ppxscal/embedding_network huggingface dataset:
Modeling constructivist teaching functionality and structure in the KBS Hyperbook System
Authors: Nicola Henze, Wolfgang Nejdl, Martin Wolpers
Venue: Proceedings of the 1999 conference on Computer support for collaborative learning (1999)

The KBS Hyperbook System is a system which uses explicit conceptual models and meta data to structure and connect external data. When these external data are pages on the WWW, the corresponding conceptual model takes the role of an information index and determines the navigational structure between these pages (corresponding to one or more views on the external data). The conceptual model also serves as a schema for the integration of new pages (similar to the role of a database schema). In this paper we show how such a model can be used to support three main aspects of constructivist learning in a computer supported teaching environment, namely structural support for goal-oriented learning and projects, the integration of student projects into hyperbook based lecture material and the implementation of student annotations.
Overlap in the Functional Neural Systems Involved in Semantic and Episodic Memory Retrieval
Authors: M. N. Rajah, A. R. McIntosh
Venue: Journal of Cognitive Neuroscience (2005)

Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems [Cabeza, R., & Nyberg, L. Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12, 1&ndash;47, 2000; Gabrieli, J. D. Disorders of memory in humans. Current Opinion in Neurology and Neurosurgery, 6, 93&ndash;97, 1993; Gabrieli, J. D. Cognitive neuroscience of human memory. Annual Review of Psychology, 49, 87&ndash;115, 1998; Squire, L. R. The organization and neural substrates of human memory. International Journal of Neurology, 21&ndash;22, 218&ndash;222, 1987; Squire, L. R., & Zola, S. M. Structure and function of declarative and nondeclarative memory systems. Proceedings of the National Academy of Sciences, U.S.A., 93, 13515&ndash;13522, 1996; Tulving, E. Multiple memory systems and consciousness. Human Neurobiology, 6, 67&ndash;80, 1987]. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks [Duzel, E., Cabeza, R., Picton, T. W., Yonelinas, A. P., Heinze, H. J., Scheich, H., & Tulving, E. Task-related and item related processes in episodic and semantic retrieval: A combined PET and ERP study. Proceedings of the National Academy of Sciences, U.S.A., 96, 1794&ndash;1799, 1999]. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.
High performance simulation in quasi-continuous manufacturing plants
Authors: Lan Chen, Michael Pidd
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Though the quality of discrete simulation software packages is high, most are aimed at systems in which discrete objects change state as they move from work-station to work-station. This generic model is a good fit for some manufacturing, for example, much automotive production. However is not well suited to very high-speed quasi-continuous manufacturing as found in the food and drinks industry. LanSkim is a prototype PC-based package designed for these applications. It is simple to use and runs very fast but is insufficiently detailed for all purposes. Adding full detail would make the simulations run very slowly, hence we examine the use of parallel computation to allow increased level of detail. We describe Lan-WARPED, based on WARPED, which is a parallel simulation models of such plant.
Mining and Sharing Heterogeneous E-Marketing Intelligence - A Universal Metadata-Based Approach
Authors: Guifa Teng, Hong Su, Xiaodong Liu, Tiger Wang
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2 (2006)

Despite the emergent business needs and technical efforts by both industrial and research communities, Ebusiness Market Intelligence (EMI) still faces many obstacles hindering its successful mining, deployment and sharing, including lack of scaleable and transferable data source, incapability to integrate heterogeneous EMI data, and lack of highly automatic and universally compatible binding techniques. This paper presents a Metadata-based approach to facilitate the generation, transformation, sharing and usage of data and knowledge among multiple heterogeneous EMI applications. A Metadata-based framework for EMI development has been developed, which provides a range of solutions for mining the Metadata from general data sources, and then integrating and migrating the Metadata among various data sources. A mechanism has been developed for the automatic binding of Metadata and data into EMI applications. A prototype tool was developed to scale up the proposed approach. Case studies have shown that the approach and the prototype tool are efficient and have the potential to deal with industrial level applications.
Time Series Analysis Using the Concept of Adaptable Threshold Similarity
Authors: Johannes Assfalg, Hans-Peter Kriegel, Peer Kroger, Peter Kunath, Alexey Pryakhin, Matthias Renz
Venue: Proceedings of the 18th International Conference on Scientific and Statistical Database Management (2006)

