ppxscal/embedding_network huggingface dataset:
Network performance of distributed coevolutionary agents
Authors: R. Subbu, A. C. Sanderson
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

Innovations in software, networks, and database systems are enabling widely distributed organizations to integrate activities, share information, collaborate on decisions, and execute transactions. However, successful enterprise-wide collaboration is increasingly dependent on the availability of generalized decision-support tools that can efficiently access and utilize distributed information. This paper presents a basic theory and network-based performance evaluation of a class of coevolutionary algorithms that supports efficient planning in a distributed environment. Performance of these coevolutionary algorithms is evaluated in a distributed information architecture (coevolutionary virtual design environment) that supports integrated design-supplier-manufacturing planning. In this architecture, distributed evolutionary agents and mobile agents are principal entities that support a network-efficient exploration of planning alternatives in which successive populations systematically select superior planning alternatives.
Visualizing Attribute Interdependencies Using Mutual Information, Hierarchical Clustering, Multidimensional Scaling, and Self-organizing Maps
Authors: Derek L. Nazareth, Ehsan S. Soofi, Huimin Zhao
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Data pre-processing tends to be the most critical and time-consuming step during data mining processes. Understanding the interdependencies among the attributes is especially important for attribute selection and model structure design. Correlation measures, such as Pearson correlation coefficient, have been typically used to measure attribute dependencies. Correlation is useful for capturing linear dependency among quantitative attributes, and is invariant under linear transformations of the variables only. More recently, mutual information has been used to measure interdependencies among attributes measured in continuous scale. Mutual information is applicable to quantitative and categorical variables, captures any type of functional dependency between variables, and is invariant under one-to-one transformations. In this paper, we employ mutual information as a unified measure of interdependencies among attributes, by extending it to accommodate attributes measured in continuous and categorical scales. We further visualize the attribute interdependencies using a host of techniques, including hierarchical clustering, multidimensional scaling, and self-organizing maps. The use of mutual information permits identification of some salient interdependencies between attributes. We demonstrate the utility of the proposed methodology using real data mining applications.
Improving hash join performance through prefetching
Authors: Shimin Chen, Anastassia Ailamaki, Phillip B. Gibbons, Todd C. Mowry
Venue: ACM Transactions on Database Systems (TODS) (2007)

Hash join algorithms suffer from extensive CPU cache stalls. This article shows that the standard hash join algorithm for disk-oriented databases (i.e. GRACE) spends over 80&percnt; of its user time stalled on CPU cache misses, and explores the use of CPU cache prefetching to improve its cache performance. Applying prefetching to hash joins is complicated by the data dependencies, multiple code paths, and inherent randomness of hashing. We present two techniques, group prefetching and software-pipelined prefetching, that overcome these complications. These schemes achieve 1.29--4.04X speedups for the join phase and 1.37--3.49X speedups for the partition phase over GRACE and simple prefetching approaches. Moreover, compared with previous cache-aware approaches (i.e. cache partitioning), the schemes are at least 36&percnt; faster on large relations and do not require exclusive use of the CPU cache to be effective. Finally, comparing the elapsed real times when disk I/Os are in the picture, our cache prefetching schemes achieve 1.12--1.84X speedups for the join phase and 1.06--1.60X speedups for the partition phase over the GRACE hash join algorithm.
Model-Driven Engineering for Development-Time QoS Validation of Component-Based Software Systems
Authors: James H. Hill, Sumant Tambe, Aniruddha Gokhale
Venue: Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (2007)

Model-driven engineering (MDE) techniques are increasingly being used to address many of the development and operational lifecycle concerns of large-scale component-based systems. One such concern lacking significant research deals with the validation of quality-of- service (QoS) properties of component-based systems throughout their development lifecycle instead of waiting until system integration time, which is very late and can be detrimental to project schedules and costs. This paper describes our novel MDE-based solution to address this challenge. At the core of our solution approach are (1) a set of domain-specific modeling languages that allow us to mimic component "business logic," and (2) a generative programming framework that synthesizes empirical benchmarking code for system emulation and continuous QoS evaluation.
Strider Search Ranger: Towards an Autonomic Anti-Spam Search Engine
Authors: Yi-Min Wang, Ming Ma
Venue: Proceedings of the Fourth International Conference on Autonomic Computing (2007)

