ppxscal/embedding_network huggingface dataset:
GPS orbit approximation using radial basis function networks
Authors: Polona Pavlovčič Prešeren, Bojan Stopar
Venue: Computers Geosciences (2009)

We present solutions for GPS orbit computation from broadcast and precise ephemerides using a group of artificial neural networks (ANNs), i.e. radial basis function networks (RBFNs). The problem of broadcast orbit correction, resulting from precise ephemerides, has already been solved using traditional polynomial and trigonometric interpolation. As an alternative approach RBFN broadcast orbit correction produces results within the accuracy range of the traditional methods. Our study shows RBFN broadcast orbit correction performs well also near the end of data intervals and for short data spans (~20min). Regarding limitations of polynomial and trigonometric extrapolation, the most significant advantage of using RBFNs over the traditional methods for GPS broadcast orbit approximation arises from its short time prediction capability.
Surface fitting with NURBS: a gauss Newton with trust region approach
Authors: Nils Carlson, Mårten Gulliksson
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

This paper shows a new approach for non-linear least squares fitting with NURBS as curves and surfaces to measured data by the Gauss-Newton method. A Trust Region algorithm is used to reach global convergence as well as variable substitution and simple bounds.
Path-following algorithms and experiments for an unmanned surface vehicle
Authors: Marco Bibuli, Gabriele Bruzzone, Massimo Caccia, Lionel Lapierre
Venue: Journal of Field Robotics (2009)

This paper addresses the problem of path following in two-dimensional space for underactuated unmanned surface vehicles (USVs), defining a set of guidance laws at the kinematic level. The proposed nonlinear Lyapunov-based control law yields convergence of the path-following error coordinates to zero. Furthermore, the introduction of a virtual controlled degree of freedom for the target to be followed on the path removes singularity behaviors present in other guidance algorithms proposed in the literature. Some heuristic approaches are then proposed to face the problem of speed of advance adaptation based on path curvature measurement and steering action prediction. Finally a set of experimental results of all the proposed guidance laws, carried out with the Charlie USV, demonstrates the feasibility of the proposed approach and the performance improvements, in terms of precision in following the reference path and transient reduction, obtained by introducing speed adaptation heuristics. &copy; 2009 Wiley Periodicals, Inc.
On-Line Tuning of a Neural PID Controller Based on Variable Structure RBF Network
Authors: Jianchuan Yin, Gexin Bi, Fang Dong
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper presents the use of a variable structure radial basis function (RBF) network for identification in PID control scheme. The parameters of PID control are on-line tuned by a sequential learning RBF network, whose hidden units and connecting parameters are adapted on-line. The RBF-network-based PID controller simplifies modeling procedure by learning input-output samples while keep the advantages of traditional PID controller simultaneously. Simulation results of ship course control simulation demonstrate the applicability and effectiveness of the intelligent PID control strategy.
Computing lexical chains with graph clustering
Authors: Olena Medelyan
Venue: Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop (2007)

This paper describes a new method for computing lexical chains. These are sequences of semantically related words that reflect a text's cohesive structure. In contrast to previous methods, we are able to select chains based on their cohesive strength. This is achieved by analyzing the connectivity in graphs representing the lexical chains. We show that the generated chains significantly improve performance of automatic text summarization and keyphrase indexing.
Global Outsourcing with Microsoft Visual Studio 2005 Team System
Authors: Jamil Azher
Venue:  (2007)

In this era of globalization, outsourcing has emerged as a key strategy not only for achieving competitive advantage but also for survival. However, despite widespread adoption, there are few practical guidelines in the market regarding how to optimally manage outsourced projects. Global Outsourcing with Microsoft Visual Studio 2005 Team System teaches managers, developers, and SQA professionals how to successfully execute outsourced projects. Providing best practices guidelines based upon empirical experience of leading practitioners in the outsourcing industry, the book shows how to construct result-oriented development processes for mitigating execution risk, improving quality, and increasing efficiency. Using Microsoft Visual Studio 2005 Team System as the underlying infrastructure, the book teaches how to customize development methodologies, integrate with MS Project and MS Outlook, embed real-time communication capabilities, manage source code and build process, run unit tests, create custom reports, and leverage the project portal. Global Outsourcing with Microsoft Visual Studio 2005 Team System is an essential IT reference for companies interested in effectively managing outsourced projects and reducing friction associated with globally distributed software development. 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
Easing the Dimensionality Curse by Stretching Metric Spaces
Authors: Ives R. Pola, Agma J. Traina, Caetano Traina,  Jr.
Venue: Proceedings of the 21st International Conference on Scientific and Statistical Database Management (2009)

