ppxscal/embedding_network huggingface dataset:
Abnormal BGP Routing Dynamics Detection by Sampling Approach in Decision Tree
Authors: Qitao Wu, Meiyan Wang
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

Because of BGP's critical importance as the de-facto Internet inter-domain routing protocol, accurate and quick detection of abnormal BGP routing dynamics is of fundamental importance to Internet security, where the costs of different errors are unequal. In such situation, cost-sensitive learning is a good solution. This paper studies the effect of both over-sampling and under-sampling in training cost-sensitive Decision Tree (C4.5). These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. The results suggest that the accuracy of the detection of abnormal BGP routing dynamics is applicable to BGP products. At the same time, we emphasize that this is a promising direction to improve security, availability, reliability and performance of Internet security by detecting and preventing abnormal BGP routing dynamics traffic.
A PSO --- Line Search Hybrid Algorithm
Authors: Ximing Liang, Xiang Li, M. Fikret Ercan
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Recently, Particle Swarm Optimization (PSO), gained vast attention and applied to variety of engineering optimization problems because of its simplicity and efficiency. The performance of the PSO algorithm can be further improved by hybrid techniques. There are numerous hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristic algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and standard PSO (S-PSO). The performance of the proposed hybrid algorithm, examined through six typical nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms S-PSO.
Equivalence of Modified Mann and Multi-step Noor Iteration Methods with Errors
Authors: Ren-Xing Ni
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

The equivalence of the strong convergence between the modified Mann and multi-step Noor iterations with errors is proved for uniformly Lipchitzian generalized strongly successively asymptotically Q-pseudocontractive operators in arbitrary real Banach space. These results generalize those recent ones due to Rhoades and Soltuz in 2003 and 2004 by extending to the more generalized modified multi-step Noor iterations with errors and the most general class of operators, and hence improve the corresponding results of the references given in this paper by providing the equivalences of convergence between all of these iteration schemes for any initial points x1, u1 in arbitrary real Banach space. Our proof methods are quite different and simple.
Many-pairs mutual information for adding structure to belief propagation approximations
Authors: Arthur Choi, Adnan Darwiche
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

We consider the problem of computing mutual information between many pairs of variables in a Bayesian network. This task is relevant to a new class of Generalized Belief Propagation (GBP) algorithms that characterizes Iterative Belief Propagation (IBP) as a polytree approximation found by deleting edges in a Bayesian network. By computing, in the simplified network, the mutual information between variables across a deleted edge, we can estimate the impact that recovering the edge might have on the approximation. Unfortunately, it is computationally impractical to compute such scores for networks over many variables having large state spaces. So that edge recovery can scale to such networks, we propose in this paper an approximation of mutual information which is based on a soft extension of d-separation (a graphical test of independence in Bayesian networks). We focus primarily on polytree networks, which are sufficient for the application we consider, although we discuss potential extensions of the approximation to general networks as well. Empirically, we show that our proposal is often as effective as mutual information for the task of edge recovery, with orders of magnitude savings in computation time in larger networks. Our results lead to a concrete realization of GBP, admitting improvements to IBP approximations with only a modest amount of computational effort.
Clipped noisy images: Heteroskedastic modeling and practical denoising
Authors: Alessandro Foi
Venue: Signal Processing (2009)

We study the denoising of signals from clipped noisy observations, such as digital images of an under- or over-exposed scene. From a precise mathematical formulation and analysis of the problem, we derive a set of homomorphic transformations that enable the use of existing denoising algorithms for non-clipped data (including arbitrary denoising filters for additive independent and identically distributed, i.i.d., Gaussian noise). Our results have general applicability and can be ''plugged'' into current filtering implementations, to enable a more accurate and better processing of clipped data. Experiments with synthetic images and with real raw data from charge-coupled device (CCD) sensor show the feasibility and accuracy of the approach.
Sensitivity Based Generalization Error for Supervised Learning Problems with Application in Feature Selection
Authors: Daniel S. Yeung
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Generalization error model provides a theoretical support for a classifier's performance in terms of prediction accuracy. However, existing models give very loose error bounds.This explains why classification systems generally rely on experimental validation for their claims on prediction accuracy. In this talk we will revisit this problem and explore the idea of developing a new generalization error model based on the assumption that only prediction accuracy on unseen points in a neighbourhood of a training point will be considered, since it will be unreasonable to require a classifier to accurately predict unseen points "far away" from training samples. The new error model makes use of the concept of sensitivity measure for multiplayer feedforward neural networks (Multilayer Perceptrons or Radial Basis Function Neural Networks). The new model will be applied to the feature reduction problem for RBFNN classifiers. A number of experimental results using datasets such as the UCI, the 99 KDD Cup, and text categorization, will be presented.
Wet releasing and stripping SU-8 structures with a nanoscale sacrificial layer
Authors: Peihong Wang, Katsuhiko Tanaka, Susumu Sugiyama, Xuhan Dai, Xiaolin Zhao
Venue: Microelectronic Engineering (2009)