Search spammers use questionable search engine optimization techniques to promote their spam links into top search results. Large-scale spammers target commerce queries that they can monetize and attempt to spam as many top search results of those queries as possible. We model the large-scale search spam problem as that of defending against correlated attacks on search rankings across multiple keywords, and propose an autonomic anti-spam approach based on self-monitoring and selfprotection. In this new approach, search engines monitor and correlate their own search results of spammer-targeted keywords to detect large-scale spam attacks that have successfully bypassed their current anti-spam solutions. They then initiate self-protection through targeted patrol of spam-heavy domains, targeted hunting at the sources of successful spam, and strengthening of specific weakness in the ranking algorithms. We describe the Strider Search Ranger system which implements this new approach, and focus on its use to defend against an important class of search spam -- the redirection spam -- as a demonstration of the general concept. We evaluate the system by testing it against actual search results and show that it can detect useful spam patterns and eliminate a significant amount of spam for all three major search engines.
Constraint incorporation in optimization
Authors: Adam B. Levy
Venue: Mathematical Programming: Series A and B (2007)

Numerical methods for solving constrained optimization problems need to incorporate the constraints in a manner that satisfies essentially competing interests; the incorporation needs to be simple enough that the solution method is tractable, yet complex enough to ensure the validity of the ultimate solution. We introduce a framework for constraint incorporation that identifies a minimal acceptable level of complexity and defines two basic types of constraint incorporation which (with combinations) cover nearly all popular numerical methods for constrained optimization, including trust region methods, penalty methods, barrier methods, penalty-multiplier methods, and sequential quadratic programming methods. The broad application of our framework relies on addition and chain rules for constraint incorporation which we develop here.
Malloc(3) revisited
Authors: Poul-Henning Kamp
Venue: Proceedings of the annual conference on USENIX Annual Technical Conference (1998)

malloc (3) is one of the oldest parts of the C language environment and not surprisingly the world has changed a bit since it was first conceived. The fact that most UNIX kernels have changed from swap/segment to virtual memory/page based memory management has not been sufficiently reflected in the implementations of the malloc/free API. A new implementation was designed, written, tested and bench-marked with an eye on the workings and performance characteristics of modern Virtual Memory systems. It works OK.
Towards an abstract recursive agent
Authors: Adriana Giret, Vicente Botti
Venue: Integrated Computer-Aided Engineering (2004)

Current business trends, policy markets, production requirements, etc., have created the need for integrating pre-existing Multi Agent Systems (MAS). In the agent-specialized literature, we have found very little work about agent architectures and methodologies that allow us to carry out recursive and dynamic analysis, design, and implementation of MASs. Several difficult challenges for automated systems can be tackled by giving full meaning to the agent concept: adopting a recursive definition of agents and allowing for the dynamic creation of agents by the agents themselves. In this work, we propose a definition of an abstract recursive agent and an initial formalization of its behaviour in terms of the behaviour of its constituent agents.
Constrained random sensor selection for application-specific data gathering in wireless sensor networks
Authors: Wook Choi, S. K. Das, H. J. Choe
Venue: Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors (2005)

Due to the application-specific nature of sensor networks, sensing-quality control factors, such as coverage and delay, need to be parameterized in order to optimize energy conservation depending on the type of applications. An application-specific data gathering scheme is an example of such an application-specific algorithm design. In this scheme, only a minimum number of data reporters (sensors) are in each round randomly selected based on the desired sensing coverage (DSC) specified by the applications/users. In this paper, we investigate the use of the Poisson sampling technique which maintains a certain minimum distance between sampled points, to cope with an undesirable property that the selected data reporters may be located too closely. Then, based on this sampling technique we propose a constrained random sensor selection scheme, called CROSS. Inherently, the CROSS improves the spatial regularity of selected sensors, thus reducing the variance of the sensor covered area in each round. Consequently, the fidelity of meeting the DSC in each round improves. We present an algorithm to compute a desired minimum distance (DMD) to be forced between the selected sensors. Simulation results demonstrate that the DMD computed by our algorithm is almost optimal in terms of the improvement on the fidelity of meeting the DSC.
Clustering and aggregation of relational data with applications to image database categorization
Authors: Hichem Frigui, Cheul Hwang, Frank Chung-Hoon Rhee
Venue: Pattern Recognition (2007)

In this paper, we introduce a new algorithm for clustering and aggregating relational data (CARD). We assume that data is available in a relational form, where we only have information about the degrees to which pairs of objects in the data set are related. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings. CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. The cluster dependent relevance weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in subsequent steps of a learning system to improve its learning behavior. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. We represent the pairwise image dissimilarities by six different relational matrices that encode color, texture, and structure information.
Scheduling and simulation: how to upgrade distributed systems
Authors: Sameer Ajmani, Barbara Liskov, Liuba Shrira
Venue: Proceedings of the 9th conference on Hot Topics in Operating Systems - Volume 9 (2003)

