ppxscal/embedding_network huggingface dataset:
Efficiency measures for SOC concepts
Authors: Hartwig Jeschke
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

This paper discusses efficiency measures for the evaluation of high performance systems on a chip (SOC), considering a throughput rate R, chip size A, power dissipation P, and a flexibility criterion F. Based on the analysis of recently published multimedia chips, the paper shows equivalences between the ratio of R over AP, a weighted sum on 1/R, A, P, and a fuzzy multicriteria analysis on R, A, P. The paper indicates the fuzzy multicriteria analysis as generalization of the other efficiency measures, which can be easily extended to multiple cost and performance criteria. Because of the application of fuzzy set theory, the multicriteria approach supports quantitative criteria with a physical background as well as qualitative criteria by linguistic variables. Uncertainties at early conceptual phases are considered by a possibilistic measure, which is based on fuzzy set theory.
Static and Dynamic Coalition Formation in Group-Choice Decision Making
Authors: Tom Wanyama
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In Group-Choice Decision Making (GCDM) where a number of stakeholders are involved in choosing a single solution from a set of available solution options, it is common for the stakeholders to form coalition during negotiation in order to increase their individual welfare. It is also common to use Multi-Agent Systems (MAS) to automate GCDM processes. In such MAS, agents have to form coalitions like their human counterparts, and within each coalition, the individual agents behave according to the strategies of their clients. This paper presents a coalition formation engine that has two coalition formation algorithms. One of the algorithms is based on the concept of static coalition formation, and the other is based on the concept of dynamic coalition formation. Moreover, the coalition formation engine is coupled with algorithms that govern the social behavior of the agents in their coalitions, to form an agent negotiation engine. Finally, this paper presents an example and simulation results that illustrate the operational effectiveness of the two coalition formation algorithms, as well as the algorithms that govern the social behavior of the agents.
A multigrid conjugate gradient method
Authors: Christoph Pflaum
Venue: Applied Numerical Mathematics (2008)

In this paper, we describe a new approach to combine the conjugate gradient method and the multigrid method. This approach simultaneously constructs conjugate new correction directions based on restricted gradients. The computational amount is O(N), where N is the number of unknowns. The algorithm is easy to implement. It only requires restriction and prolongation operators, matrix vector multiplications on several levels, and scalar products. Therefore, the algorithm can be applied to accelerate a multilevel algorithm with slow convergence. Numerical results for Poisson's equation with jumping coefficients and a Stokes type equation are presented.
Using Genetic Algorithm to Optimize Artificial Neural Network: A Case Study on Earthquake Prediction
Authors: Qiuwen Zhang, Cheng Wang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

By integrating the global searching advantage of Genetic Algorithm (GA) and the local searching ability of BP Artificial Neural Network (BP ANN), this paper proposes a new model of BP ANN based on GA (called GA-BP ANN). Firstly, it applies GA to optimize the initial interconnecting weights and thresholds of BP ANN. Then, it utilizes the BP algorithm to train the neural network more accurately. This method can speed up the convergence and avoid local minimum of BP ANN. The experiments of earthquake prediction with general BP ANN and optimized GA-BP ANN are respectively conducted as a case study. The results show that the BP ANN optimized with GA can not only get better network configurations, but also improve the efficiency, precision and stability of earthquake prediction.
The identification of users by relational agents
Authors: Daniel Schulman, Mayur Sharma, Timothy Bickmore
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Virtual agents designed to establish relationships with more than one user must be able to identify and distinguish among those users with high reliability. We describe an approach for relational agents in public spaces to identify repeat users based on two strategies: a biometric identification system based on hand geometry, and an identification dialogue that references previous conversations. The ability to re-identify visitors enables the use of persistent dialogue and relationship models, with which the agent can perform a range of behaviors to establish social bonds with users and enhance user engagement. The agent's dialogue encourages users towards repeat visits, and provides mechanisms of recovery from identification errors, as well as contextual information which may be used to improve the accuracy of the biometric identification. We have implemented and evaluated this identification system in a virtual guide agent for a science museum that is designed to conduct repeated and continuing interactions with visitors. We also present the results of a preliminary evaluation of the system, including user opinions of this technology, and of the effect of identification, both successful and unsuccessful, on acceptance and engagement of the agent.
Systematic modeling and reusing of process knowledge for rapid process configuration
Authors: L. Y. Zheng, H. F. Dong, P. Vichare, A. Nassehi, S. T. Newman
Venue: Robotics and Computer-Integrated Manufacturing (2008)

