ppxscal/embedding_network huggingface dataset:
Cellular Automata-Based Scheduling: A New Approach to Improve Generalization Ability of Evolved Rules
Authors: Paulo Moises Vidica, Gina Maira Barbosa de Oliveira
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

This paper presents a cellular automata-based algorithm designed to schedule tasks for parallel processors. In the learning phase, a genetic algorithm is used to discover cellular automata (CA) rules able to solve an instance of a multiprocessor scheduling problem. In the normal operating phase, the discovered rules are applied to find optimal or suboptimal solutions to other scheduling problem instances. A new approach to the learning phase is presented here, called Joint Evolution. The results obtained have shown evolved rules with a better generalization ability when they are applied to small variations of the problem used as base for the evolution.
The impact of search engine optimization on online advertising market
Authors: Bo Xing, Zhangxi Lin
Venue: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet (2006)

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine advertising: paid placement and search engine optimization (SEO). This study aims to analyze the condition under which SEO exist and further, its impact on the advertising market. With an analytical model, several interesting insights are generated. The results of the study fill the gap of SEO in academic research and help managers in online advertising make informed advertising decisions.
Keypoint Recognition Using Randomized Trees
Authors: Vincent Lepetit Pascal Fua
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

In many 3D object-detection and pose-estimation problems, runtime performance is of critical importance. However, there usually is time to train the system, which we will show to be very useful. Assuming that several registered images of the target object are available, we developed a keypoint-based approach that is effective in this context by formulating wide-baseline matching of keypoints extracted from the input images to those found in the model images as a classification problem. This shifts much of the computational burden to a training phase, without sacrificing recognition performance. As a result, the resulting algorithm is robust, accurate, and fast-enough for frame-rate performance. This reduction in runtime computational complexity is our first contribution. Our second contribution is to show that, in this context, a simple and fast keypoint detector suffices to support detection and tracking even under large perspective and scale variations. While earlier methods require a detector that can be expected to produce very repeatable results, in general, which usually is very time-consuming, we simply find the most repeatable object keypoints for the specific target object during the training phase. We have incorporated these ideas into a real-time system that detects planar, nonplanar, and deformable objects. It then estimates the pose of the rigid ones and the deformations of the others.
Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering
Authors: Witcha Chimphlee, Abdul Hanan Abdullah, Mohd Noor Md Sap, Surat Srinoy, Siriporn Chimphlee
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

It is an important issue for the security of network to detect new intrusion attack and also to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). Anomaly intrusion detection focuses on modeling normal behaviors and identifying significant deviations, which could be novel attacks. The normal and the suspicious behavior in computer networks are hard to predict as the boundaries between them cannot be well defined. We apply the idea of the Fuzzy Rough C-means (FRCM) to clustering analysis. FRCM integrates the advantage of fuzzy set theory and rough set theory that the improved algorithm to network intrusion detection. The experimental results on dataset KDDCup99 show that our method outperforms the existing unsupervised intrusion detection methods.
Optimized concrete delivery scheduling using combined simulation and genetic algorithms
Authors: Ming Lu, Hoi-Ching Lam
Venue: Proceedings of the 37th conference on Winter simulation (2005)

The research presented is mainly focused on how to simultaneously optimize concrete delivery scheduling and resource provisions for ready mixed concrete (RMC) plants based on a valid simulation modeling platform resulting from research, called HKCONSIM. Combined discrete-event simulation and genetic algorithms (GA) are applied in HKCONSIM to model and further optimize the one-plant-multisite RMC plant operations in Hong Kong. Logistics planning practices of RMC businesses in Hong Kong are introduced, and interfaces, features and functionalities of HKCONSIM described. The potential industry impact of the research effort is demonstrated with a case study based on one-day operations data obtained from a Hong Kong RMC plant. It is concluded that the GA-integrated simulation platforms specifically designed for RMC companies such as HKCONSIM will potentially assist managers in making optimal decisions on concrete production and delivery scheduling, thus enhancing productivity, resource utilization and concrete supply service in day-by-day operations.
Structural Encoding of Human and Schematic Faces: Holistic and Part-Based Processes
Authors: Noam Sagiv, Shlomo Bentin
Venue: Journal of Cognitive Neuroscience (2001)

The range of specificity and the response properties of the extrastriate face area were investigated by comparing the N170 event-related potential (ERP) component elicited by photographs of natural faces, realistically painted portraits, sketches of faces, schematic faces, and by nonface meaningful and meaningless visual stimuli. Results showed that the N170 distinguished between faces and nonface stimuli when the concept of a face was clearly rendered by the visual stimulus, but it did not distinguish among different face types: Even a schematic face made from simple line fragments triggered the N170. However, in a second experiment, inversion seemed to have a different effect on natural faces in which face components were available and on the pure gestalt-based schematic faces: The N170 amplitude was enhanced when natural faces were presented upside down but reduced when schematic faces were inverted. Inversion delayed the N170 peak latency for both natural and schematic faces. Together, these results suggest that early face processing in the human brain is subserved by a multiple-component neural system in which both whole-face configurations and face parts are processed. The relative involvement of the two perceptual processes is probably determined by whether the physiognomic value of the stimuli depends upon holistic configuration, or whether the individual components can be associated with faces even when presented outside the face context.
Particle DynamicsWarping Approach for Offline Signature Recognition
Authors: Gady Agam, Suneel Suresh
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.
A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks
Authors: Horst F. Wedde, Muddassar Farooq
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2006)

