Optical Character Recognition (OCR) of such a bilingual document, it is necessary to identify the script before feeding the text words to the OCRs of individual scripts. In this paper, we are introducing a simple and efficient technique of script identification for Kannada, English and Hindi text words of a printed document. The proposed approach is based on the horizontal and vertical projection profile for the discrimination of the three scripts. The feature extraction is done based on the horizontal projection profile of each text words. We analysed 700 different words of Kannada, English and Hindi in order to extract the discrimination features and for the development of knowledge base. We use the horizontal projection profile of each text word and based on the horizontal projection profile we extract the appropriate features. The proposed system is tested on 100 different document images containing more than 1000 text words of each script and a classification rate of 98.25%, 99.25% and 98.87% is achieved for Kannada, English and Hindi respectively.


Collaborative search on the plane without communication

Abstract: We generalize the classical cow-path problem [7, 14, 38, 39] into a question that is relevant for collective foraging in animal groups. Specifically, we consider a setting in which k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. Our focus is on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if agents do not commence the search in synchrony then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is $\Omega$(D + D 2 /k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation. We present an almost tight bound for the competitive penalty that must be paid, in the running time, if agents have no information about k. Specifically, on the negative side, we show that in such a case, there is no algorithm whose competitiveness is O(log k). On the other hand, we show that for every constant $\epsilon \textgreater{} 0$, there exists a rather simple uniform search algorithm which is $O( \log^{1+\epsilon} k)$-competitive. In addition, we give a lower bound for the setting in which agents are given some estimation of k. As a special case, this lower bound implies that for any constant $\epsilon \textgreater{} 0$, if each agent is given a (one-sided) $k^\epsilon$-approximation to k, then the competitiveness is $\Omega$(log k). Informally, our results imply that the agents can potentially perform well without any knowledge of their total number k, however, to further improve, they must be given a relatively good approximation of k. Finally, we propose a uniform algorithm that is both efficient and extremely simple suggesting its relevance for actual biological scenarios.


P(l)aying for Synchronization

Abstract: Two topics are presented: synchronization games and synchronization costs. In a synchronization game on a deterministic finite automaton, there are two players, Alice and Bob, whose moves alternate. Alice wants to synchronize the given automaton, while Bob aims to make her task as hard as possible. We answer a few natural questions related to such games. Speaking about synchronization costs, we consider deterministic automata in which each transition has a certain price. The problem is whether or not a given automaton can be synchronized within a given budget. We determine the complexity of this problem. We also formulate a few open questions.


A Greedy Double Swap Heuristic for Nurse Scheduling

Abstract: One of the key challenges of nurse scheduling problem (NSP) is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients' demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework which is solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software.


Approximation Algorithms for Semi-random Graph Partitioning Problems

Abstract: In this paper, we propose and study a new semi-random model for graph partitioning problems. We believe that it captures many properties of real--world instances. The model is more flexible than the semi-random model of Feige and Kilian and planted random model of Bui, Chaudhuri, Leighton and Sipser. We develop a general framework for solving semi-random instances and apply it to several problems of interest. We present constant factor bi-criteria approximation algorithms for semi-random instances of the Balanced Cut, Multicut, Min Uncut, Sparsest Cut and Small Set Expansion problems. We also show how to almost recover the optimal solution if the instance satisfies an additional expanding condition. Our algorithms work in a wider range of parameters than most algorithms for previously studied random and semi-random models. Additionally, we study a new planted algebraic expander model and develop constant factor bi-criteria approximation algorithms for graph partitioning problems in this model.


Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning in Web 3.0

Abstract: Web 3.0 is an evolving extension of the current web environme bnt. Information in web 3.0 can be collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning experience to the students. Web 3.0 is 3D, media centric and semantic. Web based learning has been on high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the queries by the students. Completely Interactive learner's query determine the customization of the intelligent tutor. This paper analyses the Web 3.0 learning environment attributes. A Maximum spanning tree model for the personalized web based collaborative learning is designed.


Efficient Constrained Regret Minimization

Abstract: Online learning constitutes a mathematical and compelling framework to analyze sequential decision making problems in adversarial environments. The learner repeatedly chooses an action, the environment responds with an outcome, and then the learner receives a reward for the played action. The goal of the learner is to maximize his total reward. However, there are situations in which, in addition to maximizing the cumulative reward, there are some additional constraints on the sequence of decisions that must be satisfied on average by the learner. In this paper we study an extension to the online learning where the learner aims to maximize the total reward given that some additional constraints need to be satisfied. By leveraging on the theory of Lagrangian method in constrained optimization, we propose Lagrangian exponentially weighted average (LEWA) algorithm, which is a primal-dual variant of the well known exponentially weighted average algorithm, to efficiently solve constrained online decision making problems. Using novel theoretical analysis, we establish the regret and the violation of the constraint bounds in full information and bandit feedback models.


Performance improvement in OFDM system by PAPR reduction

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) is an efficient method of data transmission for high speed communication systems. However, the main drawback of OFDM system is the high Peak to Average Power Ratio (PAPR) of the transmitted signals. OFDM consist of large number of independent subcarriers, as a result of which the amplitude of such a signal can have high peak values. Coding, phase rotation and clipping are among many PAPR reduction schemes that have been proposed to overcome this problem. Here two different PAPR reduction methods e.g. partial transmit sequence (PTS) and selective mapping (SLM) are used to reduce PAPR. Significant reduction in PAPR has been achieved using these techniques. The performances of the two methods are then compared.