The issue of data mining in time series databases is of utmost importance for many practical applications and has attracted a lot of research in the past years. In this paper, we focus on the recently proposed concept of threshold similarity which compares the time series based on the time frames within which they exceed a user-defined amplitude threshold ô . We propose a novel approach for cluster analysis of time series based on adaptable threshold similarity. The most important issue in threshold similarity is the choice of the threshold ô . Thus, the threshold ô is automatically adapted to the characteristics of a small training dataset using the concept of support vector machines. Thus, the optimal ô is learned from a small training set in order to yield an accurate clustering of the entire time series database. In our experimental evaluation we demonstrate that our cluster analysis using adaptable threshold similarity can be successfully applied to many scientific real-world data mining applications.
Distributed navigation in an unknown physical environment
Authors: Arnon Gilboa, Amnon Meisels, Ariel Felner
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

We address the problem of navigating from an initial node to a goal node by a group of agents in an unknown physical environment. In such environments mobile agents must physically move around to discover the existence of nodes and edges. We assume that agents communicate by exchanging messages about their discoveries, their current locations and their intended plans. We also assume that an agent can only communicate with a predefined set of neighboring agents. A distributed algorithm, which is run independently by each agent, is presented. Given the current knowledge of the agent about the environment and the positions and intentions of other agents, the algorithm instructs the agent where to go next. An experimental evaluation of the algorithm is presented, with constrained and liberal neighborhood schemes. Results show that it is more beneficial to have a constrained neighborhood scheme because with this scheme the distributed intelligent behavior of agents generates a spread of knowledge throughout the environment more efficiently. Agents reach the goal node fast and the length of the path that they find is very close to that of the optimal path.
xAnVi - An Extensible Visualization and Analysis Framework
Authors: Jan Griebsch, Oliver Arafat, Wolfram Koska
Venue: Proceedings of the conference on Information Visualization (2006)

xAnVi is an extensible framework for the analysis and visualization of graphs. The framework consists of a small core to manage resources, plugins and extension points and an arbitrarily expandable set of plugins to provide the actual application functionality. This paper describes the design approach to create a modular bioinformatics toolkit/framework for.
Extending metric index structures for efficient range query processing
Authors: Karin Kailing, Hans-Peter Kriegel, Martin Pfeifle, Stefan Sch&#x00f6;nauer
Venue: Knowledge and Information Systems (2006)

Databases are getting more and more important for storing complex objects from scientific, engineering, or multimedia applications. Examples for such data are chemical compounds, CAD drawings, or XML data. The efficient search for similar objects in such databases is a key feature. However, the general problem of many similarity measures for complex objects is their computational complexity, which makes them unusable for large databases. In this paper, we combine and extend the two techniques of metric index structures and multi-step query processing to improve the performance of range query processing. The efficiency of our methods is demonstrated in extensive experiments on real-world data including graphs, trees, and vector sets.
A TCAM-Based Parallel Architecture for High-Speed Packet Forwarding
Authors: Mohammad J. Akhbarizadeh, Mehrdad Nourani, Rina Panigrahy, Samar Sharma
Venue: IEEE Transactions on Computers (2007)

A partitioned TCAM-based search engine is presented that increases the packet forwarding rate multiple times over traditional TCAMs. The model works for IPv4 and IPv6 packet forwarding. Unlike the previous art, the improvement is achieved regardless of the incoming traffic pattern. Employing small and private memories that dynamically store popular route prefixes inside the ASIC and taking advantage of the inherent characteristics of Internet traffic to exploit parallelism make this improvement possible. Using four TCAM chips, an embodiment of the proposed model delivered more than six times the throughput of a conventional configuration with equal storage capacity and equal clock rate. Power consumption is also reduced in the new system. Other parameters such as storage density and table update performance are not adversely affected.
Deadlock Free Routing Algorithms for Mesh Topology NoC Systems with Regions
Authors: Rickard Holsmark, Maurizio Palesi, Shashi Kumar
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

Region concept helps to accommodate cores larger than the tile size in mesh topology NoC architectures. In addition, it offers many new opportunities for NoC design, as well as provides new design issues and challenges. The most important among these is the design of a deadlock free routing algorithm. In this paper, we present and compare two routing algorithms for mesh topology NoC with regions. The first algorithm is borrowed from the area of fault tolerant networks and is adapted for the NoC context. We compare this with an algorithm designed using a methodology for design of application specific routing algorithms for communication networks. Our study shows that the application specific routing algorithm not only provides much higher adaptivity, but also superior performance as compared to the other algorithm in all traffic cases.
An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients
Authors: Hyoil Han, Han C. Ryoo, Herbert Patrick
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

