ppxscal/embedding_network huggingface dataset:
Risk assessment of drilling and completion operations in petroleum wells using a monte carlo and a neural network approach
Authors: Dennis Kerr Coelho, Mauro Roisenberg, Paulo J. de Freitas Filho, Carlos Magno C. Jacinto
Venue: Proceedings of the 37th conference on Winter simulation (2005)

This paper intends to show how two different methodologies, a Monte Carlo simulation method and a connectionist approach can be used to estimate the total time assessment in drilling and completion operations of oil wells in deep waters. The former approach performs a Monte Carlo simulation based on data from field operations. In the later one, correlations and regularities in parameters selected from a petroleum company database were detected using a competitive neural network, and then, a feedforward neural network was trained to estimate the average, standard deviation and total time wasted in the accomplishment of the well. At the end, the results obtained by both models are compared. The analyst could evaluate the precision of the estimated total-time based on geometric and technological parameters provided by the neural network tool, with those supplied by the traditional Monte Carlo method based on data of the drilling and completion operations.
Adaptive wavelet neural network for prediction of hourly NOX and NO2 concentrations
Authors: Zhiguo Zhang, Ye San
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Adaptive neural network is a powerful tool for prediction of air pollution abatement scenarios. But it is often difficult to avoid overfit during the training of adaptive neural network. In this paper, based on the wavelet theory, a new algorithm is proposed to improve the generalization of adaptive neural network during on-line learning. The new algorithm trains adaptive wavelet neural network to model hourly NOx and NO2 concentrations of variance of emission sources. Results show that the new algorithm improves the generalization and the convergence velocity of adaptive wavelet neural network during on-line learning. The simulations also illustrate that adaptive wavelet neural network is capable of resolving variance of emission sources.
Polynomial association rules with applications to logistic regression
Authors: Szymon Jaroszewicz
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

A new class of associations (polynomial itemsets and polynomial association rules) is presented which allows for discovering nonlinear relationships between numeric attributes without discretization. For binary attributes, proposed associations reduce to classic itemsets and association rules. Many standard association rule mining algorithms can be adapted to finding polynomial itemsets and association rules. We applied polynomial associations to add non-linear terms to logistic regression models. Significant performance improvement was achieved over stepwise methods, traditionally used in statistics, with comparable accuracy.
Lightweight deployment-aware scheduling for wireless sensor networks
Authors: Kui Wu, Yong Gao, Fulu Li, Yang Xiao
Venue: Mobile Networks and Applications (2005)

Wireless sensor networks consist of a large number of tiny sensors that have only limited energy supply. One of the major challenges in constructing such networks is to maintain long network lifetime as well as sufficient sensing areas. To achieve this goal, a broadly-used method is to turn off redundant sensors. In this paper, the problem of estimating redundant sensing areas among neighbouring wireless sensors is analysed. We present simple methods to estimate the degree of redundancy without the knowledge of location or directional information. We also provide tight upper and lower bounds on the probability of complete redundancy and on the average partial redundancy. With random sensor deployment, our analysis shows that partial redundancy is more realistic for real applications, as complete redundancy is expensive, requiring up to 11 neighbouring sensors to provide a 90 percent chance of complete redundancy. Based on the analysis, we propose a scalable Lightweight Deployment-Aware Scheduling (LDAS) algorithm, which turns off redundant sensors without using accurate location information. Simulation study demonstrates that the LDAS algorithm can reduce network energy consumption and provide desired QoS requirement effectively.
Performance evaluation of distributed iSCSI RAID
Authors: Xubin (Ben) He, Praveen Beedanagari, Dan Zhou
Venue: Proceedings of the international workshop on Storage network architecture and parallel I/Os (2003)

iSCSI is a newly emerging protocol with the goal of implementing the storage area network (SAN) technology over TCP/IP, which brings economy and convenience whereas it also raises performance and reliability issues. This paper identifies the performance bottleneck of iSCSI, and then proposes a distributed iSCSI RAID to improve the performance by stripping data among iSCSI targets (S-iRAID) and improve the reliability by using rotated parity for data blocks (P-iRAID). Numerical results using popular benchmark have shown dramatic performance gain. S-iRAID improves the average throughput from 11.7MB/s to 46.1 MB/s by striping data among only three iSCSI targets. S-iRAID and P-iRAID can speed up the iSCSI performance by a factor of up to 6.6 and 2.17, respectively.
Active Learning to Maximize Area Under the ROC Curve
Authors: Matt Culver, Deng Kun, Stephen Scott
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.
Small operating systems for the 1990s
Authors: N. Solntseff
Venue: ACM SIGSMALL Newsletter (1981)

