ppxscal/embedding_network huggingface dataset:
Destroy to save
Authors: Geoffroy de Clippel, Victor Naroditskiy, Amy Greenwald
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

We study the problem of how to allocate m identical items among n > m agents, assuming each agent desires exactly one item and has a private value for consuming it. We assume the items are jointly owned by the agents, not by one uninformed center, so an auction cannot be used to solve our problem. Instead, the agents who receive items compensate those who do not. This problem has been studied by others recently, and their solutions have modified the classic VCG mechanism. This approach guarantees strategy-proofness and allocative efficiency. Further, in an auction setting, VCG guarantees budget balance, because payments are absorbed by the auctioneer. In our setting, however, where payments are redistributed to the agents, some money must be burned in order to retain strategy-proofness. While strategy-proofness is necessary for truthful implementation, allocative efficiency (allocating the m items to those that desire them most), is not always an appropriate goal in our setting. Rather, we contend that maximizing social surplus is. In service of this goal, we study a class of mechanisms that may burn not only money but destroy items as well. Our key finding is that destroying items can save money, and hence lead to greater social surplus. More specifically, our first observation is that a mechanism is strategy-proof iff it admits a threshold representation. Given this observation, we restrict attention to specific threshold and payment functions for which we can numerically solve for an optimal mechanism. Whereas the worst-case ratio of the realized social surplus to the maximum possible is close to 1 when m = 1 and 0 when m = n -- 1 under the VCG mechanism, the best mechanism we find coincides with VCG when m = 1 but has a ratio approaching 1 when m = n -- 1 as n increases.
A gradient-based sequential radial basis function neural network modeling method
Authors: Wen Yao, Xiaoqian Chen, Wencai Luo
Venue: Neural Computing and Applications (2009)

Radial basis function neural network (RBFNN) is widely used in nonlinear function approximation. One of the key issues in RBFNN modeling is to improve the approximation ability with samples as few as possible, so as to limit the network&#x2019;s complexity. To solve this problem, a gradient-based sequential RBFNN modeling method is proposed. This method can utilize the gradient information of the present model to expand the sample set and refine the model sequentially, so as to improve the approximation accuracy effectively. Two mathematical examples and one practical problem are tested to verify the efficiency of this method.
A Declarative Debugger for Maude Functional Modules
Authors: R. Caballero, N. Martí-Oliet, A. Riesco, A. Verdejo
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

A declarative debugger for Maude functional modules, which correspond to executable specifications in membership equational logic, is presented. Starting from an incorrect computation, declarative debugging builds a debugging tree as a logical representation of the computation, that then is traversed by asking questions to an external oracle until the error is found. We summarize the construction of appropriate debugging trees for oriented equational and membership inferences, where all the nodes whose correctness does not need any justification have been collapsed. The reflective features of Maude allow us to generate and navigate the debugging tree of a Maude computation using operations in Maude itself; even the user interface of the declarative debugger can be specified in this way. We present the debugger's main features, such as two different strategies to traverse the debugging tree, use of a correct module to reduce the number of questions asked to the user, selection of trusted vs. suspicious statements by means of labels, and trusting of statements ''on the fly.''
Variational Bayes via propositionalized probability computation in PRISM
Authors: Taisuke Sato, Yoshitaka Kameya, Kenichi Kurihara
Venue: Annals of Mathematics and Artificial Intelligence (2008)

We propose a logic-based approach to variational Bayes (VB) via propositionalized probability computation in a symbolic-statistical modeling language PRISM. PRISM computes probabilities of logical formulas by reducing them to AND/OR boolean formulas called explanation graphs containing probabilistic ${\tt msw/2}$ atoms. We put Dirichlet priors on parameters of ${\tt msw/2}$ atoms and derive a variational Bayes EM algorithm that learns their hyper parameters from data. It runs on explanation graphs deduced from a program and a goal and computes probabilities in a dynamic programming manner in time linear in the size of the graphs. To show the genericity and effectiveness of Bayesian modeling by the proposed approach, we conducted two learning experiments, one with a probabilistic right-corner grammar and the other with a profile-HMM. To our knowledge, no previous report has been made of VB applied to these models.
Finding Key Bloggers, One Post At A Time
Authors: Wouter Weerkamp, Krisztian Balog, Maarten de Rijke
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