Complex-Demand Knapsack Problems and Incentives in AC Power Systems

Abstract: We consider AC electrical systems where each electrical device has a power demand expressed as a complex number, and there is a limit on the magnitude of total power supply. Motivated by this scenario, we introduce the complex-demand knapsack problem (C-KP), a new variation of the traditional knapsack problem, where each item is associated with a demand as a complex number, rather than a real number often interpreted as weight or size of the item. While keeping the same goal as to maximize the sum of values of the selected items, we put the capacity limit on the magnitude of the sum of satisfied demands. For C-KP, we prove its inapproximability by FPTAS (unless P = NP), as well as presenting a (1/2-epsilon)-approximation algorithm. Furthermore, we investigate the selfish multi-agent setting where each agent is in charge of one item, and an agent may misreport the demand and value of his item for his own interest. We show a simple way to adapt our approximation algorithm to be monotone, which is sufficient for the existence of incentive compatible payments such that no agent has an incentive to misreport. Our results shed insight on the design of multi-agent systems for smart grid.


Diachronic Linked Data: Towards Long-Term Preservation of Structured Interrelated Information

Abstract: The Linked Data Paradigm is one of the most promising technologies for publishing, sharing, and connecting data on the Web, and offers a new way for data integration and interoperability. However, the proliferation of distributed, inter-connected sources of information and services on the Web poses significant new challenges for managing consistently a huge number of large datasets and their interdependencies. In this paper we focus on the key problem of preserving evolving structured interlinked data. We argue that a number of issues that hinder applications and users are related to the temporal aspect that is intrinsic in linked data. We present a number of real use cases to motivate our approach, we discuss the problems that occur, and propose a direction for a solution.


A note on the factorization conjecture

Abstract: We give partial results on the factorization conjecture on codes proposed by Schutzenberger. We consider finite maximal codes C over the alphabet A = {a, b} with C \cap a^* = a^p, for a prime number p. Let P, S in Z <A>, with S = S_0 + S_1, supp(S_0) \subset a^* and supp(S_1) \subset a^*b supp(S_0). We prove that if (P,S) is a factorization for C then (P,S) is positive, that is P,S have coefficients 0,1, and we characterize the structure of these codes. As a consequence, we prove that if C is a finite maximal code such that each word in C has at most 4 occurrences of b's and a^p is in C, then each factorization for C is a positive factorization. We also discuss the structure of these codes. The obtained results show once again relations between (positive) factorizations and factorizations of cyclic groups.


Publishing Life Science Data as Linked Open Data: the Case Study of miRBase

Abstract: This paper presents our Linked Open Data (LOD) infrastructures for genomic and experimental data related to microRNA biomolecules. Legacy data from two well-known microRNA databases with experimental data and observations, as well as change and version information about microRNA entities, are fused and exported as LOD. Our LOD server assists biologists to explore biological entities and their evolution, and provides a SPARQL endpoint for applications and services to query historical miRNA data and track changes, their causes and effects.


A Multi-Dimensional approach towards Intrusion Detection System

Abstract: In this paper, we suggest a multi-dimensional approach towards intrusion detection. Network and system usage parameters like source and destination IP addresses; source and destination ports; incoming and outgoing network traffic data rate and number of CPU cycles per request are divided into multiple dimensions. Rather than analyzing raw bytes of data corresponding to the values of the network parameters, a mature function is inferred during the training phase for each dimension. This mature function takes a dimension value as an input and returns a value that represents the level of abnormality in the system usage with respect to that dimension. This mature function is referred to as Individual Anomaly Indicator. Individual Anomaly Indicators recorded for each of the dimensions are then used to generate a Global Anomaly Indicator, a function with n variables (n is the number of dimensions) that provides the Global Anomaly Factor, an indicator of anomaly in the system usage based on all the dimensions considered together. The Global Anomaly Indicator inferred during the training phase is then used to detect anomaly in the network traffic during the detection phase. Network traffic data encountered during the detection phase is fed back to the system to improve the maturity of the Individual Anomaly Indicators and hence the Global Anomaly Indicator.


Hajj and Umrah Event Recognition Datasets

Abstract: In this note, new Hajj and Umrah Event Recognition datasets (HUER) are presented. The demonstrated datasets are based on videos and images taken during 2011-2012 Hajj and Umrah seasons. HUER is the first collection of datasets covering the six types of Hajj and Umrah ritual events (rotating in Tawaf around Kabaa, performing Sa'y between Safa and Marwa, standing on the mount of Arafat, staying overnight in Muzdalifah, staying two or three days in Mina, and throwing Jamarat). The HUER datasets also contain video and image databases for nine types of human actions during Hajj and Umrah (walking, drinking from Zamzam water, sleeping, smiling, eating, praying, sitting, shaving hairs and ablutions, reading the holy Quran and making duaa). The spatial resolutions are 1280 x 720 pixels for images and 640 x 480 pixels for videos and have lengths of 20 seconds in average with 30 frame per second rates.