Queries over sets of complex elements are performed extracting features from each element, which are used in place of the real ones during the processing. Extracting a large number of significant features increases the representative power of the feature vector and improves the query precision. However, each feature is a dimension in the representation space, consequently handling more features worsen the dimensionality curse. The problem derives from the fact that the elements tends to distribute all over the space and a large dimensionality allows them to spread over much broader spaces. Therefore, in high-dimensional spaces, elements are frequently farther from each other, so the distance differences among pairs of elements tends to homogenize. When searching for nearest neighbors, the first one is usually not close, but as long as one is found, small increases in the query radius tend to include several others. This effect increases the overlap between nodes in access methods indexing the dataset. Both spatial and metric access methods are sensitive to the problem. This paper presents a general strategy applicable to metric access methods in general, improving the performance of similarity queries in high dimensional spaces. Our technique applies a function that "stretches" the distances. Thus, close objects become closer and far ones become even farther. Experiments using the metric access method Slim-tree show that similarity queries performed in the transformed spaces demands up to 70% less distance calculations, 52% less disk access and reduces up to 57% in total time when comparing with the original spaces.
An e-Learning Platform for SME Manager Upgrade and its Evolution Toward a Distributed Training Environment
Authors: Nicola Capuano, Matteo Gaeta, Laura Pappacena
Venue: Proceeding of the 2005 conference on Towards the Learning Grid: Advances in Human Learning Services (2005)

The purpose of this paper is to describe the work related to the customisation, the trial and the evaluation of an innovative e-learning platform for manager upgrade in Small and Medium Enterprises (SME), in the framework of the EC funded project named InTraServ and its re-engineering process, aimed at adopting distributed services in the framework of another EC funded project named Diogene. The presented e-learning environment includes several state-of-the-art technologies and methodologies such as: metadata and ontologies for knowledge manipulation, fuzzy learner modelling, intelligent course tailoring, case based reasoning, business games and simulation tools. The proposed evolution is based on the distribution of working tasks among content provider services, content discovery services, content brokering services, training services, curriculum vitae searching services and collaboration services.
Large-scale deep unsupervised learning using graphics processors
Authors: Rajat Raina, Anand Madhavan, Andrew Y. Ng
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free parameters. We consider two well-known unsupervised learning models, deep belief networks (DBNs) and sparse coding, that have recently been applied to a flurry of machine learning applications (Hinton & Salakhutdinov, 2006; Raina et al., 2007). Unfortunately, current learning algorithms for both models are too slow for large-scale applications, forcing researchers to focus on smaller-scale models, or to use fewer training examples. In this paper, we suggest massively parallel methods to help resolve these problems. We argue that modern graphics processors far surpass the computational capabilities of multicore CPUs, and have the potential to revolutionize the applicability of deep unsupervised learning methods. We develop general principles for massively parallelizing unsupervised learning tasks using graphics processors. We show that these principles can be applied to successfully scaling up learning algorithms for both DBNs and sparse coding. Our implementation of DBN learning is up to 70 times faster than a dual-core CPU implementation for large models. For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day. For sparse coding, we develop a simple, inherently parallel algorithm, that leads to a 5 to 15-fold speedup over previous methods.
Concept Search
Authors: Fausto Giunchiglia, Uladzimir Kharkevich, Ilya Zaihrayeu
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics.
di-jest: Autonomic neighbour management for worm resilience in p2p systems
Authors: Douglas McIlwraith, Micael Paquier, Evangelos Kotsovinos
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

Internet worms pose a serious and ongoing threat to system security, often resulting in significant service downtime and disruption. In recent years peer-to-peer (p2p) networks have become a target for the deployment of worms as their high connectivity allows for rapid dissemination and homogeneity of the adopted software platform ensures the existence of common susceptibilities. In this paper we observe that peer similarity in p2p networks can greatly increase overall vulnerability; peers with largely different system characteristics are unlikely to be infected by the same worm. With this in mind we present dijest — an autonomic method for neighbour selection based on heterogeneity. Our results show the efficacy of di-jest in reducing the spread rate and potency of p2p worms. By selecting neighbours with different system characteristics dijest can reduce the number of peers infected by a worm by up to 80%.
Shake Well Before Use: Intuitive and Secure Pairing of Mobile Devices
Authors: Rene Mayrhofer, Hans Gellersen
Venue: IEEE Transactions on Mobile Computing (2009)