User generated content in general, and blogs in particular, form an interesting and relatively little explored domain for mining knowledge. We address the task of blog distillation: to find blogs that are principally devoted to a given topic, as opposed to blogs that merely happen to discuss the topic in passing. Working in the setting of statistical language modeling, we model the task by aggregating a blogger's blog posts to collect evidence of relevance to the topic and persistence of interest in the topic. This approach achieves state-of-the-art performance. On top of this baseline, we extend our model by incorporating a number of blog-specific features, concerning document structure, social structure, and temporal structure. These blog-specific features yield further improvements.
A domain engineering process for developing multi-agent systems product lines
Authors: Ingrid Nunes, Uirá Kulesza, Camila Nunes, Carlos J. P. Lucena
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Multi-agent Systems Product Lines (MAS-PLs) have emerged to integrate two promising trends of software engineering: agent-oriented software engineering and software product lines. In this paper, we propose a domain engineering process to develop MAS-PLs, built on top of agent-oriented and software product line approaches.
Genetic approach based design of dispersion-free optical fiber
Authors: Maan M. Shaker, Mahmood Sh. Majeed, Raid W. Daoud
Venue: Proceedings of the 8th WSEAS international conference on Electronics, hardware, wireless and optical communication (2009)

A new method based on genetic concepts is suggested to analyze and determine the transmission (propagation and dispersion) characteristics for conventional fibers as well as photonic crystal fibers. The power and the advantage of this work, lie in the fact that it could be applied to any optical fibers type with any refractive index profile and the method computes its proper and accurate propagation constant which leads to investigate the desired dispersion and minimizes it to the least possible value.
Managing Complexity: Insights, Concepts, Applications, 1st edition
Authors: Dirk Helbing
Venue: Understanding Complex Systems (2007)

Each chapter in Managing Complexity focuses on analyzing real-world complex systems and transferring knowledge from the complex-systems sciences to applications in business, industry and society. The interdisciplinary contributions range from markets and production through logistics, traffic control, and critical infrastructures, up to network design, information systems, social conflicts and building consensus. They serve to raise readers' awareness concerning the often counter-intuitive behavior of complex systems and to help them integrate insights gained in complexity research into everyday planning, decision making, strategic optimization, and policy. Intended for a broad readership, the contributions have been kept largely non-technical and address a general, scientifically literate audience involved in corporate, academic, and public institutions.
Understanding Users' Subject Interests in the Web Site Based on Their Usage of Its Content: A Novel Two-Phase Clustering Framework
Authors: Ahmad Ammari, Valentina Zharkova
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

In order to understand the behavior of website users, a deep analysis of content and usage data can reveal valuable knowledge about the main subjects these visitors are truly interested in. Preprocessing and clustering the highly unstructured content of web pages should be addressed very carefully in order to provide effective results. In this paper, a novel proposed two-phase self organizing feature map clustering framework to segment web users based on their subject interests in the diverse content of a University website is described. Also, the overall noise and dimensionality reduction of the sample web site content is properly addressed through the formulation of a comprehensive ten-step preprocessing procedure, which provided very promising experimental results when applied to the input web pages in the first phase of the proposed framework.
Assessment of cost/benefit of interfaces evaluation techniques
Authors: Eliane Regina de Almeida Valiati, Marcelo Soares Pimenta
Venue: Proceedings of the working conference on Advanced visual interfaces (2002)

Literature presents a considerable number of techniques that may be used in the process of evaluation of interfaces. Each technique has its own features, involves the employment of different resources, and allows the obtainment of distinct results depending on the way it is conducted. The present article aims at assessing, by means of a set of experiments, the costs and benefits found specifically in the application of three techniques: heuristic evaluation, user tests, and recommendation conformity inspection.
Computer algebra software for least squares and total least norm inversion of geophysical models
Authors: I. Bifulco, G. Raiconi, R. Scarpa
Venue: Computers Geosciences (2009)

We consider the model inversion problem that arises in geophysical sciences. Whether it is formulated in a deterministic or stochastic framework, it can be solved by minimizing an appropriate loss function with respect to unknown parameters. In such an optimization the efficiency of local minimization is crucial but the complexity of involved models often restricts the applicability of derivative based techniques. We consider the application of modern computer algebra programs to automatically compute needed derivatives and we propose a computer shell developed under the MATLAB^(R) environment that can be used to efficiently solve these problems. The software can be used by users that do not have specific skills in numerical and symbolic computation techniques. In order to show the general applicability of the proposed procedure and the software tool, we report the application to two different, but simple, ground deformation models and to two solution techniques: the classical nonlinear least squares, that is the most used approach, and the L"1 structured total least norm approach, which has proved to be very effective in dealing with data characterized by large outliers. Experimental results from synthetic and real data are shown.
But who will monitor the monitor?
Authors: David M. Rahman
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