AGEM: Adaptive Greedy-Compass Energy-aware Multipath Routing Protocol for WMSNs

Abstract: This paper presents an Adaptive Greedy-compass Energy-aware Multipath protocol (AGEM), a novel routing protocol for wireless multimedia sensors networks (WMSNs). AGEM uses sensors node positions to make packet forwarding decisions. These decisions are made online, at each forwarding node, in such a way that there is no need for global network topology knowledge and maintenance. AGEM routing protocol performs load-balancing to minimize energy consumption among nodes using twofold policy: (1) smart greedy forwarding, based on adaptive compass and (2) walking back forwarding to avoid holes. Performance evaluations of AGEM compared to GPSR (Greedy Perimeter Stateless Routing) show that AGEM can: (a) maximize the network lifetime, (b) guarantee quality of service for video stream transmission, and (c) scale better on densely deployed wireless sensors network.


Orion Routing Protocol for Delay-Tolerant Networks

Abstract: In this paper, we address the problem of efficient routing in delay tolerant network. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performance. In ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count and first packet arrival.


GEAMS: a Greedy Energy-Aware Multipath Stream-based Routing Protocol for WMSNs

Abstract: Because sensor nodes operate on power limited batteries, sensor functionalities have to be designed carefully. In particular, designing energy-efficient packet forwarding is important to maximize the lifetime of the network and to minimize the power usage at each node. This paper presents a Geographic Energy-Aware Multipath Stream-based (GEAMS) routing protocol for WMSNs. GEAMS routing decisions are made online, at each forwarding node in such a way that there is no need to global topology knowledge and maintenance. GEAMS routing protocol performs load-balancing to minimize energy consumption among nodes using twofold policy: (1) smart greedy forwarding and (2) walking back forwarding. Performances evaluations of GEAMS show that it can maximize the network lifetime and guarantee quality of service for video stream transmission in WMSNs.


Streaming multimedia over WMSNs: an online multipath routing protocol

Abstract: Routing is a challenge to Wireless Multimedia Sensor Networks (WMSNs) for supporting multimedia applications due to nodes' energy constraints and computational capabilities, and the ways sensor nodes obtain forwarding information. In this paper, we propose an online multipath routing protocol that uses nodes' positions to make forwarding decisions at each hop. Real-time decisions are made without any need to have the entire network topology knowledge. The protocol achieves load-balancing and minimises nodes' energy consumption by utilizing: (a) smart greedy forwarding scheme for selecting next hop, and (b) walking back forwarding scheme to bypass network holes. Performance comparisons of the proposed protocol (schemes) are made with TPGF and GPSR. The results show that our schemes: (a) maximise the overall network lifespan by not draining energy from some specific nodes, (b) provide QoS delivery for video streams by using best nodes along the route, and (c) scale better in high density WMSN.


Dynamic Loop Parallelisation

Abstract: Regions of nested loops are a common feature of High Performance Computing (HPC) codes. In shared memory programming models, such as OpenMP, these structure are the most common source of parallelism. Parallelising these structures requires the programmers to make a static decision on how parallelism should be applied. However, depending on the parameters of the problem and the nature of the code, static decisions on which loop to parallelise may not be optimial, especially as they do not enable the exploitation of any runtime characteristics of the execution including changes to the iterations of the loops to be parallelised. We have developed a system that allows a code to make a dynamic choice, at runtime, of what parallelism is applied to nested loops. Our method for providing dynamic decisions on which loop to parallelise significantly outperforms the standard methods for acheiving this through OpenMP (using if clauses).


Flip Distance Between Two Triangulations of a Point-Set is NP-complete

Abstract: Given two triangulations of a convex polygon, computing the minimum number of flips required to transform one to the other is a long-standing open problem. It is not known whether the problem is in P or NP-complete. We prove that two natural generalizations of the problem are NP-complete, namely computing the minimum number of flips between two triangulations of (1) a polygon with holes; (2) a set of points in the plane.


Relaxed Half-Stochastic Belief Propagation

Abstract: Low-density parity-check codes are attractive for high throughput applications because of their low decoding complexity per bit, but also because all the codeword bits can be decoded in parallel. However, achieving this in a circuit implementation is complicated by the number of wires required to exchange messages between processing nodes. Decoding algorithms that exchange binary messages are interesting for fully-parallel implementations because they can reduce the number and the length of the wires, and increase logic density. This paper introduces the Relaxed Half-Stochastic (RHS) decoding algorithm, a binary message belief propagation (BP) algorithm that achieves a coding gain comparable to the best known BP algorithms that use real-valued messages. We derive the RHS algorithm by starting from the well-known Sum-Product algorithm, and then derive a low-complexity version suitable for circuit implementation. We present extensive simulation results on two standardized codes having different rates and constructions, including low bit error rate results. These simulations show that RHS can be an advantageous replacement for the existing state-of-the-art decoding algorithms when targeting fully-parallel implementations.