A challenge in facilitating spontaneous mobile interactions is to provide pairing methods that are both intuitive and secure. Simultaneous shaking is proposed as a novel and easy-to-use mechanism for pairing of small mobile devices. The underlying principle is to use common movement as a secret that the involved devices share for mutual authentication. We present two concrete methods, ShaVe and ShaCK, in which sensing and analysis of shaking movement is combined with cryptographic protocols for secure authentication. ShaVe is based on initial key exchange followed by exchange and comparison of sensor data for verification of key authenticity. ShaCK, in contrast, is based on matching features extracted from the sensor data to construct a cryptographic key. The classification algorithms used in our approach are shown to robustly separate simultaneous shaking of two devices from other concurrent movement of a pair of devices, with a false negative rate of under 12 percent. A user study confirms that the method is intuitive and easy to use, as users can shake devices in an arbitrary pattern.
How to Extract and Expand Randomness: A Summary and Explanation of Existing Results
Authors: Yvonne Cliff, Colin Boyd, Juan Gonzalez Nieto
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

We examine the use of randomness extraction and expansion in key agreement (KA) protocols to generate uniformly random keys in the standard model. Although existing works provide the basic theorems necessary, they lack details or examples of appropriate cryptographic primitives and/or parameter sizes. This has lead to the large amount of min-entropy needed in the (non-uniform) shared secret being overlooked in proposals and efficiency comparisons of KA protocols. We therefore summarize existing work in the area and examine the security levels achieved with the use of various extractors and expanders for particular parameter sizes. The tables presented herein show that the shared secret needs a min-entropy of at least 292 bits (and even more with more realistic assumptions) to achieve an overall security level of 80 bits using the extractors and expanders we consider. The tables may be used to find the min-entropy required for various security levels and assumptions. We also find that when using the short exponent theorems of Gennaro et al., the short exponents may need to be much longer than they suggested.
Multi-valued Pattern Databases
Authors: Carlos Linares López
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Pattern Databases were a major breakthrough in heuristic search by solving hard combinatorial problems various orders of magnitude faster than state-of-the-art techniques at that time. Since then, they have received a lot of attention. Moreover, pattern databases are also researched in conjunction with other domain-independent techniques for solving planning tasks. However, they are not the only technique for improving heuristic estimates. Although more modest, perimeter search can also lead to significant improvements in the number of generated nodes and overall running time. Therefore, whether they can be combined or not is a natural and interesting issue. While other researchers have recently proven that a joint application of both ideas (termed as multiple goal) leads to no progress at all, it is shown here that there are other alternatives for putting both techniques together---denoted here as multi-valued. This paper shows that multi-valued pattern databases can still improve the performance of standard (or single-valued) pattern databases in practice. It also examines how to enhance memory usage when comparing multi-valued pattern databases in contraposition to various single-valued standard pattern databases.
Seven Principles of Synthetic Intelligence
Authors: Joscha Bach
Venue: Proceeding of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference (2008)

Understanding why the original project of Artificial Intelligence is widely regarded as a failure and has been abandoned even by most of contemporary AI research itself may prove crucial to achieving synthetic intelligence. Here, we take a brief look at some principles that we might consider to be lessons from the past five decades of AI. The author's own AI architecture --MicroPsi --attempts to contribute to that discussion.
A Generic Tool to Browse Tutor-Student Interactions: Time Will Tell!
Authors: Jack Mostow, Joseph Beck, Andrew Cuneo, Evandro Gouvea, Cecily Heiner
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