This paper proposes an infrastructure for data mining, fusion and patient care management using continuous stream data monitored from critically ill patients. Stream data mining, fusion, and management provide efficient ways to increase data utilization and to support knowledge discovery, which can be utilized in many clinical areas to improve the quality of patient care services. The primary goal of our work is to establish a customized infrastructure model designed for critical care services at hospitals. However this structure can be easily expanded to other areas of clinical specialties.
High LO-RF isolation of zero-IF mixer in 0.18 μm CMOS technology
Authors: Heng-Ming Hsu, Tai-Hsing Lee
Venue: Analog Integrated Circuits and Signal Processing (2006)

In this study, we introduce a zero-IF sub-harmonic mixer with high isolation in the 5 GHz band using 0.18 ¿m CMOS technology. Placing an LC-Tank between the class AB stage and the mixer core improves the isolation between the LO to RF at low supply voltage. The measured isolation is 48 dB between the LO and RF ports, and the 9.5 dB conversion gain is achieved with a supply voltage of 7 mA at 2.5 V. In order to alleviate the degradation of linearity due to the high conversion gain, we adopt the class AB stage as RF input stage. The measured IIP3 is ¿7.5 dBm.
The speciating island model: an alternative parallel evolutionary algorithm
Authors: Steven Gustafson, Edmund K. Burke
Venue: Journal of Parallel and Distributed Computing (2006)

This paper presents an investigation of a novel model for parallel evolutionary algorithms (EAs) based on the biological concept of species. In EA population search, new species represent solutions that could lead to good solutions but are disadvantaged due to their dissimilarity from the rest of the population. The Speciating Island Model (SIM) attempts to exploit new species when they arise by allocating them to new search processes executing on other islands (other processors). The long term goal of the SIM is to allow new species to diffuse throughout a large (conceptual) parallel computer network, where idle and unimproving processors initiate a new search process with them. In this paper, we focus on the successful identification and exploitation of new species and show that the SIM can achieve improved solution quality as compared to a canonical parallel EA.
Deep computing in biology: challenges and progress
Authors: Ajay Royyuru
Venue: Proceedings of the 15th international conference on Parallel architectures and compilation techniques (2006)

The Computational Biology Center at IBM Research pursues basic and exploratory research at the interface of information technology and biology. Information technology plays a vital role in enabling new science and discovery in biology. Advances in high throughput and platform technologies in biology present an unprecedented challenge in scale, management, and analysis of biological data. Advances in computing architecture and scale are enabling simulations of complex biological processes at various organizational levels from atomic to cellular and beyond. High performance computing that takes full advantage of massive parallelism is a necessary means to obtain the performance needed to tackle this complexity. This talk will provide an overview of our current research in computational biology and highlight recent advances in large scale simulations of biological systems.
New Intersection Algorithm of Convex Polygons Based on Voronoi Diagrams
Authors: Chenglei Yang, Pu Shi, Wei Zao, Lu Wang, Xiangxu Meng, Jiaye Wang
Venue: Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering (2006)

A new fast and easy to implement tracing algorithm is presented for querying the intersection points of two convex polygons. We trace two edges to find the intersection points always along the boundaries of the union and intersection of P and Q respectively, which is different from O'Rourke's method that traces two edges always along P and Q respectively; and we utilizes a simple method to decide which edge to be advanced next step by using the space adjacency recording by polygons' outer Voronoi diagrams. Compared with O'Rourke's algorithm considered as the simplest algorithm available usually, the algorithm in this paper is more comprehensible; and it's easy to implement and robust. The algorithm can also be used to query one intersection point of a convex polygon and a general polygon in a special virtual indoor scene to detect collision.
Optimal localization of partially known signals in unknown noise fields
Authors: P. Stoica, M. Viberg, B. Ottersten, T. Kailath
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