Securing Mobile Ad hoc Networks:Key Management and Routing

Abstract: Secure communication between two nodes in a network depends on reliable key management systems that generate and distribute keys between communicating nodes and a secure routing protocol that establishes a route between them. But due to lack of central server and infrastructure in Mobile Ad hoc Networks (MANETs), this is major problem to manage the keys in the network. Dynamically changes in network's topology causes weak trust relationship among the nodes in the network. In MANETs a mobile node operates as not only end terminal but also as an intermediate router. Therefore, a multi-hop scenario occurs for communication in MANETs; where there may be one or more malicious nodes in between source and destination. A routing protocol is said to be secure that detects the detrimental effects of malicious node(s in the path from source to destination). In this paper, we proposed a key management scheme and a secure routing protocol that secures on demand routing protocol such as DSR and AODV. We assume that MANETs is divided into groups having a group leader in each group. Group leader has responsibility of key management in its group. Proposed key management scheme is a decentralized scheme that does not require any Trusted Third Party (TTP) for key management. In proposed key management system, both a new node and group leader authenticates each other mutually before joining the network. While proposed secure routing protocol allows both communicating parties as well as intermediate nodes to authenticate other nodes and maintains message integrity


Identifying And Weighting Integration Hypotheses On Open Data Platforms

Abstract: Open data platforms such as data.gov or opendata.socrata. com provide a huge amount of valuable information. Their free-for-all nature, the lack of publishing standards and the multitude of domains and authors represented on these platforms lead to new integration and standardization problems. At the same time, crowd-based data integration techniques are emerging as new way of dealing with these problems. However, these methods still require input in form of specific questions or tasks that can be passed to the crowd. This paper discusses integration problems on Open Data Platforms, and proposes a method for identifying and ranking integration hypotheses in this context. We will evaluate our findings by conducting a comprehensive evaluation using on one of the largest Open Data platforms.


Linking Social Networking Sites to Scholarly Information Portals by ScholarLib

Abstract: Online Social Networks usually provide no or limited way to access scholarly information provided by Digital Libraries (DLs) in order to share and discuss scholarly content with other online community members. The paper addresses the potentials of Social Networking sites (SNSs) for science and proposes initial use cases as well as a basic bi-directional model called ScholarLib for linking SNSs to scholarly DLs. The major aim of ScholarLib is to make scholarly information provided by DLs accessible at SNSs, and vice versa, to enhance retrieval quality at DL side by social information provided by SNSs.


Open Data Visualization: Keeping Traces of the Exploration Process

Abstract: This paper describes a system to support the visual exploration of Open Data. During his/her interactive experience with the graphics, the user can easily store the current complete state of the visualization application (called a viewpoint). Next, he/she can compose sequences of these viewpoints (called scenarios) that can easily be reloaded. This feature allows to keep traces of a former exploration process, which can be useful in single user (to support investigation carried out in multiple sessions) as well as in collaborative setting (to share points of interest identified in the data set).


Refining Inductive Types

Abstract: Dependently typed programming languages allow sophisticated properties of data to be expressed within the type system. Of particular use in dependently typed programming are indexed types that refine data by computationally useful information. For example, the N-indexed type of vectors refines lists by their lengths. Other data types may be refined in similar ways, but programmers must produce purpose-specific refinements on an ad hoc basis, developers must anticipate which refinements to include in libraries, and implementations must often store redundant information about data and their refinements. In this paper we show how to generically derive inductive characterisations of refinements of inductive types, and argue that these characterisations can alleviate some of the aforementioned difficulties associated with ad hoc refinements. Our characterisations also ensure that standard techniques for programming with and reasoning about inductive types are applicable to refinements, and that refinements can themselves be further refined.


Dynamic Dependency Pairs for Algebraic Functional Systems

Abstract: We extend the higher-order termination method of dynamic dependency pairs to Algebraic Functional Systems (AFSs). In this setting, simply typed lambda-terms with algebraic reduction and separate {\beta}-steps are considered. For left-linear AFSs, the method is shown to be complete. For so-called local AFSs we define a variation of usable rules and an extension of argument filterings. All these techniques have been implemented in the higher-order termination tool WANDA.


An improved approach to attribute reduction with covering rough sets

Abstract: Attribute reduction is viewed as an important preprocessing step for pattern recognition and data mining. Most of researches are focused on attribute reduction by using rough sets. Recently, Tsang et al. discussed attribute reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel S. Yeung, Approximations and reducts with covering generalized rough sets, Computers and Mathematics with Applications 56 (2008) 279-289], where an approach based on discernibility matrix was presented to compute all attribute reducts. In this paper, we provide an improved approach by constructing simpler discernibility matrix with covering rough sets, and then proceed to improve some characterizations of attribute reduction provided by Tsang et al. It is proved that the improved discernible matrix is equivalent to the old one, but the computational complexity of discernible matrix is greatly reduced.


CloudMonitor: Profiling Power Usage

Abstract: In Cloud Computing platforms the addition of hardware monitoring devices to gather power usage data can be impractical or uneconomical due to the large number of machines to be metered. CloudMonitor, a monitoring tool that can generate power models for software-based power estimation, can provide insights to the energy costs of deployments without additional hardware. Accurate power usage data leads to the possibility of Cloud providers creating a separate tariff for power and therefore incentivizing software developers to create energy-efficient applications.


Public Data Integration with WebSmatch

Abstract: Integrating open data sources can yield high value information but raises major problems in terms of metadata extraction, data source integration and visualization of integrated data. In this paper, we describe WebSmatch, a flexible environment for Web data integration, based on a real, end-to-end data integration scenario over public data from Data Publica. WebSmatch supports the full process of importing, refining and integrating data sources and uses third party tools for high quality visualization. We use a typical scenario of public data integration which involves problems not solved by currents tools: poorly structured input data sources (XLS files) and rich visualization of integrated data.


Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (2011)

Abstract: This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.


Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010)

Abstract: This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11 2010.