This paper presents a new method of wet releasing SU-8 structures using very thin Omnicoat^(TM) as sacrificial layer and NMD-3 developer as releasing solution. Four-inch sized SU-8 structures with different pattern and thickness of 65@mm have been successfully released from silicon substrate by this method. Experiments show that Omnicoat^(TM) layer is very helpful to strip SU-8 molds in which metal structures are electroplated. SU-8 can be removed directly using remover if there is an Omnicoat^(TM) layer between the substrate and SU-8 and the electroplated structures is with large dimension. In the case of electroplated structures with small feature size, SU-8 can be removed by combining wet stripping and plasma etching. Nickel micro coils with linewidth of 10@mm and aspect ratio of 4.1 have been fabricated. The analysis about the influence of Omnicoat^(TM) layer's thickness on integrity of SU-8 micro coils is also given in our work.
Towards synthesizing optimal coordination modules for distributed agents
Authors: Manh Tung Pham, Kiam Tian Seow
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

In a discrele-event framework, we define the concept of a coordinable language and show that it is the necessary and sufficient existence condition of coordination modules for distributed agents to achieve conformance to a given interagent constraint language. We also present a synthesis algorithm to compute near optimal coordination modules.
The Effects of an Anti-glare Sleeve Installed on Fluorescent Tube Lamps on Glare and Reading Comfort
Authors: Shiaw-Tsyr Uang, Cheng-Li Liu, Mali Chang
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction (2009)

Our previous study has demonstrated the benefits of a reflective sleeve to redirect lighting and to enhance luminous intensity of fluorescent tube lamps in certain light projecting angles. A reflective sleeve is composed of a plastic reflector and a transparent refractor. However, the intensive centralized lighting may increase the possibilities of producing glare. In this study, the transparent refractor of the sleeve is replaced with a diffuser to compose an anti-glare sleeve. This study adopts measurement, optical software simulation, and experiment methods to investigate the effects of an anti-glare sleeve on redirecting lighting and reducing glare. The results demonstrate that luminous intensity towards viewing objects of a fluorescent tube lamp enhances after adopting an anti-glare sleeve. In addition, software simulation indicates an anti-glare sleeve increases light uniformity and reduces glare. The subjective evaluation also shows that florescent tube lamps with anti-glare sleeves produce less light reflection on various papers and more comfortable reading.
Energy-Efficient Circular Sector Sensing Coverage Model for Wireless Sensor Networks
Authors: Peter Soreanu, Zeev Volkovich
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

Reducing the energy consumption in Wireless Sensor Networks (WSN) is an important goal, sought to extend the life-expectancy of the networks. One of the main problems in actual implementation is their inherently limited life-span, due to the (usually) not field-replaceable energy source. The energy consumption is due to the data and management communication, messages, sensing, processing (and storage) activities of the motes. The sensing may be local, e.g. temperature, radiation, humidity or similar physical measurement. There are a lot of applications, civilian or military alike, which implement some remote discovery, localization or tracking activities. In this case, the active sensing is performed mainly by emitting light, electromagnetic or sound waves. Obviously this imposes a heavy burden on the limited energy resources available to the mote, shortening his life and consequently the life of the WSN itself. Previously published sensing models use a circular coverage area around the motes, with fixed (unit-disk) or variable radius. Our work proposes a more energy-efficient sensing model, based on circular sectors with variable angles and radii, negotiated between the motes, as a function of their current energy capabilities. The performance of this model was checked vs. previously published schemes, using a proprietary simulation program, developed specifically for this purpose. The paper presents the results of simulations, showing significant energy savings. The obtained data confirm the viability and the advantages of this new sensing model. The possible implementation of such a WSN will significantly improve the energy-related performance of the WSN, allowing the development of new applications and improving the performance of existing ones.
A Color-Based Interest Operator
Authors: Marta Penas, Linda G. Shapiro
Venue: Proceedings of the 15th International Conference on Image Analysis and Processing (2009)

