ppxscal/embedding_network huggingface dataset:
Virtual-Force-Based Geometric Routing Protocol in MANETs
Authors: Cong Liu, Jie Wu
Venue: IEEE Transactions on Parallel and Distributed Systems (2009)

Routing is the foremost issue in mobile ad hoc networks (MANETs). To guarantee delivery and improve performance, most position-based routing protocols, e.g. \emph{greedy-face-greedy} (GFG), forward a message in greedy routing mode until the message is forwarded to a \emph{local minimum} where greedy forwarding is impossible. They then switch to a less efficient mode, face routing. Face routing requires the underlying network to be a planar graph and a unit disk graph (UDG) which makes geometric routing only theoretically feasible. To remove these constraints, this paper tackles the local minimum problem with two new methods. First, we construct a virtual small world network by adding virtual long links to the network to reduce the number of local minima. Second, we use the virtual force method to recover from local minima without relying on face routing. Combining these two methods, we propose a purely greedy routing protocol, \emph{small world iterative navigation greedy} (SWING+) routing protocol. Simulation is performed to evaluate SWING+ against existing geometric routing protocols. Simulation results show that SWING+ guarantees delivery and its performance is comparable to that of the state-of-the-art \emph{greedy other adaptive face routing} (GOAFR+) routing protocol.
Conjugated trees with minimum general Randić index
Authors: Xiaodan Chen, Jianguo Qian
Venue: Discrete Applied Mathematics (2009)

The general Randic index R"@a(G) is the sum of the weights (d"G(u)d"G(v))^@a over all edges uv of a (molecular) graph G, where @a is a real number and d"G(u) is the degree of the vertex u of G. In this paper, for any real number @a@?-1, the minimum general Randic index R"@a(T) among all the conjugated trees (trees with a Kekule structure) is determined and the corresponding extremal conjugated trees are characterized. These trees are also extremal over all the conjugated chemical trees.
A relay node deployment method for disconnected wireless sensor networks: Applied in indoor environments
Authors: Jenn-Hwan Tarng, Bing-Wen Chuang, Pei-Chen Liu
Venue: Journal of Network and Computer Applications (2009)

In this paper, a 2-D geometric and adaptive relay node deployment method with polynomial-time complexity is proposed to reduce relay nodes for bridging all disconnected node pairs of a wireless sensor network (WSN). In this method, proper locations for placing relay nodes are discovered on a plane, which gives a much larger degree of freedom to compare with the traditional 1-D search algorithm. In this way, the deployed relay node can bridge as many disconnected node pairs as possible around it. Besides, the method can also adapt to radio environments because that a sophisticated propagation model including large-scale path loss, shadowing, and multipath fading effects is used to estimate link connectivity. Simulation result validates its robustness and efficiency compared with the existing relay node deployment methods. The proposed method significantly reduces the total number of relay nodes compared with the related works in all of the investigated indoor environments.
Predictive models in the brain
Authors: Keith L. Downing
Venue: Connection Science (2009)

Many neuroscientists view prediction as one of the core brain functions. However, there is little consensus as to the exact nature of predictive information and processes, or the neural mechanisms that realise them. This paper reviews a host of neural models believed to underlie the learning and deployment of predictive knowledge in a variety of brain regions: neocortex, hippocampus, thalamus, basal ganglia and cerebellum. These are compared and contrasted in order to codify a few basic aspects of neural circuitry and dynamics that appear to be the heart of prediction.
Evolvable neural networks ensembles for accidents diagnosis
Authors: Hany Sallam, Carlo S. Regazzoni, Ihab Talkhan, Amir Atiya
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