A Uniqueness Theorem for Clustering

Abstract: Despite the widespread use of Clustering, there is distressingly little general theory of clustering available. Questions like "What distinguishes a clustering of data from other data partitioning?", "Are there any principles governing all clustering paradigms?", "How should a user choose an appropriate clustering algorithm for a particular task?", etc. are almost completely unanswered by the existing body of clustering literature. We consider an axiomatic approach to the theory of Clustering. We adopt the framework of Kleinberg, [Kle03]. By relaxing one of Kleinberg's clustering axioms, we sidestep his impossibility result and arrive at a consistent set of axioms. We suggest to extend these axioms, aiming to provide an axiomatic taxonomy of clustering paradigms. Such a taxonomy should provide users some guidance concerning the choice of the appropriate clustering paradigm for a given task. The main result of this paper is a set of abstract properties that characterize the Single-Linkage clustering function. This characterization result provides new insight into the properties of desired data groupings that make Single-Linkage the appropriate choice. We conclude by considering a taxonomy of clustering functions based on abstract properties that each satisfies.


Most Relevant Explanation: Properties, Algorithms, and Evaluations

Abstract: Most Relevant Explanation (MRE) is a method for finding multivariate explanations for given evidence in Bayesian networks [12]. This paper studies the theoretical properties of MRE and develops an algorithm for finding multiple top MRE solutions. Our study shows that MRE relies on an implicit soft relevance measure in automatically identifying the most relevant target variables and pruning less relevant variables from an explanation. The soft measure also enables MRE to capture the intuitive phenomenon of explaining away encoded in Bayesian networks. Furthermore, our study shows that the solution space of MRE has a special lattice structure which yields interesting dominance relations among the solutions. A K-MRE algorithm based on these dominance relations is developed for generating a set of top solutions that are more representative. Our empirical results show that MRE methods are promising approaches for explanation in Bayesian networks.


The Entire Quantile Path of a Risk-Agnostic SVM Classifier

Abstract: A quantile binary classifier uses the rule: Classify x as +1 if P(Y = 1|X = x) >= t, and as -1 otherwise, for a fixed quantile parameter t {[0, 1]. It has been shown that Support Vector Machines (SVMs) in the limit are quantile classifiers with t = 1/2 . In this paper, we show that by using asymmetric cost of misclassification SVMs can be appropriately extended to recover, in the limit, the quantile binary classifier for any t. We then present a principled algorithm to solve the extended SVM classifier for all values of t simultaneously. This has two implications: First, one can recover the entire conditional distribution P(Y = 1|X = x) = t for t {[0, 1]. Second, we can build a risk-agnostic SVM classifier where the cost of misclassification need not be known apriori. Preliminary numerical experiments show the effectiveness of the proposed algorithm.


Exploring compact reinforcement-learning representations with linear regression

Abstract: This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.


Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies

Abstract: We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme.


Probabilistic Structured Predictors

Abstract: We consider MAP estimators for structured prediction with exponential family models. In particular, we concentrate on the case that efficient algorithms for uniform sampling from the output space exist. We show that under this assumption (i) exact computation of the partition function remains a hard problem, and (ii) the partition function and the gradient of the log partition function can be approximated efficiently. Our main result is an approximation scheme for the partition function based on Markov Chain Monte Carlo theory. We also show that the efficient uniform sampling assumption holds in several application settings that are of importance in machine learning.


Ordinal Boltzmann Machines for Collaborative Filtering

Abstract: Collaborative filtering is an effective recommendation technique wherein the preference of an individual can potentially be predicted based on preferences of other members. Early algorithms often relied on the strong locality in the preference data, that is, it is enough to predict preference of a user on a particular item based on a small subset of other users with similar tastes or of other items with similar properties. More recently, dimensionality reduction techniques have proved to be equally competitive, and these are based on the co-occurrence patterns rather than locality. This paper explores and extends a probabilistic model known as Boltzmann Machine for collaborative filtering tasks. It seamlessly integrates both the similarity and co-occurrence in a principled manner. In particular, we study parameterisation options to deal with the ordinal nature of the preferences, and propose a joint modelling of both the user-based and item-based processes. Experiments on moderate and large-scale movie recommendation show that our framework rivals existing well-known methods.


Measuring Inconsistency in Probabilistic Knowledge Bases

Abstract: This paper develops an inconsistency measure on conditional probabilistic knowledge bases. The measure is based on fundamental principles for inconsistency measures and thus provides a solid theoretical framework for the treatment of inconsistencies in probabilistic expert systems. We illustrate its usefulness and immediate application on several examples and present some formal results. Building on this measure we use the Shapley value-a well-known solution for coalition games-to define a sophisticated indicator that is not only able to measure inconsistencies but to reveal the causes of inconsistencies in the knowledge base. Altogether these tools guide the knowledge engineer in his aim to restore consistency and therefore enable him to build a consistent and usable knowledge base that can be employed in probabilistic expert systems.


Bisimulation-based Approximate Lifted Inference

Abstract: There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.


Regret-based Reward Elicitation for Markov Decision Processes

Abstract: The specification of aMarkov decision process (MDP) can be difficult. Reward function specification is especially problematic; in practice, it is often cognitively complex and time-consuming for users to precisely specify rewards. This work casts the problem of specifying rewards as one of preference elicitation and aims to minimize the degree of precision with which a reward function must be specified while still allowing optimal or near-optimal policies to be produced. We first discuss how robust policies can be computed for MDPs given only partial reward information using the minimax regret criterion. We then demonstrate how regret can be reduced by efficiently eliciting reward information using bound queries, using regret-reduction as a means for choosing suitable queries. Empirical results demonstrate that regret-based reward elicitation offers an effective way to produce near-optimal policies without resorting to the precise specification of the entire reward function.