Consider a group of individuals whose behavior is subject to moral hazard, and suppose that providing them with incentives requires a monitor to detect deviations. What about the monitor's deviations? I study mediated contracts and find that the monitor's deviations are effectively irrelevant. Hence, nobody needs to monitor the monitor. I also characterize exactly when such contracts can provide the right incentives for everyone. In doing so, several new characterizations of virtual implementation are derived.
Trust driven information sharing in peer-to-peer social networks: design and analysis
Authors: Arindam Mitra
Venue:  (2008)

This thesis presents a novel mechanism for trust based information sharing within P2P networks that leverage from social network relations. First, we present a trusted gossip algorithm that uses a trust based filter inlined within the gossip protocol to retard the spread of untrustworthy information while accelerating the spread of trustworthy information. Epidemic protocols such as gossip have proven to have many desirable properties for information sharing. However, trust is one of the issues that were yet to be examined with respect to these protocols. Our experiments show that the trusted gossip protocol is capable of dissemination with very low error probabilities and higher speedup. Trusted gossip takes into consideration that an information considered as fact by one node can be considered as rumor by another. This makes trusted gossip an ideal choice for dissemination without bias or censorship in applications where users do not actively seek information. Second, we propose a targeted gossip algorithm that refines the performance of the trusted gossip protocol. Targeted gossip uses a message targeting scheme that allows information to be gossiped among nodes who are most likely be interested in receiving it. Experimental results show that targeted gossip retains all the benefits of trusted gossip while significantly reducing various overheads incurred by the gossip process. Third, we examine the performance of the trusted gossip algorithms under node churning. Churning refers to the property of nodes leaving and rejoining the network repeatedly. Our experiments show that the trusted gossip algorithms continue to perform equally well at high churn rates as with a static network. We use real traces from representative social networking web sites to evaluate the performance of our algorithms. Finally, we extended the Bayesian trust framework from literature to develop an augmented Bayesian trust framework for trust estimation within the trusted gossip protocol. The aim is to include more information for computing trust values for nodes while keeping the overhead due to extra information minimal. Experiments show that the augmented Bayesian framework is able to compute trust values more accurately and faster than the base framework. The augmented framework can also be applied to other service domains like P2P resource sharing networks.
Computing entropy for z2-actions
Authors: Larry A. Pierce,  II
Venue:  (2008)

For a certain class of Z2 -actions, we provide a proof of a conjecture that the ratio of the Perron eigenvalues of the transfer matrices of the free boundary restrictions converge to the entropy of that action. Also, a novel method for computing the entropy of Z2 -actions is conjectured. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Determination of the steel casting cross-section with prescribed average temperature
Authors: Radosław Grzymkowski, Damian Słota
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

This paper presents the method of determination of the steel casting cross-section, in which average temperature was equal to a prescribed value. The method proposed here does not require evaluation of temperature distribution. On the basis of input data, a linear or non-linear equation is created (depending on the heat flux form on the region boundaries), which solution enabled determination of the cross-section.
Real-time fluid simulation with adaptive SPH
Authors: He Yan, Zhangye Wang, Jian He, Xi Chen, Changbo Wang, Qunsheng Peng
Venue: Computer Animation and Virtual Worlds (2009)

We present a new adaptive model for real-time fluid simulation based on Smoothed Particle Hydrodynamics (SPH) framework. Unlike traditional time-consuming SPH methods, our model can simulate fluid at a considerably faster speed without losing realism. In our model, we first introduce the non-uniform particle system and propose a generalized distance field function which considers not only geometrical complexity but also physical complexity of fluid body. And the new sampling rules for splitting and merging of particles are also presented. This can greatly reduce the computation time of the dynamic fluid simulation. Then, a new pressure state equation and an adaptive surface tension model are proposed to enhance the stability of the system and to make the free surface more realistic. To further accelerate the computation, a special fluid solver is designed and implemented using GPU. Various fluid phenomena like breaking wave and flood are simulated at real-time. Experiments demonstrate that our new adaptive model can greatly enhance the computation efficiency of fluid simulation compared with previous adaptive methods. Copyright &copy; 2009 John Wiley & Sons, Ltd. We present a new adaptive sampling model based on SPH framework for real-time fluid simulation by introducing a generalized distance field function in non-uniform particle system and building the splitting and merging rules for particles. To make the simulation more stable and visually continuous, we introduce a modified pressure state equation and a new surface tension model. Various fluid scenes including breaking wave and flood are simulated in real-time with realism.
Graph-based approaches to insider threat detection
Authors: William Eberle, Lawrence Holder
Venue: Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies (2009)

