ppxscal/embedding_network huggingface dataset:
CultureSampo: A National Publication System of Cultural Heritage on the Semantic Web 2.0
Authors: Eero Hyvönen, Eetu Mäkelä, Tomi Kauppinen, Olli Alm, Jussi Kurki, Tuukka Ruotsalo, Katri Seppälä, Joeli Takala, Kimmo Puputti, Heini Kuittinen, Kim Viljanen, Jouni Tuominen, Tuomas Palonen, Matias Frosterus, Reetta Sinkkilä, Panu Paakkarinen, Joonas Laitio, Katariina Nyberg
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

CultureSampo is an application demonstration of a national level publication system of cultural heritage contents on the Web, based on ideas and technologies of the Semantic (Web and) Web 2.0. On the semantic side, the system presents new solutions to interoperability problems of dealing with multiple ontologies of different domains, and to problems of integrating multiple metadata schemas and cross-domain content into a homogeneous semantic portal. A novelty of the system is to use semantic models based on events and narrative process descriptions for modeling and visualizing cultural phenomena, and for semantic recommendations. On the Web 2.0 side, CultureSampo proposes and demonstrates a content creation process for collaborative, distributed ontology and content development including different memory organizations and citizens. The system provides the cultural heritage contents to end-users in a new way through multiple (nine) thematic perspectives, based on semantic visualizations. Furthermore, CultureSampo services are available for external web-applications to use through semantic AJAX widgets.
Support vector machines for query-focused summarization trained and evaluated on pyramid data
Authors: Maria Fuentes, Enrique Alfonseca, Horacio Rodríguez
Venue: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions (2007)

This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a query-focused summary. Several SVMs are trained using information from pyramids of summary content units. Their performance is compared with the best performing systems in DUC-2005, using both ROUGE and autoPan, an automatic scoring method for pyramid evaluation.
A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers
Authors: José Antonio Martín H., Javier de Lope, Matilde Santos
Venue: Neurocomputing (2009)

A general method to learn the inverse kinematic of multi-link robots by means of neuro-controllers is presented. We can find analytical solutions for the most used and well-known robots in the literature. However, these solutions are specific to a particular robot configuration and are not generally applicable to other robot morphologies. The proposed method is general in the sense that it is independent of the robot morphology. The method is based on the evolutionary computation paradigm and works obtaining incrementally better neuro-controllers. Furthermore, the proposed method solves some specific issues in robotic neuro-controller learning: it avoids any neural network learning algorithm which relies on the classical supervised input-target learning scheme and hence it lets to obtain neuro-controllers without providing targets. It can converge beyond local optimal solutions, which is one of the main drawbacks of some neural network training algorithms based on gradient descent when applied to highly redundant robot morphologies. Furthermore, using learning algorithms such as the neuro-evolution of augmenting topologies it is also possible to learn the neural network topology which is a common source of empirical testing in neuro-controllers design. Finally, experimental results are provided when applying the method to two multi-link robot learning tasks and a comparison between structural and parametric evolutionary strategies on neuro-controllers is shown.
Speculative Clustered Caches for Clustered Processors
Authors: Dana S. Henry, Gabriel H. Loh, Rahul Sami
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

Clustering is a technique for partitioning superscalar processor's execution resources to simultaneously allow for more in-flight instructions, wider issue width, and more aggressive clock speeds. As either the size of individual clusters or the total number of clusters increases, the distance to the first level data cache increases as well. Although clustering may expose more parallelism by allowing a greater number of instructions to be simultaneously analyzed and issued, the gains may be obliterated if the latencies to memory grow too large. We propose to augment each cluster with a small, fast, simple Level Zero (L0) data cache that is accessed in parallel with a traditional L1 data cache. The difference between our solution and other proposed caching techniques for clustered processors is that we do not support versioning or coherence. This may occasionally result in a load instruction that reads a stale value from the L0 cache, but the common case is a low latency hit in the L0 cache. Our simulation studies show that 4KB, 2-way set associative L0 caches provide a 6.5---12.3% IPC improvement over a wide range of processor configurations.
Replacing introductory programming courses with a broader perspective on computing
Authors: Gregory W. Hislop
Venue: ACM SIGCSE Bulletin (2009)