Exact Structure Discovery in Bayesian Networks with Less Space

Abstract: The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2nnO(1). The usage of these algorithms is limited to networks on at most around 25 nodes mainly due to the space requirement. Here, we study space-time tradeoffs for finding an optimal network structure. When little space is available, we apply the Gurevich-Shelah recurrence-originally proposed for the Hamiltonian path problem-and obtain time 22n-snO(1) in space 2snO(1) for any s = n/2, n/4, n/8, . . .; we assume the indegree of each node is bounded by a constant. For the more practical setting with moderate amounts of space, we present a novel scheme. It yields running time 2n(3/2)pnO(1) in space 2n(3/4)pnO(1) for any p = 0, 1, . . ., n/2; these bounds hold as long as the indegrees are at most 0.238n. Furthermore, the latter scheme allows easy and efficient parallelization beyond previous algorithms. We also explore empirically the potential of the presented techniques.


Logical Inference Algorithms and Matrix Representations for Probabilistic Conditional Independence

Abstract: Logical inference algorithms for conditional independence (CI) statements have important applications from testing consistency during knowledge elicitation to constraintbased structure learning of graphical models. We prove that the implication problem for CI statements is decidable, given that the size of the domains of the random variables is known and fixed. We will present an approximate logical inference algorithm which combines a falsification and a novel validation algorithm. The validation algorithm represents each set of CI statements as a sparse 0-1 matrix A and validates instances of the implication problem by solving specific linear programs with constraint matrix A. We will show experimentally that the algorithm is both effective and efficient in validating and falsifying instances of the probabilistic CI implication problem.


Convexifying the Bethe Free Energy

Abstract: The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorithm in an attempt to overcome convergence and local optima problems. Notable among these are convexified free energy approximations that lead to inference procedures with provable convergence and quality properties. However, empirically LBP still outperforms most of its convex variants in a variety of settings, as we also demonstrate here. Motivated by this fact we seek convexified free energies that directly approximate the Bethe free energy. We show that the proposed approximations compare favorably with state-of-the art convex free energy approximations.


Convergent message passing algorithms - a unifying view

Abstract: Message-passing algorithms have emerged as powerful techniques for approximate inference in graphical models. When these algorithms converge, they can be shown to find local (or sometimes even global) optima of variational formulations to the inference problem. But many of the most popular algorithms are not guaranteed to converge. This has lead to recent interest in convergent message-passing algorithms. In this paper, we present a unified view of convergent message-passing algorithms. We present a simple derivation of an abstract algorithm, tree-consistency bound optimization (TCBO) that is provably convergent in both its sum and max product forms. We then show that many of the existing convergent algorithms are instances of our TCBO algorithm, and obtain novel convergent algorithms "for free" by exchanging maximizations and summations in existing algorithms. In particular, we show that Wainwright's non-convergent sum-product algorithm for tree based variational bounds, is actually convergent with the right update order for the case where trees are monotonic chains.


Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions

Abstract: Strategyproof mechanisms provide robust equilibrium with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms that simultaneously satisfy other desirable properties, we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof "reference" mechanism that solves a problem relaxation. Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regretbased metrics are not, and can be used for online selection of an effective mechanism.


MAP Estimation of Semi-Metric MRFs via Hierarchical Graph Cuts

Abstract: We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials. For this problem, we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st-MINCUT problem. Unlike previous move making approaches, e.g. the widely used a-expansion algorithm, our method obtains the guarantees of the standard linear programming (LP) relaxation for the important special case of metric labeling. Unlike the existing LP relaxation solvers, e.g. interior-point algorithms or tree-reweighted message passing, our method is significantly faster as it uses only the efficient st-MINCUT algorithm in its design. Using both synthetic and real data experiments, we show that our technique outperforms several commonly used algorithms.


The Temporal Logic of Causal Structures

Abstract: Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulae with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm's computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.


Constraint Processing in Lifted Probabilistic Inference

Abstract: First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.


Monolingual Probabilistic Programming Using Generalized Coroutines

Abstract: Probabilistic programming languages and modeling toolkits are two modular ways to build and reuse stochastic models and inference procedures. Combining strengths of both, we express models and inference as generalized coroutines in the same general-purpose language. We use existing facilities of the language, such as rich libraries, optimizing compilers, and types, to develop concise, declarative, and realistic models with competitive performance on exact and approximate inference. In particular, a wide range of models can be expressed using memoization. Because deterministic parts of models run at full speed, custom inference procedures are trivial to incorporate, and inference procedures can reason about themselves without interpretive overhead. Within this framework, we introduce a new, general algorithm for importance sampling with look-ahead.


Counting Belief Propagation

Abstract: A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflected in the graphical structure and hence not exploitable by efficient inference techniques such as belief propagation (BP). In this paper, we present a new and simple BP algorithm, called counting BP, that exploits such additional symmetries. Starting from a given factor graph, counting BP first constructs a compressed factor graph of clusternodes and clusterfactors, corresponding to sets of nodes and factors that are indistinguishable given the evidence. Then it runs a modified BP algorithm on the compressed graph that is equivalent to running BP on the original factor graph. Our experiments show that counting BP is applicable to a variety of important AI tasks such as (dynamic) relational models and boolean model counting, and that significant efficiency gains are obtainable, often by orders of magnitude.