Prediction and diagnosis of nuclear accidents is one of the most important tasks for nuclear safety. Since accurate diagnosis of nuclear accident is a very important issue for avoidance of disastrous outcomes, it is more desirable to make a decision by combining the results of various expert classifiers rather than by depending on the results of only one classifier. To deal with such challenging problem, the idea of using ensemble classifiers to predict and diagnose accidents is proposed in this paper. Ensembles improve both the efficiency in learning the model and the accuracy in performing classification. Ensemble classifier has substantial advantage over single classifier in prediction accuracy, and the ensemble framework is effective for a variety of classification models. A new method to design and evolve neural network ensembles NNEs based on speciation is used classify reactor data. The main advantage of this method is that, it completely evolves NNEs by combining the evolution of neural networks and the configuration of the ensemble in one evolutionary phase. After forming the ensemble, several accident diagnoses were performed. The results indicate that ensemble able to diagnose the accidents accurately with high rate.
Task Based Automatic Examination System for Sequenced Test
Authors: Song Luo, Jianbin Hu, Zhong Chen
Venue: Proceedings of the 2009 International Conference on Electronic Computer Technology (2009)

Over the last years, automatic computer examination systems have been widely used for computer-based tests. But these systems are based on traditional question-answer examination style which is not fit for the sequenced test. The sequenced test should consider the context of the examinee, e.g. the order of questions or the permissions of the examinee. In this paper, we propose an effective and practical automatic examination architecture based on task. The task is abstracted from the examination process and can meet the requests of the sequenced test, such as order and dependency. At the end of the paper, we implement an automatic examination system based on task for the stake test which proves quite efficient in practice.
A memetic algorithm-based heuristic for a scheduling problem in printed circuit board assembly
Authors: Patcharaporn Neammanee, Manop Reodecha
Venue: Computers and Industrial Engineering (2009)

This research develops a memetic algorithm to solve Printed Circuit Board (PCB) scheduling with sequence-dependent setup times on a single machine with constrained feeder capacity. The objective of the scheduling problem is to minimize the total weighted tardiness. A memetic algorithm-based heuristics is developed by integrating a genetic algorithm, Minimum Slack Time (MST) scheduling rule, ''Keep Tool Needed Soonest'' (KTNS) policy, and a local search procedure. Application of the MA results in two outcome plans: a scheduling plan and a feeder setup plan. Numerical experiments show that compared to a number of commonly used dispatching rules, the memetic algorithm provides better solutions in term of minimum total weighted tardiness. Even the computation is the highest, it still practical. Calibration of MA parameter values is also explored in this study.
Superscalar architecture design for high performance DSP operations
Authors: Faheem Sheikh, Shahid Masud, Rehan Ahmed
Venue: Microprocessors Microsystems (2009)

This paper evaluates the possibility of using a general purpose superscalar architecture as the main computational engine for high performance DSP algorithms. Real-time sample rate conversion (SRC) in a software defined radio (SDR) has been taken as an example representing a class of computationally demanding DSP tasks. This scenario corresponds to digital filters operating at a high sampling rate in intermediate frequency (IF) stage of a multi-standard wireless transceiver. However, instead of a dedicated signal processing engine, a superscalar processor is designed for SRC implementation. An iterative, SimpleScalar based architectural modeling tool has been developed to analyze various parameters of superscalar processors. Both power and performance metrics have been taken under consideration to come up with an efficient design. It has been shown that the resulting superscalar architecture can provide a fully programmable solution capable of supporting future wireless communication standards in real-time. The design methodology explored in this work can be extended to obtain efficient processor architectures for a range of other applications.
Using partitioned databases for statistical data analysis
Authors: Ruven Brooks, Meera Blattner, Zdzislaw Pawlak, Eamon Barrett
Venue: Proceedings of the May 4-7, 1981, national computer conference (1981)

The statistical analysis of scientific data is a process that can be viewed as consisting of three fundamental phases. First, the observations are recorded. Next, they are encoded into a numeric form suitable for statistical analysis. Finally, the calculations are performed for the particular type of analysis needed for the design of the study. This ordering is, however, only conceptual; in most real studies, the three phases interact and are overlapped. Thus, it may be the case that a preliminary analysis run indicates that a more refined coding scheme is needed or that the coding process reveals deficiencies in the data collection.
Immune programming models of Cryptosporidium parvum inactivation by ozone and chlorine dioxide
Authors: Adriel Lau, Petr Musilek
Venue: Information Sciences: an International Journal (2009)