In this paper we propose a novel interest operator robust to photometric and geometric transformations. Our operator is closely related to the grayscale MSER but it works on the HSV color space, as opposed to the most popular operators in the literature, which are intensity based. It combines a fine and a coarse overlapped quantization of the HSV color space to find maximally stable extremal regions on each of its components and combine them into a final set of regions that are useful in images where intensity does not discriminate well. We evaluate the performance of our operator on two different applications: wide-baseline stereo matching and image annotation.
Detecting Conflicts in ABAC Policies with Rule-Reduction and Binary-Search Techniques
Authors: Cheng-chun Shu, Erica Y. Yang, Alvaro E. Arenas
Venue: Proceedings of the 2009 IEEE International Symposium on Policies for Distributed Systems and Networks (2009)

Attribute-based access control (ABAC) policies are effective and flexible in governing the access to information and resources in open distributed computing environments. However, ABAC policy rules are often complex making them prone to conflicts. This paper proposes an optimized method to detect the conflicts between statistically conflicting rules in an ABAC policy. This method includes two optimization techniques: rule reduction and binary-search. The first technique reduces the rules into a set of compact, semantically equivalent rules through removing redundant information among the rules. The binary-search technique is then applied to discover the conflicts among them.
Scaling up a NLU system from text to dialogue understanding
Authors: R. Delmonte, A. Bristot, G. Voltolina, Vincenzo Pallotta
Venue: Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing (2009)

In this paper we will present work carried out to scale up the system for text understanding called GETARUNS, and port it to be used in dialogue understanding. We will present the adjustments we made in order to cope with transcribed spoken dialogues like those produced in the ICSI Berkely project. In a final section we present preliminary evaluation of the system on non-referential pronominals individuation.
Driving and Situation Awareness: A Cognitive Model of Memory-Update Processes
Authors: Josef F. Krems, Martin R. Baumann
Venue: Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009 (2009)

Safe driving requires a mental representation of objects and situational features relevant to the driver's behavior. This includes the generation of predictions of how the situation will develop in the near future. These processes are summarized under the term "situation awareness", previously proposed in the aviation domain. By now the cognitive mechanisms underlying situation awareness are far from being understood properly. In this paper we propose a theory that is based on results from studies in language understanding [1] and attention [2] and that is applied to the driving context. Mechanisms for the construction of a situation model and for the selection of actions are outlined. Finally, predictions of the model concerning the effect of experience, relevance, and criticality on the drivers´ mental representation are investigated. In a second study the effects of cognitive tasks on predicting events in traffic are focused.
An Efficient Discussion for the Earthquake Searching Problem
Authors: Wang Qi, Liu Xiaoshan, Cao Nanbin
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

Wenchuan earthquake is the most serious natural disaster in Chinese history. In this emergency the important thing is to take effective measures to avoid casualties. To bring proper measures into effect, we must set down the searching strategy rapidly according to different situation, which the focus is to arrange the rescue pattern and to select the accurate searching route. Here we present a simple earthquake searching model and carry out preliminary study on this model. The focus is to minimize the maximal searching time of all searchers, which can be looked upon as an optimal problem. Here we present a bulldozer model to arrange the route for each member and obtain an efficient result.
Pedagogical second-order λ-calculus
Authors: Loïc Colson, David Michel
Venue: Theoretical Computer Science (2009)

The present work introduces the notion of pedagogical natural deduction systems, i.e. natural deduction systems for which hypotheses occurring in a proof must be motivated by an example. In formal terms, we replace the rule (Hyp): by the rule (P-Hyp): where @s denotes a substitution which replaces all variables of @C with an example. This substitution is called the motivation of @C. These systems are in essence negationless. In the present paper, we study second order propositional calculus, since it is a simple non-trivial natural deduction system in which negation can be defined. We present a Curry-Howard version of pedagogical second-order propositional calculus, i.e.@l-calculus bearing the pedagogical constraint. We establish that this system has the usual properties of typed calculi (subject reduction and strong normalization) and that it can type all second-order terms of @l@m-calculus using a CPS translation. Furthermore, the main novelty is that all functions are useful: each polymorphic function has an instance which can be applied to a closed term.
A Certified Data Race Analysis for a Java-like Language
Authors: Frédéric Dabrowski, David Pichardie
Venue: Proceedings of the 22nd International Conference on Theorem Proving in Higher Order Logics (2009)