In the course of 24 years' association with computers, I have seen the rise and fall of several alternate ways of looking at operating systems. 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
Enhancing Tutoring Intelligence Using Knowledge Discovery Techniques
Authors: Engelbert Mephu Nguifo, Roger Nkambou
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

In this paper, we present an approach to enhance tutoring knowledge using a promising approach of data and knowledge discovery based on pattern sequence recognition. We show how the approach is used to discover new meta knowledge and rules in a given domain which then extend domain knowledge and serve as problem space allowing the intelligent tutoring agent to guide learners in problem solving situations.
Memory Scalability Evaluation of the Next-Generation Intel Bensley Platform with InfiniBand
Authors: Matthew J. Koop, Wei Huang, Abhinav Vishnu, Dhabaleswar K. Panda
Venue: Proceedings of the 14th IEEE Symposium on High-Performance Interconnects (2006)

As multi-core systems gain popularity for their increased computing power at low-cost, the rest of the architecture must be kept in balance, such as the memory subsystem. Many existing memory subsystems can suffer from scalability issues and show memory performance degradation with more than one process running. To address these scalability issues, Fully-Buffered DIMMs have recently been introduced. In this paper we present an initial performance evaluation of the next-generation multi-core Intel platform by evaluating the FB-DIMM-based memory subsystem and the associated InfiniBand performance. To the best of our knowledge this is the first such study of Intel multi-core platforms with multi-rail InfiniBand DDR configurations. We provide an evaluation of the current-generation Intel Lindenhurst platform as a reference point. We find that the Intel Bensley platform can provide memory scalability to support memory accesses by multiple processes on the same machine as well as drastically improved inter-node throughput over InfiniBand. On the Bensley platform we observe a 1.85 times increase in aggregate write bandwidth over the Lindenhurst platform. For inter-node MPI-level benchmarks we show bi-directional bandwidth of over 4.55 GB/sec for the Bensley platform using 2 DDR InfiniBand Host Channel Adapters (HCAs), an improvement of 77% over the current generation Lindenhurst platform. The Bensley system is also able to achieve a throughput of 3.12 million MPI messages/sec in the above configuration.
High Reliability in Autonomous Decentralized IC Card Ticket System
Authors: Akio Shiibashi, Fumiaki Mashiba, Kinji Mori
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

The Automatic Fare Collection System requires both high performance and high reliability. The IC (Integrated Circuit) Card Ticket System has been developed in order to improve the passengers' satisfaction and to reduce the maintenance costs. However, high performance on wireless communications lowers reliability due to the passengers' behaviors. The Autonomous Decentralized Architecture has been implemented as a solution. This paper introduces the "Autonomous Decentralized Data Consistency Technology" for high reliability and evaluates the effectiveness by the functional reliability. The results show the functional reliability is depends on the number of the application of this technology and that the decentralized models are, under particular conditions, competitive to the centralized one. These conditions are made use of the practical "Suica" system at East Japan Railway Company and the system is running satisfactory, without any fatal errors.
Analysis of two existing and one new dynamic programming algorithm for the generation of optimal bushy join trees without cross products
Authors: Guido Moerkotte, Thomas Neumann
Venue: Proceedings of the 32nd international conference on Very large data bases (2006)

Two approaches to derive dynamic programming algorithms for constructing join trees are described in the literature. We show analytically and experimentally that these two variants exhibit vastly diverging runtime behaviors for different query graphs. More specifically, each variant is superior to the other for one kind of query graph (chain or clique), but fails for the other. Moreover, neither of them handles star queries well. This motivates us to derive an algorithm that is superior to the two existing algorithms because it adapts to the search space implied by the query graph.
Discriminant Feature Fusion Strategy for Supervised Learning
Authors: Jun-Bao Li, Shu-Chuan Chu, Jung-Chou Harry Chang, Jeng-Shyang Pan
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.
The Role of Large-Scale Memory Organization in the Mismatch Negativity Event-Related Brain Potential
Authors: István Winkler, Erich Schröger, Nelson Cowan
Venue: Journal of Cognitive Neuroscience (2001)