This paper presents analytical models of Cryptosporidium parvum inactivation that have been evolved using immune programming. The objective of these models is to predict the reduction of infectivity associated with the disinfection by ozone and chlorine dioxide. To solve this problem, we introduce a modified immune programming approach together with corresponding implementation of the immune algorithm. The modeling results indicate that models obtained with immune programming outperform the traditional temperature corrected Chick-Watson models, as well as previously developed artificial neural network models. Detailed analysis of modeling errors, prediction power, and behavior of the models are included. Obtained models reveal that some input attributes have no effect on the prediction performance. This finding corresponds to the results previously obtained by saliency analysis of neural models. Results obtained in this study suggest that immune programming is becoming a mature technology which is ready for wide implementation in applications.
Creating User Profiles of Web Visitors Using Zones, Weights and Actions
Authors: Natascha Hoebel, Roberto V. Zicari
Venue: Proceedings of the 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services (2008)

In this paper, we report our experience in the implementation of a module for creating user profiles of Web visitors by using Zones, Weights and Actions. The module is part of Gugubarra 2.0, a tool for better understanding and management of communities of registered Web visitors, currently being developed by the database group at the Computer Science Institute of the Goethe University in Frankfurt. In addition we introduce a semantic concept for our approach.
On-line scheduling on an unbounded parallel batch machine to minimize makespan of two families of jobs
Authors: Ruyan Fu, Ji Tian, Jinjiang Yuan
Venue: Journal of Scheduling (2009)

We study the on-line scheduling on an unbounded parallel batch machine to minimize makespan of two families of jobs. In this model, jobs arrive over time and jobs from different families cannot be scheduled in a common batch. We provide a best possible on-line algorithm for the problem with competitive ratio $(\sqrt{17}+3)/4\approx1.7808$ .
Breaking the data encryption standard using networks of evolutionary processors with parallel string rewriting rules
Authors: Ashish Choudhary, Kamala Krithivasan
Venue: International Journal of Computer Mathematics (2009)

In this paper we introduce a biologically inspired distributed computing model called networks of evolutionary processors with parallel string rewriting rules (NEPPS), which is a variation of the hybrid networks of evolutionary processors introduced by Martin-Vide et al. Such a network contains simple processors that are located in the nodes of a virtual graph. Each processor has strings (each string having multiple copies) and string rewriting rules. The rules are applied parallely on the strings. After the strings have been rewritten, they are communicated among the processors through filters. We show that we can theoretically break the DES (data encryption standard), which is the most widely used cryptosystem, using NEPPS. We prove that, given an arbitrary
(Φ,Φ*) Image Decomposition Models and Minimization Algorithms
Authors: Triet M. Le, Linh H. Lieu, Luminita A. Vese
Venue: Journal of Mathematical Imaging and Vision (2009)

We propose in this paper minimization algorithms for image restoration using dual functionals and dual norms. In order to extract a clean image u from a degraded version f=Ku+n (where f is the observation, K is a blurring operator and n represents additive noise), we impose a standard regularization penalty &Phi;(u)=&int; &phi;(|Du|)dx< &infin; on u, where &phi; is positive, increasing and has at most linear growth at infinity. However, on the residual f&iquest;Ku we impose a dual penalty &Phi;*(f-Ku)< &infin;, instead of the more standard $\|f-\mathit{Ku}\|^{2}_{L^{2}}$ fidelity term. In particular, when &phi; is convex, homogeneous of degree one, and with linear growth (for instance the total variation of u), we recover the (BV,BV *) decomposition of the data f, as suggested by Y. Meyer (Oscillating Patterns in Image Processing and Nonlinear Evolution Equations, University Lecture Series, vol. 22, Am. Math. Soc., Providence, 2001). Practical minimization methods are presented, together with theoretical, experimental results and comparisons to illustrate the validity of the proposed models. Moreover, we also show that by a slight modification of the associated Euler-Lagrange equations, we obtain well-behaved approximations and improved results.
Ten Ideas for Policymakers to Drive Digital Progress
Authors: Daniel Castro, Rob Atkinson
Venue: IEEE Internet Computing (2009)