This poster describes an effort to create a different initial course sequence for computing degrees. This effort, the EntryPoint project, focuses primarily on the first two years of the undergraduate program. An intention of the project is to retain programming learning objectives, but move away from the traditional programming-focused introductory sequence. A three dimensional model, CAP (content, application, and process), is used to organize the discussion of introductory courses.
Experimental Study: Link Quality and Deployment Issues in Wireless Sensor Networks
Authors: Monique Becker, Andre-Luc Beylot, Riadh Dhaou, Ashish Gupta, Rahim Kacimi, Michel Marot
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

In this paper, we highlight the extent of the effects of topological specificities on the deployed solutions, which can be useful to refine already proposed models as well as to carry out protocol tuning or adjustments. We present, an intensive experimental study on wireless Link Quality Indicator (LQI). Using Moteiv's Tmote Sky sensors, we deployed multiHopLQI algorithm of TinyOS in various network configurations: homogeneous and heterogeneous; straight-line and grid topologies with various transmission power levels and distances.Initially, we study LQI time-varying and try to understand the relationship between transmission power level, distance and link quality and present how some random disturbances due to external (physical changes) or internal phenomena (node movement,power variation) may affect the dynamics of the network. Later, we address impacts and side effects of position and power transmission level of some important nodes in the network like the Base Station in such LQI based algorithms.
Using the EAP framework for fast media independent handover authentication
Authors: Antonio Izquierdo, Nada Golmie, Katrin Hoeper, Lidong Chen
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

In this paper we compare different authentication techniques that may be used in order to reduce the time and resources required to perform a handover: namely, re-authentication and pre-authentication. We provide a detailed analysis of each mechanism, and highlight key performance trade-offs for select scenarios and parameters of interest.
Neural network adaptation process effectiveness dependent of constant training data availability
Authors: Ewa Dudek-Dyduch, Ryszard Tadeusiewicz, Adrian Horzyk
Venue: Neurocomputing (2009)

The paper discusses and compares two different ways of adapting artificial intelligence systems. One is founded on a well known biological mechanism of gradual training of neurons or other parameters. The second one uses a significant extra feature of training data that ably makes us possible to adapt the artificial intelligence system in more effective way than nature does in biological systems. This extra feature is availability of all training data before the adaptation process begins till an end of which all these data have to be constant. This feature provides an ability to analyze training data globally and very quickly tune an artificial intelligence system with them. The paper focus the attention on this important difference between biological and artificial intelligence problems because in most cases of artificial intelligence problems training data are gathered, available and constant during the training process. On the other hand, the biological nervous systems gather training data during the whole life, have to change the inner model, so training is a very good solution for them because it makes them possible to tune with changing training data. Artificial intelligence systems can also use training inherent in biological systems but in most cases it is possible to find more quickly and effectively the solution if only the mentioned feature is met. The above thesis is illustrated by means of some examples.
Integrating Generalized Linear Auto-Regression and Artificial Neural Networks for Coal Demand Forecasting
Authors: Ping Yao
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this study, a novel combined forecasting model integrating generalized linear auto-regression (GLAR) with artificial neural networks (ANN) was proposed to obtain accurate prediction results and ameliorate forecasting performances. We compare the performance of the new model with the two individual forecasting models-GLAR and ANN. Empirical results obtained reveal that the prediction using the combined model is generally better than those obtained using the individual model of GLAR and ANN in terms of the same evaluation measurements. Our findings reveal that the combined model proposed here can be used as an alternative forecasting tool for coal demand to achieve greater forecasting accuracy and improve prediction quality further.
Enabling Pervasive Collaboration with Platform Composition
Authors: Trevor Pering, Roy Want, Barbara Rosario, Shivani Sud, Kent Lyons
Venue: Proceedings of the 7th International Conference on Pervasive Computing (2009)