The mismatch negativity (MMN) component of event-related brain potentials is elicited by infrequent changes in regular acoustic sequences even if the participant is not actively listening to the sound sequence. Therefore, the MMN is assumed to result from a preattentive process in which an incoming sound is checked against the automatically detected regularities of the auditory sequence and is found to violate them. For example, presenting a discriminably different (deviant) sound within the sequence of a repetitive (standard) sound elicits the MMN. In the present article, we tested whether the memory organization of the auditory sequence can affect the preattentive change detection indexed by the MMN. In Experiment 1, trains of six standard tones were presented with a short, 0.5-sec stimulus onset asynchrony (SOA) between tones in the train. This was followed by a variable SOA between the last standard and the deviant tone (the "irregular presentation" condition). Of 12 participants displaying an MMN at the 0.5-sec predeviant SOA, it was elicited by 11 with the 2-sec predeviant SOA, in 5 participants with the 7-sec SOA, and in none with the 10-sec SOA. In Experiment 2, we repeated the 7-sec irregular predeviant SOA condition, along with a "regular presentation" condition in which the SOA between any two tones was 7 sec. MMN was elicited in about half of the participants (9 out of 16) in the irregular presentation condition, whereas in the regular presentation condition, MMN was elicited in all participants. These results cannot be explained on the basis of memory-strength decay but can be interpreted in terms of automatic, auditory preperceptual grouping principles. In the irregular presentation condition, the close grouping of standards may cause them to become irrelevant to the mismatch process when the deviant tone is presented after a long silent break. Because the MMN indexes preattentive auditory processing, the present results provide evidence that large-scale preperceptual organization of auditory events occurs despite attention being directed away from the auditory stimuli.
Computational Quantification of Trust Updates
Authors: Arthur Ramer
Venue: Proceedings of the International Workshop on on Integrating AI and Data Mining (2006)

Lemmy People have become slaves to probability. Dickson They just obey logical orders. Alphaville. (Jean-Luc Godard 1965) A computational model for expressions of trust values is outlined. It is based on the proposal by Jonker and Treur to base trust updates on reported experiences. The model handles arbitrary sequences of experience inputs; its such updates are fully commutative and associative. It satisfies all the axiomatic properties suggested for the trust values. Trust is interpreted as family of probabilistic beliefs on the space of possible experience reports. Expansion of the space of reports gives rise to inverse conditioning of probability distributions and thus of trust values. Belief and trust changes follow the AGM structure. Inverse conditioning is put into effect through a suitable application of maximum entropy principles.
Motion Patterns: High-Level Representation of Natural Video Sequences
Authors: Duangmanee Putthividhya, Te-Won Lee
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

This work investigates the use of nonlinear dependencies in natural image sequence statistics to learn higher-order structures in natural videos. We propose a two-layer model that learns variance correlation between linear ICA coefficients and present a novel nonlinear representation of natural videos. The first layer performs a linear mapping from pixel values to ICA coefficients. In doing so, the spatiotemporal dynamics in natural videos are decomposed into a set of bases each encoding "independent motion." By assuming that the nonlinear dependency of ICA coefficients takes the form of variance correlation, the second layer learns the joint distribution of ICA sources that captures how these independent bases co-activate. Experimental results show that the abstract representation correspond to various activation patterns of bases with similar motion, hence the term "motion patterns." Our model offers a novel description of higher-order structures in natural videos. We illustrate the usefulness of the proposed representation in video segmentation and denoising tasks.
On the complete axiomatization for prefix iteration modulo observation congruence
Authors: Taolue Chen, Tingting Han, Jian Lu
Venue: Acta Cybernetica (2006)

Prefix iteration is a variation on the original binary version of the Kleene star operation P*Q, obtained by restricting the first argument to be an atomic action. Aceto and Ingólfsdóttir provided an axiom system for observation congruence over basic CCS with prefix iteration. However hitherto the only direct completeness proof given for such a system is very long and technical. In this paper, we provide a new proof for the completeness of the axiom system in [3], which is a considerable simplification comparing to the original proof. Thus the open problem to find a direct completeness proof is closed.
Extending the Selective MPEG Encryption Algorithm PVEA
Authors: Li Weng, Karel Wouters, Bart Preneel
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

Currently, the requirements on MPEG encryption algorithms include: fast speed, format compliance, a wide range of security levels, easy implementation, etc. However, promising candidates that possess all these properties have not appeared. In this work we design a selective encryption algorithm for MPEG video with the aim to address all these problems.
Guest Editors' Introduction: Machine Ethics
Authors: Michael Anderson, Susan Leigh Anderson
Venue: IEEE Intelligent Systems (2006)