This work presents the use of graph-based approaches to discovering anomalous instances of structural patterns in data that represent entities, relationships and actions. Using the minimum description length (MDL) principle to first identify the normative pattern, the algorithms presented in this paper identify the three possible changes to a graph: modifications, insertions and deletions. Each algorithm discovers those substructures that match the closest to the normative pattern without matching exactly. As a result, this proposed approach searches for those activities that appear to match normal (or legitimate) transactions, but in fact are structurally different. After briefly presenting the three algorithms, we then show the usefulness of applying these graph theoretic approaches to discovering illegal activity for a simulated insider threat within a passport processing scenario.
Kernel-based skyline cardinality estimation
Authors: Zhenjie Zhang, Yin Yang, Ruichu Cai, Dimitris Papadias, Anthony Tung
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

The skyline of a d-dimensional dataset consists of all points not dominated by others. The incorporation of the skyline operator into practical database systems necessitates an efficient and effective cardinality estimation module. However, existing theoretical work on this problem is limited to the case where all d dimensions are independent of each other, which rarely holds for real datasets. The state of the art Log Sampling (LS) technique simply applies theoretical results for independent dimensions to non-independent data anyway, sometimes leading to large estimation errors. To solve this problem, we propose a novel Kernel-Based (KB) approach that approximates the skyline cardinality with nonparametric methods. Extensive experiments with various real datasets demonstrate that KB achieves high accuracy, even in cases where LS fails. At the same time, despite its numerical nature, the efficiency of KB is comparable to that of LS. Furthermore, we extend both LS and KB to the k-dominant skyline, which is commonly used instead of the conventional skyline for high-dimensional data.
Localised laser joining of glass to silicon with BCB intermediate layer
Authors: Qiang Wu, N. Lorenz, D. P. Hand
Venue: Microsystem Technologies (2009)

The use of a laser to provide localised heating is an ideal solution to the problem of packaging micro-electro-mechanical-systems (MEMS) whilst maintaining a low device temperature to avoid changes in temperature-sensitive materials in the device. In this paper we present localised laser bonding of glass to silicon (normally used as the MEMS substrate) by using a fibre-delivered high power laser diode array to cure an intermediate layer of the thermosetting polymer Benzocyclobutene (BCB). In our experiments, we use two techniques to realise localised heating: one is to use an axicon together with a conventional positive lens to generate a ring focus; the other is to use a scanning focused laser beam. In both cases localised cooling is required to confine the elevated temperatures to the bonding area. Finite Element (FE) simulation indicates that both techniques should keep the temperature in the centre of the package to approximately that of the ambient environment (300&#x00a0;K) during the process. However, experiments show that the temperature in the centre of the package rises to a value of around 500&#x00a0;K, likely due to poor contact between silicon and cooling sink. Experimentally, we confirmed that either technique can be used to obtain excellent bonding of glass to silicon with leak rate at a level of 10&#x2212;10&#x00a0;mbar&#x00a0;l&#x00a0;s&#x2212;1, whilst keeping the centre of device at a lower temperature.
Reliable H∞ control of a class of uncertain interconnected systems: an LMI approach
Authors: G. Pujol
Venue: International Journal of Systems Science (2009)

The aim of this work is to study the reliable control design problem for parameter-dependent interconnected systems in the presence of actuator failures. Moreover, the control ensures H∞ performance in front of L2 perturbations. A more practical model of actuators or control channel failures than outage is adopted, considering partial achievement. The continuous H∞ control problem is solved via elementary manipulations on linear matrix inequalities and the resulting control systems are robustly stable against plant uncertainties and failures.
Development of smart sensors system for machine fault diagnosis
Authors: Jong-Duk Son, Gang Niu, Bo-Suk Yang, Don-Ha Hwang, Dong-Sik Kang
Venue: Expert Systems with Applications: An International Journal (2009)

Machine fault diagnosis is a traditional maintenance problem. In the past, the maintenance using tradition sensors is money-cost, which limits wide application in industry. To develop a cost-effective maintenance technique, this paper presents a novel research using smart sensor systems for machine fault diagnosis. In this paper, a smart sensors system is developed which acquires three types of signals involving vibration, current, and flux from induction motors. And then, support vector machine, linear discriminant analysis, k-nearest neighbors, and random forests algorithm are employed as classifiers for fault diagnosis. The parameters of these classifiers are optimized by using cross-validation method. The experimental results show that smart sensor system has the similar performance for applying in intelligent machine fault diagnosis with reduced product cost. Developed smart sensors have feasibility to apply for intelligent fault diagnosis.