Rapid product development relies heavily on quick and reliable process planning and knowledge reuse. Process configuration borrows the principles of product configuration and applies them to process planning. It allows the reuse of hierarchical, historical and case-based manufacturing process knowledge to generate process plans efficiently and effectively. This paper presents a systematic knowledge model for manufacturing process cases. The model represents process knowledge at different levels of granularity and to facilitate process configuration. With this knowledge model the process knowledge is categorized into 6 levels: (i) core process skeletons, (ii) process networks, (iii) process routes, (iv) process segments, (v) processes/workplan, and (vi) operations/working step. Based on the systematic knowledge model, this paper also proposes an approach to achieve rapid process configuration by reusing the process knowledge and by performing the involved configuration rules so as to create an appropriate, reliable and effective process plan. The prototype for a graphical process planner based on this knowledge model is developed and an aerospace example is used to validate the proposed process knowledge model and process configuration.
Analysis of System Identification Using the Neural Extended Kalman Filter
Authors: Stephen C. Stubberud, Kathleen A. Kramer
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The neural extended Kalman filter (NEKF) is an on-line state estimation technique that can be used to identify differences between the a priori mathematical model and the actual system dynamics. Several applications of this algorithm have been applied to control problems. Two of the approaches seem most promising. The first is to place the NEKF in the feedback loop as an adaptive state estimator. The second is to use the NEKF as a system identification scheme outside the control loop and periodically replacing the model used by the state estimator with that of the NEKF. These two approaches are compared to determine the benefits of each by applying them to an example control system where the system model used in the control law was a mismodeled version of the actual system.
An Approximation Algorithm for the General Mixed Packing and Covering Problem
Authors: Florian Diedrich, Klaus Jansen
Venue: Combinatorics, Algorithms, Probabilistic and Experimental Methodologies: First International Symposium, ESCAPE 2007, Hangzhou, China, April 7-9, 2007, Revised Selected Papers (2007)

We present a price-directive decomposition algorithm to compute an approximate solution of the mixed packing and covering problem; it either finds x ¿ B such that f(x) ≤ c(1 + ¿)a and g(x) ¿ (1 ¿ ¿)b/c or correctly decides that {x ¿ B|f(x) ≤ a, g(x) ¿ b} = ¿. Here f,g are vectors of M ¿ 2 convex and concave functions, respectively, which are nonnegative on the convex compact set ¿ ¿ B ¿ ¿ N ; B can be queried by a feasibility oracle or block solver, a, $b\in \mathbb{R}^{M}_{++}$ and c is the block solver's approximation ratio. The algorithm needs only O(M(ln M + ¿ ¿ 2 ln ¿ ¿ 1)) iterations, a runtime bound independent from c and the input data. Our algorithm is a generalization of [16] and also approximately solves the fractional packing and covering problem where f,g are linear and B is a polytope; there, a width-independent runtime bound is obtained.
Experimental Analysis of Exchange Ratio in Exchange Monte Carlo Method
Authors: Kenji Nagata, Sumio Watanabe
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

In hierarchical learning machines such as neural networks, Bayesian learning provides better generalization performance than maximum likelihood estimation. However, its accurate approximation using a Markov chain Monte Carlo (MCMC) method requires huge computational cost. The exchange Monte Carlo (EMC) method was proposed as an improved algorithm of MCMC method. Although its effectiveness has been shown not only in Bayesian learning but also in many fields, the mathematical foundation of EMC method has not yet been established. In our previous work, we analytically clarified the asymptotic behavior of average exchange ratio, which is used as a criterion for designing the EMC method. In this paper, we verify the accuracy of our result by comparing the theoretical value of average exchange ratio with the experimental value, and propose the method to check the convergence of EMC method based on our theoretical result.
A new approach to obtain the canonical form in two-dimension systems of second order differential equations
Authors: Nicolae-Doru Stanescu
Venue: Proceedings of the 9th WSEAS International Conference on Acoustics & Music: Theory & Applications (2008)