The major contribution of the paper is a comprehensive survey of existing state-of-the-art Nature inspired routing protocols for fixed telecommunication networks developed by researchers who are trained in novel and different design doctrines and practices. Nature inspired routing protocols have been becoming the focus of research because they achieve the complex task of routing through simple agents which traverse the network and collect the routing information in an asynchronous fashion. Each node in the network has a limited information about the state of the network, and it routes data packets to their destination based on this local information. The agent-based routing algorithms provide adaptive and efficient utilization of network resources in response to changes in the network catering for load balancing and fault management. The paper describes the important features of stigmergic routing algorithms, evolutionary routing algorithms and artificial intelligence routing algorithms for fixed telecommunication networks. We also provide a summary of the protocols developed by the networking community. We believe that the survey will be instrumental in bridging the gap among different communities involved in research of telecommunication networks.
Generalized (η,ρ)-Invex Functions and Semiparametric Duality Models for Multiobjective Fractional Programming Problems Containing Arbitrary Norms
Authors: G. J. Zalmai
Venue: Journal of Global Optimization (2006)

In this paper, we construct several semiparametric duality models and prove appropriate duality theorems under various generalized (&eta;,&rho;)-invexity assumptions for a multiobjective fractional programming problem involving arbitrary norms.
A Classification Structure for Automated Negotiations
Authors: Ricardo Buttner
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

To date, a lot of automated negotiation models have been developed and manifold negotiation challenges have been already addressed. However, in order to evaluate the state of the art of automated negotiations a classification structure is needed. This paper proposed a taxonomy for classifying automated negotiations. The evaluation of the state of the art to this taxonomy shows the manifold addressing of negotiation challenges, but mainly related to the structure and the process of the negotiation. However, criteria of theoretic foundations and specific negotiation phenomena seem to be inadequately researched.
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
Authors: Bjorn Stenger, Arasanathan Thayananthan Philip H. S. Torr, Roberto Cipolla
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background.
Transforming Pharmaceutical Marketing Through e-detailing: Case Studies and Recommendations
Authors: Martina Y. Trucco, Saidat Amirkhanova
Venue: Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (2006)

This paper presents an overview of how online product detailing ("e-detailing") is transforming traditional pharmaceutical marketing methods, as well as presenting results from a meta-analysis of pilot programs at several companies. Case studies from various multinational companies are presented and analyzed. Recommendations for successful implementation are offered based on the programs studied.
Researchers Make Web Searches More Intelligent
Authors: Steven J. Vaughan-Nichols
Venue: Computer (2006)

Several businesses are exploring the use of artificial intelligence approaches such as natural-language processing and statistical machine learning in Web searches.
Some inequalities for eigenvalues of Schur complements of Hermitian matrices
Authors: Jianzhou Liu, Yunqing Huang, Anping Liao
Venue: Journal of Computational and Applied Mathematics (2006)

In this paper, using a minimum principle for Schur complements of positive semidefinite Hermitian matrices and some estimates of the eigenvalues and the singular values, we obtain some inequalities for the eigenvalues of the Schur complement of the matrix product B A B in terms of the eigenvalues of the Schur complements of BB and A.
Telepresence Across Networks: A Combined Deadband and Prediction Approach
Authors: Michael F. Zaeh, Stella Clarke, Peter Hinterseer, Eckehard Steinbach
Venue: Proceedings of the conference on Information Visualization (2006)

Executing telepresence operations across networks poses a number of challenges. In particular, the transfer of certain data types requires high sampling rates, such as haptic and velocity data. This paper presents a two-step approach which reduces the total amount of data sent across a network, while attempting to reconstruct the original data series. The first step utilises a deadband approach to eliminate the transfer of redundant data. In this sense, redundant refers to the fact that the data will be undetectable or of no consequence at the receiving end. The second step is a prediction implementation to best reconstruct the reduced series to its original form. This combined deadband and prediction approach is applied to velocity data extracted from a telepresence scenario, resulting in an 86% reduction in the amount of data transferred, and good data reconstruction properties.
On The Accuracy of Event Distribution Lists for Publish/Subscribe in Dynamic Distributed Systems
Authors: R. Baldoni, G. Cortese, F. Morabito, L. Querzoni, S. Tucci Piergiovanni, A. Virgillito
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