A basic question in mining data from an intelligent tutoring system is, “What happened when…?” A generic tool to answer such questions should let the user specify which phenomenon to explore; explore selected events and the context in which they occurred; and require minimal effort to adapt the tool to new versions, to new users, or to other tutors. We describe an implemented tool and how it meets these requirements. The tool applies to MySQL databases whose representation of tutorial events includes student, computer, start time, and end time. It infers the implicit hierarchical structure of tutorial interaction so humans can browse it. A companion paper [1] illustrates the use of this tool to explore data from Project LISTEN's automated Reading Tutor.
Enriching Classroom Scenarios with Tagged Objects
Authors: Marc Jansen, Björn Eisen, Ulrich Hoppe
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Over the last recent years, the technology of RF-ID tags enriched a lot of different scenarios. Already at the very beginning, this technology was considered to be useful in the area of CSCL (Computer Supported Collaborative Learning). This paper describes an approach that uses RF-ID tags in order to store information about objects used in a physical simulation environment. Hereby, the stored information and therefore the RF-ID tags help to bridge the gap between a physical environment and a virtual setting in a collaborative modelling and design system.
Integration and Innovation Orient to E-SocietyVolume 2: 7th IFIP International Conference on e-Business, e-Services, and e-Society (I3E2007), October 10-12, 1st edition
Authors: Weijun Wang, Weijun Wang, Yanhui Li, Zhao Duan, Li Yan, Hongxiu Li, Xiaoxi Yang
Venue: Federation For Information Processing (2007)

International Federation for Information Processing The IFIP series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; computers and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Proceedings and post-proceedings of referred international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP series is to encourage education and the dissemination and exchange of information about all aspects of computing. For more information about the 300 other books in the IFIP series, please visit springer.com.
A system for integral efficiency analysis of sustainable technologies
Authors: Guido Rößling, Yavor Kolarov
Venue: ACM SIGCSE Bulletin (2009)

Sustainability is receiving increasing interest from the CS community and average users. We present plans for a system that provides an integral efficiency analysis for recommending the most appropriate sustainable technology for a given location.
A Representation-Theoretical Analysis of the OMG Modelling Suite
Authors: Cesar Gonzalez-Perez, Brian Henderson-Sellers
Venue: Proceeding of the 2005 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the fourth SoMeT_W05 (2005)

Conceptual modelling in software engineering is dominated by the implicit semantics of UML, some other OMG products, and a bunch of related modelling tools. Although other alternatives to modelling are seen in academic circles, industry is dominated by this single approach. With no room to improve and no freedom to experiment, stagnation is guaranteed. The paper claims that a technology-free, purely theoretical analysis of models and modelling is necessary in order to find what real models are about, what the real modelling mechanisms behind software engineering are, and what kind of modelling infrastructure should serve as a foundation for future modelling technologies.
A Heuristic Technique for the Capacity Assessment of Periodic Trains
Authors: M. Abril, M. A. Salido, F. Barber, L. Ingolotti, A. Lova, P. Tormos
Venue: Proceeding of the 2005 conference on Artificial Intelligence Research and Development (2005)

Railway capacity has been a significant issue in the railway industry. Numerous approaches and tools have been developed to compute railway capacity. Nowadays, due to the European politics of liberation of the railway, numerous railway companies need to realize capacity studies to improve their networks. In this paper, we present a fractional heuristic technique to compute the maximum railway capacity to schedule periodic trains, developed in collaboration with the Spanish Railway Infrastructure Manager (ADIF). This technique studies the capacity of every line section considering its adjacent line sections. The results show that this technique obtain saturated running map.
The financial meltdown and computing
Authors: Moshe Y. Vardi
Venue: Communications of the ACM (2009)

For many of us, the past year has been one of the most unsettling in our lifetime. In the late 1980s and early 1990s, we watched communism collapse of its own dead weight. In late 2008, we saw capitalism nearly crumble. Lehman Brothers, a major U.S. investment bank, declared bankruptcy last September, sending the world's financial system into a tailspin. Only a massive intervention by central banks saved the system from collapse.
History-Based Inside-Outside Algorithm
Authors: Heshaam Feili, Gholamreza Ghassem-Sani
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Grammar induction is one of the most important research areas of the natural language processing. The lack of a large Treebank, which is required in supervised grammar induction, in some natural languages such as Persian encouraged us to focus on unsupervised methods. We have found the Inside-Outside algorithm, introduced by Lari and Young, as a suitable platform to work on, and augmented IO with a history notion. The result is an improved unsupervised grammar induction method called History-based IO (HIO). Applying HIO to two very divergent natural languages (i.e., English and Persian) indicates that inducing more conditioned grammars improves the quality of the resultant grammar. Besides, our experiments on ATIS and WSJ show that HIO outperforms most current unsupervised grammar induction methods.