Temporal Action-Graph Games: A New Representation for Dynamic Games

Abstract: In this paper we introduce temporal action graph games (TAGGs), a novel graphical representation of imperfect-information extensive form games. We show that when a game involves anonymity or context-specific utility independencies, its encoding as a TAGG can be much more compact than its direct encoding as a multiagent influence diagram (MAID).We also show that TAGGs can be understood as indirect MAID encodings in which many deterministic chance nodes are introduced. We provide an algorithm for computing with TAGGs, and show both theoretically and empirically that our approach improves significantly on the previous state of the art.


MAP Estimation, Message Passing, and Perfect Graphs

Abstract: Efficiently finding the maximum a posteriori (MAP) configuration of a graphical model is an important problem which is often implemented using message passing algorithms. The optimality of such algorithms is only well established for singly-connected graphs and other limited settings. This article extends the set of graphs where MAP estimation is in P and where message passing recovers the exact solution to so-called perfect graphs. This result leverages recent progress in defining perfect graphs (the strong perfect graph theorem), linear programming relaxations of MAP estimation and recent convergent message passing schemes. The article converts graphical models into nand Markov random fields which are straightforward to relax into linear programs. Therein, integrality can be established in general by testing for graph perfection. This perfection test is performed efficiently using a polynomial time algorithm. Alternatively, known decomposition tools from perfect graph theory may be used to prove perfection for certain families of graphs. Thus, a general graph framework is provided for determining when MAP estimation in any graphical model is in P, has an integral linear programming relaxation and is exactly recoverable by message passing.


Improved Mean and Variance Approximations for Belief Net Responses via Network Doubling

Abstract: A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute responses to queries; i.e., conditional probabilities of interest. A query is a function of the parameters, hence a random variable. Van Allen et al. (2001, 2008) showed how to quantify uncertainty about a query via a delta method approximation of its variance. We develop more accurate approximations for both query mean and variance. The key idea is to extend the query mean approximation to a "doubled network" involving two independent replicates. Our method assumes complete data and can be applied to discrete, continuous, and hybrid networks (provided discrete variables have only discrete parents). We analyze several improvements, and provide empirical studies to demonstrate their effectiveness.


First-Order Mixed Integer Linear Programming

Abstract: Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inference in FOL for corresponding problems.


Distributed Parallel Inference on Large Factor Graphs

Abstract: As computer clusters become more common and the size of the problems encountered in the field of AI grows, there is an increasing demand for efficient parallel inference algorithms. We consider the problem of parallel inference on large factor graphs in the distributed memory setting of computer clusters. We develop a new efficient parallel inference algorithm, DBRSplash, which incorporates over-segmented graph partitioning, belief residual scheduling, and uniform work Splash operations. We empirically evaluate the DBRSplash algorithm on a 120 processor cluster and demonstrate linear to super-linear performance gains on large factor graph models.


Censored Exploration and the Dark Pool Problem

Abstract: We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/ exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.


Generating Optimal Plans in Highly-Dynamic Domains

Abstract: Generating optimal plans in highly dynamic environments is challenging. Plans are predicated on an assumed initial state, but this state can change unexpectedly during plan generation, potentially invalidating the planning effort. In this paper we make three contributions: (1) We propose a novel algorithm for generating optimal plans in settings where frequent, unexpected events interfere with planning. It is able to quickly distinguish relevant from irrelevant state changes, and to update the existing planning search tree if necessary. (2) We argue for a new criterion for evaluating plan adaptation techniques: the relative running time compared to the "size" of changes. This is significant since during recovery more changes may occur that need to be recovered from subsequently, and in order for this process of repeated recovery to terminate, recovery time has to converge. (3) We show empirically that our approach can converge and find optimal plans in environments that would ordinarily defy planning due to their high dynamics.


A Sampling-Based Approach to Computing Equilibria in Succinct Extensive-Form Games

Abstract: A central task of artificial intelligence is the design of artificial agents that act towards specified goals in partially observed environments. Since such environments frequently include interaction over time with other agents with their own goals, reasoning about such interaction relies on sequential game-theoretic models such as extensive-form games or some of their succinct representations such as multi-agent influence diagrams. The current algorithms for calculating equilibria either work with inefficient representations, possibly doubly exponential inthe number of time steps, or place strong assumptions on the game structure. In this paper,we propose a sampling-based approach, which calculates extensive-form correlated equilibria with small representations without placing such strong assumptions. Thus, it is practical in situations where the previous approaches would fail. In addition, our algorithm allows control over characteristics of the target equilibrium, e.g., we can ask for an equilibrium with high social welfare. Our approach is based on a multiplicativeweight update algorithm analogous to AdaBoost, and Markov chain Monte Carlo sampling. We prove convergence guarantees and explore the utility of our approach on several moderately sized multi-player games.


Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making

Abstract: We introduce a challenging real-world planning problem where actions must be taken at each location in a spatial area at each point in time. We use forestry planning as the motivating application. In Large Scale Spatial-Temporal (LSST) planning problems, the state and action spaces are defined as the cross-products of many local state and action spaces spread over a large spatial area such as a city or forest. These problems possess state uncertainty, have complex utility functions involving spatial constraints and we generally must rely on simulations rather than an explicit transition model. We define LSST problems as reinforcement learning problems and present a solution using policy gradients. We compare two different policy formulations: an explicit policy that identifies each location in space and the action to take there; and an abstract policy that defines the proportion of actions to take across all locations in space. We show that the abstract policy is more robust and achieves higher rewards with far fewer parameters than the elementary policy. This abstract policy is also a better fit to the properties that practitioners in LSST problem domains require for such methods to be widely useful.