The advent of very large scale distributed applications in the form of peer-to-peer systems, introduced new problems caused by their intrinsic dynamic behavior (i.e., nodes continuously joining and leaving the system). Such problems inhibit the usage of many techniques developed for quasi-static distributed systems. In this paper we analyze the behavior of a simple subscription-flooding based algorithm for publish/ subscribe in a highly dynamic environment. Specifically we define the problem of keeping a list of processes matching a given event (Event Distribution List) complete and accurate. We propose a simple variant of the subscription flooding approach, introducing expiration of subscriptions and their periodic refresh, and show how it can maintain accurate and complete EDLs in a dynamic distributed system.
Applying discrete event simulation and an automated bottleneck analysis as an aid to detect running production constraints
Authors: Patrick Faget, Ulf Eriksson, Frank Herrmann
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Discrete event simulation is an important decision support tool to evaluate changes in manufacturing, distribution or process facilities. The challenge arises when it comes to the integration of simulation as an effective tool to detect manufacturing constraints and to suggest improvement alternatives. This paper describes the application of a method for detecting bottlenecks in discrete event models developed by Toyota Motor Company. The objective in this case is to automate the bottleneck analysis facilitating the understanding and adoption of simulation by decision makers without knowledge of simulation. The main results of this paper are the validation of the bottleneck detection method and its integration with MS Excel spreadsheets. Moreover system improvement alternatives are presented by the use of design of experiments.
Old and New
Authors: Richard Mateosian
Venue: IEEE Micro (2006)

Richard Mateosian reviews old and new books, including Weinberg on Writing--The Fieldstone Method, The Art of Computer Programming, From Java to Ruby--Things Every Manager Should Know, and Introduction to DITA--A User Guide to the Darwin Information Typing Architecture.
Divisive Hierarchical K-Means
Authors: Sid LAMROUS, Mounira TAILEB
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

This paper focuses on clustering methods for content-based image retrieval CBIR. Hierarchical clustering methods are a way to investigate grouping in data, simultaneously over a variety of scales, by creating a cluster tree. Traditionally, these methods group the objects into a binary hierarchical cluster tree. Our main contribution is the proposal of a new divisive hierarchy that is based on the construction of a non-binary tree. Each node can have more than two divisive clusters by detecting a better grouping in m classes (m¸[2,5]). To determine how to divide the nodes in the hierarchical tree into clusters nodes, we use K-means clustering, [1]. At each node, to determine the correct number of clusters, we use a quality criterion called Silhouette. The solution that kmeans reaches often depends on the starting centroids, however we tested three methods of initialization, and we used the most suitable for our case.
Involuntary Motor Activity in Pianists Evoked by Music Perception
Authors: Jens Haueisen, Thomas R. Knösche
Venue: Journal of Cognitive Neuroscience (2001)

Pianists often report that pure listening to a well-trained piece of music can involuntarily trigger the respective finger movements. We designed a magnetoencephalography (MEG) experiment to compare the motor activation in pianists and nonpianists while listening to piano pieces. For pianists, we found a statistically significant increase of activity above the region of the contralateral motor cortex. Brain surface current density (BSCD) reconstructions revealed a spatial dissociation of this activity between notes preferably played by the thumb and the little finger according to the motor homunculus. Hence, we could demonstrate that pianists, when listening to well-trained piano music, exhibit involuntary motor activity involving the contralateral primary motor cortex (M1).
Analyzing Student Activity in Computer Assisted Language Learning
Authors: Petter Karlstrom, Teresa Cerratto-Pargman
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

We study the use of a computer application, intended for Computer Assisted Language Learning (CALL). We present an analytical framework for CALL, consisting of technology, interaction with technology, a relationship between technology and students, and a context where technology is situated. The application we study performs several different kinds of state of the art linguistic analyses, and is intended for writing texts while paying attention to linguistic forms. We have conducted a naturalistic field study of two informants use the tool collaboratively. Our question is in what manners these students put the tool into use. These students let initiative be taken by the CALL application, despite it being designed with student initiative in mind, and despite students being aware of features and occasions where they could take initiative. We use our framework to point out how this student-system relationship is formed, and provide guidance for future design.
An Ontological Service Oriented and Web Searching Mechanism in Distributed e-learning Repositories
Authors: Konstantinos Votis, Christos. Alexakos, Kostas Giotopoulos, Spiridon. Likothanassis
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

The great heterogeneity of web based Learning systems storing and providing digital e-learning data requires the introduction of interoperability aspects in order to resolve integration problems in a flexible and dynamic way. Learning Objects developed and stored in many different places on the web have a tremendous potential to benefit e-learning in particular and education in general. So the distributed e-learning data integration is significant for the enforcement of novel searching mechanisms in distributed e-learning repositories Our approach introduces an advanced search mechanism which initializes a semantic model representing the digital content stored in distributed elearning servers, through the use of ontologies In order to retrieve useful content to be added to the learning environment while bypassing compatibility problems between different ontological representations of the same domain, we will use a mixed approach constituting both keywords and ontologies Searching tasks are carried out in the metadata level, where information concerning web digital content is published, managed and stored in the form of a scalable description of knowledge domains.
Better selection of the best
Authors: Huaiyu Harry Ma, Thomas R. Willemain
Venue: Proceedings of the 36th conference on Winter simulation (2004)

We present a new method of selecting the best of several competing system designs on the basis of expected steady-state performance. The method uses a new form of time-series bootstrap in a sequential hypothesis testing frame-work. We illustrate the efficiency of the new method by comparing its results against the current state of the art in a series of simulation experiments. The new method achieves equal or greater probability of correct selection of the best system with substantial savings in the number of simulation observations required for the decision.