A fundamental issue in multithreaded programming is detecting data races. A program is said to be well synchronised if it does not contain data races w.r.t. an interleaving semantics. Formally ensuring this property is central, because the java Memory Model then guarantees that one can safely reason on the interleaved semantics of the program. In this work we formalise in the coq proof assistant a java bytecode data race analyser based on the conditional must-not alias analysis of Naik and Aiken. The formalisation includes a context-sensitive points-to analysis and an instrumented semantics that counts method calls and loop iterations. Our java-like language handles objects, virtual method calls, thread spawning and lock and unlock operations for threads synchronisation.
Towards a Policy-Driven Framework for Managing Service Dependability
Authors: Jorge Bernal Bernabe, Juan M. Marín Perez, Daniel J. Martínez Manzano, Manuel Gil Perez, Antonio F. Gómez Skarmeta
Venue: Proceedings of the 2009 Second International Conference on Dependability (2009)

Many critical activities rely on the correct and uninterrupted operation of networked Computer Information Systems (CIS). Such systems are however exposed to many different kinds of risk, and thus many researches have been taking place for enabling them to perform self monitoring and self healing to maintain their operation over time as specified by domain policies. This capability is often referred to as dependability.The DESEREC project has defined a tiered architecture as a policy based framework to increase the dependability of existing and new networked CIS, using technology-independant information which is translated at runtime to suit the managed components.
Bridging lexical gaps between queries and questions on large online Q&A collections with compact translation models
Authors: Jung-Tae Lee, Sang-Bum Kim, Young-In Song, Hae-Chang Rim
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

Lexical gaps between queries and questions (documents) have been a major issue in question retrieval on large online question and answer (Q&A) collections. Previous studies address the issue by implicitly expanding queries with the help of translation models pre-constructed using statistical techniques. However, since it is possible for unimportant words (e.g., non-topical words, common words) to be included in the translation models, a lack of noise control on the models can cause degradation of retrieval performance. This paper investigates a number of empirical methods for eliminating unimportant words in order to construct compact translation models for retrieval purposes. Experiments conducted on a real world Q&A collection show that substantial improvements in retrieval performance can be achieved by using compact translation models.
CL-SNA: social network analysis with Lisp
Authors: Mehmet Gencer, Coskun Gunduz, Vehbi Sinan Tunalioglu
Venue: Proceedings of the 2007 International Lisp Conference (2007)

Social relations have been a research interest in many areas of social sciences. In sociology, for example, individuals up in the power hierarchy in a group can be determined quantitatively by looking at the number of direct relations to others in the group[1]. Similarly, in management and economics, alliance relationships between firms are examined carefully to explain phenomenon such as innovation and market leadership[6]. Similar problems were encountered in Anthropology, Zoology, and many other fields. In most studies regarding social relations, one must look into the content of social relations in addition to their structure, an endeavor which demands ethnographic methods. Nevertheless, analysis of the structure alone has been a powerful tool which not only enables quantitative assessment of social structures, but also prompts elaboration of interpretive process.
Bipolar Fuzzy Mathematical Morphology for Spatial Reasoning
Authors: Isabelle Bloch
Venue: Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing (2009)

Bipolarity is an important feature of spatial information, involved in the expressions of preferences and constraints about spatial positioning, or in pairs of "opposite" spatial relations such as left and right. Imprecision should also be taken into account, and fuzzy sets is then an appropriate formalism. In this paper, we propose to handle such information based on mathematical morphology operators, extended to the case of bipolar fuzzy sets. The potential of this formalism for spatial reasoning is illustrated on a simple example in brain imaging.
Policies for Self Tuning Home Networks
Authors: Dimosthenis Pediaditakis, Leonardo Mostarda, Changyu Dong, Naranker Dulay
Venue: Proceedings of the 2009 IEEE International Symposium on Policies for Distributed Systems and Networks (2009)

A home network (HN) is usually managed by a user who does not possess knowledge and skills required to perform management tasks. When abnormalities are detected, it is desirable to let the network tune itself under the direction of certain policies. However, self tuning tasks usually require coordination between several network components and most of the network management policies can only specify local tasks. In this paper, we propose a state machine based policy framework to address the problem of fault and performance management in the context of HN. Policies can be specified for complex management tasks as global state machines which incorporate global system behavior monitoring and reactions. We demonstrate the policy framework through a case study in which policies are specified for dynamic selection of frequency channel in order to improve wireless link quality in the presence of RF interference.