Upgrading the software of long-lived distributed systems is difficult. It is not possible to upgrade all the nodes in a system at once, since some nodes may be down and halting the system for an upgrade is unacceptable. This means that different nodes may be running different software versions and yet need to communicate, even though those versions may not be fully compatible. We present a methodology and infrastructure that addresses these challenges and makes it possible to upgrade distributed systems automatically while limiting service disruption.
Peer-to-Peer Economics for Post Catastrophic Recovery
Authors: Yoshihiko Suko, Eiichi Morino, Susumu Takahashi, Takaaki Suzuki, Kenji Saito, Jun Murai
Venue: Proceedings of the 2007 International Symposium on Applications and the Internet Workshops (2007)

Post-catastrophic recovery is gaining more importance to human societies. This paper proposes use of a distributed autonomous economic medium to support recovery of communities after catastrophic events. It proposes a model in which everyone in the world can help each other as peers, instead of creating one's dependencies on others. The model applies peer-to-peer technologies on the Internet to achieve an environment for mutual help in the planetary scale.
Partially signal dependent slant transforms for multispectral classification
Authors: Sos S. Agaian, Khaled Tourshan, Joseph P. Noonan
Venue: Integrated Computer-Aided Engineering (2003)

The significant advance in high spatial and spectral resolution sensors technology on board remote sensing spacecraft has increased, by order of magnitude, the spectral bands or features dimensionality of datasets. The need arises, in remote sensing, for feature dimensionality reduction to offset the error in estimation of the class statistics. Principal component analysis (PCA) or KLT the image dependent transform is a useful technique in reducing the dimensionality of the datasets at a high costs of hardware and O(N$^3$) computational operations. This paper discusses the development of partially signal dependent classes of sequential slant Hadamard transforms that require elementary operations and can be used for multispectral classifications.
Robust exponential stability analysis of neural networks with multiple time delays
Authors: Zhanshan Wang, Huaguang Zhang, Wen Yu
Venue: Neurocomputing (2007)

This paper considers the robust stability of neural networks with multiple delays. Based on Lyapunov stability theory and linear matrix inequality technique, some new delay independent conditions are derived to guarantee the global robust exponential stability of the equilibrium point. Furthermore, the obtained results are generalized to the interval neural networks and bidirectional associative memory (BAM) neural networks. Two examples are used to show the effectiveness of the obtained results.
A Fast Algorithm for Balanced Graph Clustering
Authors: Mao Lin Huang, Quang Vinh Nguyen
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

Scalability problem is a long-lasting challenge for both information visualization and graph drawing communities. Available graph visualization techniques could perform well for small or medium size graphs but they are rarely able to handle very large and complex graphs. One of effective approach to solve this problem is to employ graph abstraction; that is to hierarchically partitioning the complete graph into a clustered graph. A graph visualization technique is then applied to display the abstract view of this clustered graph with partially displayed detail of one or a few sub-graphs where the user is currently focusing on. This reduces the complexity of display and makes it easier for users to interpret, perceive and navigate the large scale information. In this paper, we propose a graph clustering method which can quickly discover the community structure embedded in large graphs and partition the graph into densely connected sub-graphs. The proposed algorithm can not only run fast, but also achieve a consistent partitioning result in which a graph is divided into a set of clusters of the similar size in terms of their visual complexity and the number of nodes and edges. In addition, we also provide a mechanism to partition very dense graphs in which the number of edges is much larger than the number of nodes.
ARWOPS: A Framework for Searching Workflow Patterns Candidate to be Reused
Authors: Giovanni Di Dio
Venue: Proceedings of the Second International Conference on Internet and Web Applications and Services (2007)

This paper proposes a framework for searching Workflow Patterns which candidate themselves to be reused. Targets can be different: this tool can be used in order to allow to a planner of new business processes of use general solutions to the common problems, that are evidently complex to be individualised (e.g. for the complexity of the application domain), for improving the quality of the business processes, moreover, a planner of new Web Service composite can examine the repository of single Web Services and the further repository of notable Web Service composite. In fact the homogeneity among the solutions facilitates the organization, the realization and the control, increases the effectiveness and decreases the general costs (organisational, managerial, formative). A process composed of more phases is defined for this purpose: analysis of the processes of workflow, candidacy of the patterns, semantic validation and assignment of the single patterns. Therefore, this paper introduces equally the current state of the research in this area and a methodology for searching of patterns of workflow because I want individualise, from the analysis also of different workflow, parts of workflow that interact according to a determined model of interaction to get common objectives. Finally, this paper presents the ARWOPS (Automatic Reusable WOrkflow Patterns Search) Tool that implements the proposed methodology and its application to a realistic case study is shown.
Hierarchical WK-Recursive Topologies for Multicomputer Systems
Authors: Ronald Fernandes, Arkady Kanevsky
Venue: Proceedings of the 1993 International Conference on Parallel Processing - Volume 01 (1993)