Most methods for sensor array signal processing require the covariance matrix of the background noise to be known. Various techniques for overcoming this limitation have recently been proposed. While most of these are based on assumptions on the noise, we present herein an alternative approach based on partial knowledge of the signals. Methods yielding minimum variance estimates for the model in question are presented and analyzed.
A Hybrid Method for Searching Near-Optimal Artificial Neural Networks
Authors: Leandro M. Almeida, Teresa B. Ludermir
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

This paper describes a method for searching nearoptimal neural networks using Genetic Algorithms. The method uses an evolutionary search with the simultaneous selection of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the method is able to produce compact, efficient networks with satisfactory generalization power and shorter training times in comparison to other algorithms.
Bayesian model based parameter estimation and model selection in impulsive noise environments
Authors: J. J. K. Rooney, W. J. Fitzgerald
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

An effective Monte Carlo method using importance sampling is presented to estimate Bayesian model evidence. This is applied to Bayesian model selection in non-Gaussian noise, determining the appropriate signal model and noise statistics simultaneously. The authors also discuss the resolution of two closely spaced frequencies in impulsive noise. The resolution obtained assuming the correct noise statistics is contrasted with that obtained using the fast Fourier transform (FFT) and Gaussian noise assumption. Examples of parameter estimation and model selection in impulsive noise environments are included.
nk-bags: Research Articles
Authors: Kankana Chakrabarty, Ioan Despi
Venue: International Journal of Intelligent Systems (2007)

We generalize the concept of bags by introducing the notion of nk-bags with membership functions ranging in P(N), the power set of positive integers. Consequently, a number of operations on nk-bags are defined and some characterizations are done. &copy; 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 223&ndash;236, 2007.
An Adaptive Neural Sliding Mode Controller for MIMO Systems
Authors: Shiuh-Jer Huang, Kuo-Ching Chiou
Venue: Journal of Intelligent and Robotic Systems (2006)

Here, a novel adaptive neural sliding mode controller (ANSMC) is proposed to handle the coupling and dynamic uncertainty of MIMO systems. The structure of this model-free new controller is based on a radial basis function neural network (RBFNN) which is derived from Lyapunov stability theory and relaxing Kalman---Yacubovich lemma to monitor the system for tracking a user-defined reference model. The weights of RBFNN can be initialized at zero, then, a novel online tuning algorithm is developed based on Lyapunov stability theory. A boundary layer function is introduced into the updating law to cover the parameter errors and modeling errors, and to guarantee the state errors converge into a specified error bound. An e-modification is added into the updating law to release the assumption of persistent excitation and obtain the appropriate values of the connecting weights of a RBFNN. To evaluate the control performance of the proposed controller, a two-link robot system is chosen as the simulation case. The numerical simulations results show that this novel controller has very good tracking accuracy, stability and robustness.
Study on Intelligent Management System of Recovering Scheme for Power Grid Accident
Authors: Liu Fu-chao, Xie Jian-cang, Yue Xinli, Wang Liang, Xiao Gang
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

In this paper, the work of how to auto-create the Recovering Scheme for Power Grid Controller (PGC) in the accident emergency has been researched. Applying the CBR (Case-Based Reasoning) technique into Power Grid Faults management, imitating PGC thinking way in emergency of blackout, the authors develop Power Grid Accident Recovering Model. Due to the different importance of factors, the method of alter-weight KNN (k-nearest neighbor) is used to create the best handling fault scheme. According to Component Technology and MVC (Model View Controller) pattern, a new Recovering Scheme Management System (RSMS) is put forward, and the detailed process of implementation is completed by JSP, JavaBean, Servlet etc. A new technical management way is exploited for PGC in blackout emergency.
A Knowledge-Based Approach for Detecting Unattended Packages in Surveillance Video
Authors: Sijun Lu, Jian Zhang, David Feng
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

This paper describes a novel approach for detecting unattended packages in surveillance video. Unlike the traditional approach to just detecting stationary objects in monitored scenes, our approach detects unattended packages based on accumulated knowledge about human and non-human objects from continuous object tracking and classification. We design different reasoning rules for detecting different scenarios of the unattended package events. In the case where a package is left unattended by a single person explicitly, a rule using human activity recognition is introduced to decide the package ownership. In the case where a suspicious package is dropped down by a group of humans or under heavy occlusions, a rule based on historic tracking and classification information is proposed. Furthermore, an additional rule is given to reduce false alarms that may happen with traditional stationary object detection methods.