Innovators continue to find new ways to use information technology (IT) to make our lives better. And looking forward, IT will continue to be a critical component of solutions to many social challenges. But policymakers must create the right environment for technological progress. This article offers ten guiding principals to create technology policy that spurs and sustains digital progress.
Trim-to-Coherence Fourier Transform
Authors: M. Böhm, M. Tasche, B. Seifert, F. Mitschke
Venue: Journal of Computational Physics (2009)

We introduce a discrete Fourier transform technique which extracts more spectral information from a given time series data set than conventional discrete Fourier transform (DFT). Valid information is obtained between the spectral bins of conventional DFT, scalloping error is greatly reduced, and amplitude and phase of Fourier components are more true to the process under study as with conventional DFT. We call the general idea Trim-to-Coherence Fourier Transform, and its particular embodiment 'Phase-Rotation Fourier Transform'. Treatment of the raw data is minimally invasive; e.g. there is no zero padding.
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning
Authors: Min-Ling Zhang, Zhi-Hua Zhou
Venue: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (2008)

Multi-instance multi-label learning (MIML) deals with the problem where each training example is associated with not only multiple instances but also multiple class labels. Previous MIML algorithms work by identifying its equivalence in degenerated versions of multi-instance multi-label learning. However, useful information encoded in training examples may get lost during the identification process. In this paper, a maximum margin method is proposed for MIML which directly exploits the connections between instances and labels. The learning task is formulated as a quadratic programming (QP) problem and implemented in its dual form. Applications to scene classification and text categorization show that the proposed approach achieves superior performance over existing MIML methods.
Optimizing Instruction Scheduling through Combined In-Order and O-O-O Execution in SMT Processors
Authors: Hui Wang, Rama Sangireddy, Sandeep Baldawa
Venue: IEEE Transactions on Parallel and Distributed Systems (2009)

The resource sharing nature of Simultaneous Multithreading (SMT) processors and the presence of long latency instructions from concurrent threads make the instruction scheduling window (IW), which is a primary shared component among key pipeline structures in SMT, a performance bottleneck. Due to the tight constraints on its physical size, the IW faces more severe pressure to handle the instructions from various threads while attempting to avoid resource monopolization by some low-ILP threads. It is particularly challenging to optimize the efficiency and fairness in IW utilization to fulfill the affordable performance by SMT under the shadow of long latency instructions. Most of the existing optimization schemes in SMT processors rely on the fetch policy to control the instructions that are allowed to enter the pipeline, while little effort is put to control the long latency instructions that are already located in the IW. In this paper, we propose streamline buffers to handle the long latency instructions that have already entered the pipeline and clog the IW, while the controlling fetch policies take time to react. Each streamline buffer extracts from IW and holds a chain of instructions from a thread that are stalled by dependency on a long latency load.
Exploring Building Blocks through Crossover
Authors: Zhenhua Li, Erik D. Goodman
Venue: Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence (2008)

For binary-represented problems with strong building block structure and low epistasis, one-point crossover success (both offspring are better or one is better and the other not worse) and crossover failure (both offspring are worse or one worse and one not better) indicate that a building block was produced or broken near the crossover point. Starting from such a crossover point, and flipping bits ahead of and behind the crossover point until the fitness no longer decreases, gives an indication of the building block extent. The resulting string can be extracted and used to replace the corresponding part of the best individual found to date, to increase its fitness. Experiments on test functions Royal Road R1 and R2, Holland's Royal Road Challenge function and the H-IFF function show that such a method could improve performance significantly on the first 3 functions but be trapped on the last, relative to a classical GA.
An undergraduate/graduate program in information systems
Authors: Charles J. Testa
Venue: Proceedings of the May 6-10, 1974, national computer conference and exposition (1974)