Emerging pervasive computing technologies present many opportunities to aid ad-hoc collocated group collaboration. To better understand ad-hoc collaboration using pervasive technologies, or Pervasive Collaboration, a design space composed of three axes(composition granularity, sharing models, and resource references) is outlined, highlighting areas that are only partially covered by existing systems. Addressing some of these gaps, Platform Composition is a technique designed to overcome the usability limitations of small mobile devices and facilitate group activities in ad-hoc environments by enabling users to run legacy applications on a collection mobile devices. The associated Composition Framework prototype demonstrates a concrete implementation that explores the applicability of existing technologies, protocols, and applications to this model. Overall, Platform Composition promises to be an effective technique for supporting collaborative work on mobile devices, without requiring significant changes to the underlying computer platform or end-user applications.
HSM: Heterogeneous Subspace Mining in High Dimensional Data
Authors: Emmanuel Müller, Ira Assent, Thomas Seidl
Venue: Proceedings of the 21st International Conference on Scientific and Statistical Database Management (2009)

Heterogeneous data, i.e. data with both categorical and continuous values, is common in many databases. However, most data mining algorithms assume either continuous or categorical attributes, but not both. In high dimensional data, phenomena due to the "curse of dimensionality" pose additional challenges. Usually, due to locally varying relevance of attributes, patterns do not show across the full set of attributes.In this paper we propose HSM, which defines a new pattern model for heterogeneous high dimensional data. It allows data mining in arbitrary subsets of the attributes that are relevant for the respective patterns. Based on this model we propose an efficient algorithm, which is aware of the heterogeneity of the attributes. We extend an indexing structure for continuous attributes such that HSM indexing adapts to different attribute types. In our experiments we show that HSM efficiently mines patterns in arbitrary subspaces of heterogeneous high dimensional data.
System Zoo 3 Simulation Models. Economy, Society, Development
Authors: Hartmut Bossel
Venue:  (2007)

About the book: Mathematical modeling and computer simulation make it possible to understand and control the dynamic processes taking place in complex systems. Simulation provides insights into the often surprising diversity of possible behaviors, and allows identifying possibilities for intervention and options for alternative development. About one hundred simulation models from all areas of life are fully documented in the three volumes of the 'System Zoo'. They can be quickly implemented and easily operated using freely available system dynamics software. Volume 3 of the System Zoo contains simulation models of economic and social systems and global development, among them: production, stocks and orders, marketing and consumption, competition, life planning, employment, ecotax, escalation, dependence, aggression, population and community development, debt crisis, globalization, the world models of the Club of Rome (by Forrester and the Meadows group) and examples of nonnumerical knowledge processing applied to impact assessment and decision processes. The System Zoo collection of simulation models is particularly well-suited for teaching, training, and research projects at all levels from high school to university, and for individual study. Volume 1 of the System Zoo contains simulation models of elementary systems, and of systems from the fields of physics and engineering. Volume 2 of the System Zoo presents simulation models related to climate, vegetation, ecosystems and resources. About the author: Hartmut Bossel is Professor Emeritus of environmental systems analysis. He taught for many years at the University of California in Santa Barbara and the University of Kassel, Germany, where he was director of the Center for Environmental Systems Research until his retirement. He holds an engineering degree from the Technical University of Darmstadt, and a Ph.D. degree from the University of California at Berkeley. With a background in engineering, systems science, and mathematical modeling, he has led many research projects and future studies in different countries, developing computer simulation models and decision support systems in the areas of energy supply policy, global dynamics, orientation of behavior, agricultural policy, and forest dynamics and management. He has written numerous books on modeling and simulation of dynamic systems, social change and future paths, and has published widely in the scientific literature in several fields. 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
Robust guaranteed cost control for a class of two-dimensional discrete systems with shift-delays
Authors: Shuxia Ye, Weiqun Wang, Yun Zou
Venue: Multidimensional Systems and Signal Processing (2009)