Complexity Analysis and Variational Inference for Interpretation-based Probabilistic Description Logic

Abstract: This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies. Inference is shown to be PEXP-complete, and variational methods are designed so as to exploit logical inference whenever possible.


Prediction Markets, Mechanism Design, and Cooperative Game Theory

Abstract: Prediction markets are designed to elicit information from multiple agents in order to predict (obtain probabilities for) future events. A good prediction market incentivizes agents to reveal their information truthfully; such incentive compatibility considerations are commonly studied in mechanism design. While this relation between prediction markets and mechanism design is well understood at a high level, the models used in prediction markets tend to be somewhat different from those used in mechanism design. This paper considers a model for prediction markets that fits more straightforwardly into the mechanism design framework. We consider a number of mechanisms within this model, all based on proper scoring rules. We discuss basic properties of these mechanisms, such as incentive compatibility. We also draw connections between some of these mechanisms and cooperative game theory. Finally, we speculate how one might build a practical prediction market based on some of these ideas.


Mean Field Variational Approximation for Continuous-Time Bayesian Networks

Abstract: Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact representation, inference in such models is intractable even in relatively simple structured networks. Here we introduce a mean field variational approximation in which we use a product of inhomogeneous Markov processes to approximate a distribution over trajectories. This variational approach leads to a globally consistent distribution, which can be efficiently queried. Additionally, it provides a lower bound on the probability of observations, thus making it attractive for learning tasks. We provide the theoretical foundations for the approximation, an efficient implementation that exploits the wide range of highly optimized ordinary differential equations (ODE) solvers, experimentally explore characterizations of processes for which this approximation is suitable, and show applications to a large-scale realworld inference problem.


Deterministic POMDPs Revisited

Abstract: We study a subclass of POMDPs, called Deterministic POMDPs, that is characterized by deterministic actions and observations. These models do not provide the same generality of POMDPs yet they capture a number of interesting and challenging problems, and permit more efficient algorithms. Indeed, some of the recent work in planning is built around such assumptions mainly by the quest of amenable models more expressive than the classical deterministic models. We provide results about the fundamental properties of Deterministic POMDPs, their relation with AND/OR search problems and algorithms, and their computational complexity.


REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs

Abstract: We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~O(HSpAT). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.


Are visual dictionaries generalizable?

Abstract: Mid-level features based on visual dictionaries are today a cornerstone of systems for classification and retrieval of images. Those state-of-the-art representations depend crucially on the choice of a codebook (visual dictionary), which is usually derived from the dataset. In general-purpose, dynamic image collections (e.g., the Web), one cannot have the entire collection in order to extract a representative dictionary. However, based on the hypothesis that the dictionary reflects only the diversity of low-level appearances and does not capture semantics, we argue that a dictionary based on a small subset of the data, or even on an entirely different dataset, is able to produce a good representation, provided that the chosen images span a diverse enough portion of the low-level feature space. Our experiments confirm that hypothesis, opening the opportunity to greatly alleviate the burden in generating the codebook, and confirming the feasibility of employing visual dictionaries in large-scale dynamic environments.


A Bayesian Sampling Approach to Exploration in Reinforcement Learning

Abstract: We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to resample and how to combine the models. We show that our algorithm achieves nearoptimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.


Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds on Probability Distributions in Bayesian Networks

Abstract: We present a new method to propagate lower bounds on conditional probability distributions in conventional Bayesian networks. Our method guarantees to provide outer approximations of the exact lower bounds. A key advantage is that we can use any available algorithms and tools for Bayesian networks in order to represent and infer lower bounds. This new method yields results that are provable exact for trees with binary variables, and results which are competitive to existing approximations in credal networks for all other network structures. Our method is not limited to a specific kind of network structure. Basically, it is also not restricted to a specific kind of inference, but we restrict our analysis to prognostic inference in this article. The computational complexity is superior to that of other existing approaches.


Intelligent learning environments within blended learning for ensuring effective C programming course

Abstract: This paper describes a blended learning implementation and experience supported with intelligent learning environments included in a learning management system (LMS) called @KU-UZEM. The blended learning model is realized as a combination of face to face education and e-learning. The intelligent learning environments consist of two applications named CTutor, ITest. In addition to standard e-learning tools, students can use CTutor to resolve C programming exercises. CTutor is a problem-solving environment, which diagnoses students' knowledge level but also gives feedbacks and tips to help them to understand the course subject, overcome their misconceptions and reinforce learnt concepts. ITest provides an assessment environment in which students can take quizzes that were prepared according to their learning levels. The realized model was used for two terms in the "C Programming" course given at Afyon Kocatepe University. A survey was conducted at the end of the course to find out to what extent the students were accepting the blended learning model supported with @KU-UZEM and to discover students' attitude towards intelligent learning environments. Additionally, an experiment formed with an experimental group who took an active part in the realized model and a control group who only took the face to face education was performed during the first term of the course. According to the results, students were satisfied with intelligent learning environments and the realized learning model. Furthermore, the use of intelligent learning environments improved the students' knowledge about C programming.