A recent report by the Association for Computing Machinery (ACM) Curriculum Committee on Computer Education for Management (CEM) offered recommendations for graduate professional programs in information systems development. The integration of the disciplines of computer science and business administration led to the development of a curriculum whose objectives are: "(1) to develop a systems point of view, (2) to provide a conceptual basis for the analysis of information systems in large complex organizations, (3) to develop an understanding of how to create an economically viable and technologically feasible computer-based system, and (4) to provide experience in the implementation of a complex information system." Thus, the program is designed to prepare students for a career as a designer and manager of computer-based information systems. It is assumed that students entering this graduate program will have sufficient preparation in basic mathematics, operations research, statistics, psychology, economics, and computer programming.
An efficient algorithm for minimum feedback vertex sets in rotator graphs
Authors: Chi-Jung Kuo, Chiun-Chieh Hsu, Hon-Ren Lin, Kung-Kuei Lin
Venue: Information Processing Letters (2009)

For a rotator graph with n! nodes, Hsu and Lin [C.C. Hsu, H.R. Lin, H.C. Chang, K.K. Lin, Feedback Vertex Sets in Rotator Graphs, in: Lecture Notes in Comput. Sci., vol. 3984, 2006, pp. 158-164] first proposed an algorithm which constructed a feedback vertex set (FVS) with time complexity O(n^n^-^3). In addition, they found that the size of the FVS is n!/3, which was proved to be minimum. In this paper, we present an efficient algorithm which constructs an FVS for a rotator graph in O(n!) time and also obtains the minimum FVS size n!/3. In other words, this algorithm derives the optimal result with linear time complexity in terms of the number of nodes in the rotator graph.
Exploiting user location for load balancing WLANs and improving wireless QoS
Authors: George Alyfantis, Stathes Hadjiefthymiades, Lazaros Merakos
Venue: ACM Transactions on Autonomous and Adaptive Systems (TAAS) (2009)

A &ldquo;Smart Spaces System&rdquo;, called MITOS, for improved user connectivity in large wireless LAN installations is proposed. MITOS extends the scope of resource management to the dynamic relocation of nomadic users: the system suggests to a user the best location to move to for obtaining a satisfactory quality of service level, when the controlling access point of its current location becomes congested. The system monitors the traffic and user location across the network, and formulates the appropriate relocation proposal urging specific users to move to better locations at reasonable distances. Two enhancements to the basic MITOS system are introduced for maintaining an almost uniform load level across the considered infrastructure: the first uses microeconomic concepts, while the second borrows game theoretic mechanisms from the Santa Fe Bar problem. Simulation results on the efficiency of the proposed schemes are provided.
Detecting, Assessing and Monitoring Relevant Topics in Virtual Information Environments
Authors: Jörg Ontrup, Helge Ritter, Sören W. Scholz, Ralf Wagner
Venue: IEEE Transactions on Knowledge and Data Engineering (2009)

The ability to assess the relevance of topics and related sources in information-rich environments is a key to success when scanning business environments. This paper introduces a hybrid system to support managerial information gathering. The system is made up of three components: (1) a hierarchical hyperbolic SOM for structuring the information environment and visualizing the intensity of news activity with respect to identified topics, (2) a spreading activation network for the selection of the most relevant information sources with respect to an already existing knowledge infrastructure, and (3) measures of interestingness for association rules as well as statistical testing facilitates the monitoring of already identified topics. Embedding the system by a framework describing three modes of human information seeking behavior endorses an active organization, exploration and selection of information that matches the needs of decision makers in all stages of the information gathering process. By applying our system in the domain of the hotel industry we demonstrate how typical information gathering tasks are supported. Moreover, we present an empirical study investigating the effectiveness and efficiency of the visualization framework of our system.