This paper considers the problem of robust guaranteed cost control for uncertain two-dimensional (2-D) discrete shift-delayed systems in Fornasini---Marchesini (FM) second model. The parameter uncertainty is assumed to be norm-bounded. The problem addressed is the design of state feedback controllers such that the closed-loop system is asymptotically stable and an adequate level of performance can be guaranteed for all admissible uncertainties. The cost function with shift-delays is proposed and an upper bound of the cost function is given. In terms of a linear matrix inequality (LMI), a sufficient condition for the solvability of the problem is obtained. A desired state feedback controller can be constructed by solving a certain LMI. A numerical example is provided to demonstrate the application of the proposed method.
A general student's model suitable for intelligent e-learning systems
Authors: F. De Arriaga, C. Gingell, A. De Arriaga, J. Arriaga, F. Arriaga
Venue: Proceedings of the 2nd conference on European computing conference (2008)

The developments produced in the field of Distributed Artificial Intelligence (DAI) and cognitive modeling open new possibilities to enhance the functionality of Intelligent E-Learning Systems (ILS). Learning is a difficult problem but cognitive modeling, including the expert mental models, brings forth a deeper insight and collaborate to build a general student's model including affective computing and fuzzy evaluation and knowledge representation techniques. The paper describes the new possible ILS tutorial and managerial functions, that have been considered to produce the student's model. Expert representation will follow a similar pattern to ease both shallow and deep error analysis. The student's model that also takes into account the particular aspects of collaborative learning, is also described. DYNAMICS, is a practical example of a working Intelligent E-Learning System with high functionality implemented by means of a multi-agent architecture and the student's model presented in this paper. It helps the student alone or a group of students as a coach for decision making learning on system modelling on the state space. Some operational results are shown and discussed.
Time-critical collision handling for deformable modeling
Authors: Marc Gissler, Ruediger Schmedding, Matthias Teschner
Venue: Computer Animation and Virtual Worlds (2009)

Collision handling is a comparatively time-consuming task in dynamic simulations and the computational efficiency of collision handling techniques can vary significantly dependent on the spatial configuration of the environment. These issues have to be addressed in interactive simulations such as games or surgical simulators, where a pre-defined response time should be guaranteed for each simulation step. We present a time-critical collision handling approach for deformable objects. The technique employs spatial subdivision for the detection of collisions and penetration depth information is computed to estimate penalty forces. Detection, penetration depth estimation, and response are divided into atomic tasks. In case of an interruption, the algorithm basically resumes in the next time step. If collisions are not completely handled in one simulation step, the algorithm ensures that persistent collisions are handled in a subsequent simulation step. If an exact response cannot be computed in a given time frame, the algorithm efficiently approximates penalty forces for colliding points. Experiments indicate that the proposed technique provides a physically plausible collision handling in the case of incomplete or inconsistent collision information. User-defined limits for the computation time can be guaranteed with an efficiency gain of up to factor three. Copyright &copy; 2009 John Wiley & Sons, Ltd. A time-critical collision handling approach for deformable objects is presented. Detection, penetration depth estimation, and response are divided into atomic tasks. In case of an interruption, the algorithm basically resumes in the next time step. It is ensured that persistent collisions are handled in a subsequent simulation step. Approximate penalty forces are provided for colliding points whose collision handling is postponed to the next time step. The results are physically plausible and given time constraints can be guaranteed.
Microarray Analysis for Gene Expression that involves Trace Metals
Authors: Chinnu Subramaniam, Jiyuan An, Yi-Ping Phoebe Chen
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

Microarray plays a major role to identify the over-and under-expressed genes. It is a well-known fact that trace elements in our body play a major role in the metabolic processes of all living organisms. In this paper, the microarray studies related to major trace metals are reviewed. This review forms the basis for the converged effort to locate the genes that are either defective and destabilise the concentration of the trace metals or influenced by the changed concentration of the trace metals that are needed for proper functions of the human body, at different parts of the body.
The big picture on small screens delivering acceptable video quality in mobile TV
Authors: H. Knoche, M. A. Sasse
Venue: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) (2009)

Mobile TV viewers can change the viewing distance and (on some devices) scale the picture to their preferred viewing ratio, trading off size for angular resolution. We investigated optimal trade-offs between size and resolution through a series of studies. Participants selected their preferred size and rated the acceptability of the visual experience on a 200ppi device at a 4:3 aspect ratio. They preferred viewing ratios similar to living room TV setups regardless of the much lower resolution: at a minimum 14 pixels per degree. While traveling on trains people required videos with a height larger than 35mm.