We present two Hierarchical networks that use the WKR network as a basic module. The new net works retain the recursive structure of the WKR net work, at the same time have reduced diameter. Various properties of the new networks are discussed and are compared with those of other hierarchical networks.
A functionality-directed clustering technique for low-power MTCMOS design&mdash;computation of simultaneously discharging current
Authors: Ang-Chih Hsieh, Tzu-Teng Lin, Tsuang-Wei Chang, Tingting Hwang
Venue: ACM Transactions on Design Automation of Electronic Systems (TODAES) (2007)

Multithreshold CMOS (MTCMOS) is a circuit style that can effectively reduce leakage power consumption. Sleep transistor sizing is the key issue when a MTCMOS circuit is designed. If the size of sleep transistor is large enough, the circuit performance can surely be maintained but the area and dynamic power consumption of the sleep transistor may increase. On the other hand, if the sleep transistor size is too small, there will be significant performance degradation because of the increased resistance to ground. Previous approaches &lsqb;Kao et al. 1998; Anis et al. 2002&rsqb; to designing sleep transistor size are based mainly on mutually-exclusive discharge patterns. However, these approaches considered only the topology of a circuit (i.e., interconnections of nodes in the circuit-graph saving the functionality of node). We observed that any two possible simultaneously switching gates may not discharge at the same time in terms of functionality. Thus, we propose an algorithm to determine how to cluster cells to share sleep transistors, while taking both topology and functionality into consideration. Moreover, one placement refinement algorithm that takes clustering information into account will be presented. At the logic level, the results show that the proposed clustering method can achieve an average of 22&percnt; reduction in terms of the number of unit-size sleep transistors as compared to a method that does not consider functionality. At the physical level, two placement results are discussed. The first is produced by a traditional placement tool plus topology check (functionality check) for insertion of sleep transistors. It shows that the functionality check algorithm produces 9&percnt; less chip area as compared with the topology check algorithm. The second result is produced by a placement refinement algorithm where the initial placement is done in the first placement experiment. It shows that the placement refinement algorithm achieves 5&percnt; more reduction in area at the expense of 4&percnt; increase in wire length. Totally, around 14&percnt; reduction is achieved by utilizing the clustering information.
Computing Cross Associations for Attack Graphs and Other Applications
Authors: M. H. Heydari, L. Morales, C. O. Shields,  Jr., I. H. Sudborough
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Applications in information security, data mining, e-commerce, information retrieval and network management require the analysis of large graphs in order to discover homogeneous groupings of rows and columns, called cross associations. We show that finding an optimal cross association is NP-complete. Furthermore, we give a heuristic algorithm with an O(n^4) running time for finding good cross associations.
On the Throughput Capacity of Hybrid Wireless Networks Using an L-Maximum-Hop Routing Strategy
Authors: Yong Pei, Viraj S. Ambetkar, James W. Modestino, Xiaochun Wang
Venue: Wireless Personal Communications: An International Journal (2007)

The capacity of ad hoc network is constrained by the multi-hop relay traffic. We investigate a hybrid wireless network with base stations distributed in an ad hoc network in order to localize the ad hoc traffic. We propose to organize the ad hoc traffic based on the adjacency of each node itself instead of the base station, and propose an L-maximum-hop routing strategy in order to obtain improved spectrum efficiency. The throughput capacity of the resulting hybrid wireless network is studied. The paper also demonstrates that this type of hybrid wireless network is scalable.
Quadratically gated mixture of experts for incomplete data classification
Authors: Xuejun Liao, Hui Li, Lawrence Carin
Venue: Proceedings of the 24th international conference on Machine learning (2007)

We introduce quadratically gated mixture of experts (QGME), a statistical model for multi-class nonlinear classification. The QGME is formulated in the setting of incomplete data, where the data values are partially observed. We show that the missing values entail joint estimation of the data manifold and the classifier, which allows adaptive imputation during classifier learning. The expectation maximization (EM) algorithm is derived for joint likelihood maximization, with adaptive imputation performed analytically in the E-step. The performance of QGME is evaluated on three benchmark data sets and the results show that the QGME yields significant improvements over competing methods.