Machine ethics is concerned with how machines behave toward human users and other machines. It aims to create a machine that's guided by an acceptable ethical principle or set of principles in the decisions it makes about possible courses of action it could take. As ethics experts continue to progress toward consensus concerning the right way to behave in ethical dilemmas, the task for those working in machine ethics is to codify these insights. Eight articles in this special issue address the issues.This article is part of a special issue on Machine Ethics.
Watching-TV with A Side Trip to the Web for Information Complementation
Authors: Qiang Ma, Hisashi Miyamori, Katsumi Tanaka
Venue: Proceedings of the Second International Conference on Semantics, Knowledge, and Grid (2006)

Digital broadcasting and storage television combining broadcasting and computer technologies are changing the way we watch television. We propose a new way of enhancing information access obtained from nextgeneration storage television. We can suspend TVwatching to access the Web to browse additional information. This additional information is retrieved automatically based on a complementary information retrieval method. When we have finished browsing, we can return to the point at which we paused and continue watching TV. We also present some results obtained by evaluating our method.
Extraction in software watermarking
Authors: William Zhu, Clark Thomborson
Venue: Proceedings of the 8th workshop on Multimedia and security (2006)

The widespread use of the Internet makes software piracy and unauthorized modification easier and more frequent. Among the many techniques developed for protecting software copyrights is software watermarking which embeds secret messages into software to identify its owners and developers. While digital watermarking for media such as video, audio, and text is a popular research field, software watermarking is still a relatively new scientific area. The key concepts in software watermarking are informal; some are even confusing. Formalizing these fundamental terms would facilitate the research in this field. In this paper, we formally define the following concepts involved in embedding watermarks into and extracting watermarks from a program in software watermarking: embedding, set of candidate watermarks, representative set, representative degree, extracting, extractability, blindly extractability, and representative extracting.Through the concepts of the representative sets and the representative degree of an embedding algorithm and a program, we characterize the intrinsic property of an extractable embedding algorithm for software watermarking. Furthermore, the concept of the representative extracting algorithm is used to show the best thing we can get for a general software watermarking embedding algorithm.
On the complexity of practical ATL model checking
Authors: Wiebe van der Hoek, Alessio Lomuscio, Michael Wooldridge
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

We investigate the computational complexity of reasoning about multi-agent systems using the cooperation logic ATL of Alur, Henzinger, and Kupferman. It is known that satisfiability checking is EXPTIME-complete for "full" ATL, and PSPACE-complete (in the general case) for the fragment of ATL corresponding to Pauly's Coalition Logic. In contrast, the model checking problems for ATL and Coalition Logic can both be solved in time polynomial in the size of the formula and the size of model against which the formula is to be checked. However, these latter results assume an extensive representation of models, in which all states of a model are explicitly enumerated. Such representations are not feasible in practice. In this paper we investigate the complexity of the ATL and Coalition Logic model checking problems for a more "reasonable" model representation known as SRML ("Simple Reactive Modules Language"), a simplified version of the actual model representation languages used for model checkers such as SMV and MOCHA. While it is unsurprising that, when measured against such representations, the model checking problems for ATL and Coalition Logic have a higher complexity than when measured against explicit state representations, we show that in fact the ATL and Coalition Logic model checking problems for SRML models have the same complexity as the corresponding satisfiability problems. That is, model checking ATL formulae against SRML models is EXPTIME-complete, and model checking Coalition Logic formulae against SRML models is PSPACE-complete. We conclude by investigating some technical issues around these results, and discussing their implications.
Rapid Authoring of Intelligent Tutors for Real-World and Experimental Use
Authors: Vincent Aleven, Jonathan Sewall, Bruce M. McLaren, Kenneth R. Koedinger
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Authoring tools for Intelligent Tutoring Systems are especially valuable if they not only provide a rich set of options for the efficient authoring of tutoring systems but also support controlled experiments in which the added educational value of new tutor features is evaluated. The Cognitive Tutor Authoring Tools (CTAT) provide both. Using CTAT, real-world "Example-Tracing Tutors" can be created without programming. CTAT also provides various kinds of support for controlled experiments, such as administration of different experimental treatments, logging, and data analysis. We present two case studies in which Example-Tracing Tutors created with CTAT were used in classroom experiments. The case studies illustrate a number of new features in CTAT: Use of Macromedia Flash MX 2004 for creating tutor interfaces, extensions to the Example-Tracing Engine that allow for more flexible tutors, a Mass Production facility for more efficient template-based authoring, and support for controlled experiments.