This paper is dedicated to the transformation of the non-linear equations to the form used in their analytical approach. We presented the case of a system with two non-linear differential equations of second order and we give necessary and sufficient conditions to bring them at the form used for the analytical approximation of the solution. We also present an example and finally a few conclusions.
Transparency Analysis of Bilateral Controllers Based on the Methodology of State Convergence
Authors: Juan M. Bogado, Manuel Ferre, Rafael Aracil, Jose M. Azorin, Eva M. Fernández, José Baca
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

This paper shows a new methodology for bilateral controller design based on transparency using a modified state convergence control scheme. This methodology offers some advantages upon designing bilateral systems. The design is based on modelling the behaviour of the master and the slave by applying state space equations, and considering perfect transparency cannot be reached. Therefore, the objective of the controllers is to guarantee the convergence of the master and slave status. This paper describes the criteria to achieve transparency on steady state.
A Texture-Based Hardware-Independent Technique for Time-Varying Volume Flow Visualization
Authors: Zhanping Liu, Robert J. Moorhead,  II
Venue: Journal of Visualization (2005)

Existing texture-based 3D flow visualization techniques, e.g., volume Line Integral Convolution (LIC), are either limited to steady flows or dependent on special-purpose graphics cards. In this paper we present a texture-based hardware-independent technique for time-varying volume flow visualization. It is based on our Accelerated Unsteady Flow LIC (AUFLIC) algorithm (Liu and Moorhead, 2005), which uses a flow-driven seeding strategy and a dynamic seeding controller to reuse pathlines in the value scattering process to achieve fast time-dependent flow visualization with high temporal-spatial coherence. We extend AUFLIC to 3D scenarios for accelerated generation of volume flow textures. To address occlusion, lack of depth cuing, and poor perception of flow directions within a dense volume, we employ magnitude-based transfer functions and cutting planes in volume rendering to clearly show the flow structure and the flow evolution.
Engineering User Requirements for e-Government Services: A Dutch Case Study
Authors: Lex Velsen, Thea Geest, Marc Hedde, Wijnand Derks
Venue: Proceedings of the 7th international conference on Electronic Government (2008)

E-Government services can be made more user-friendly by involving prospective users in the requirements engineering stage. In this paper, we present user requirements engineering activities for e-Government services, demonstrate their effectiveness by means of a case study, and reflect upon their use. We used a combination of interviews with clients and involved service employees, which resulted in a set of requirements covering the different needs of future users. The design based on these requirements was tested with fifteen potential end-users, using rapid prototyping and a citizen walkthrough. These sessions show that it is crucial to test user requirements with potential end-users to create an e-Service that successfully caters to clients, providing such things as personalization and interoperability in an e-Government setting.
Active Learning with Support Vector Machines for Tornado Prediction
Authors: Theodore B. Trafalis, Indra Adrianto, Michael B. Richman
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this paper, active learning with support vector machines (SVMs) is applied to the problem of tornado prediction. This method is used to predict which storm-scale circulations yield tornadoes based on the radar derived Mesocyclone Detection Algorithm (MDA) and near-storm environment (NSE) attributes. The main goal of active learning is to choose the instances or data points that are important or have influence to our model to be labeled and included in the training set. We compare this method to passive learning with SVMs where the next instances to be included to the training set are randomly selected. The preliminary results show that active learning can achieve high performance and significantly reduce the size of training set.
Bio2RDF: Towards a mashup to build bioinformatics knowledge systems
Authors: François Belleau, Marc-Alexandre Nolin, Nicole Tourigny, Philippe Rigault, Jean Morissette
Venue: Journal of Biomedical Informatics (2008)

Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://bio2rdf.org/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://bio2rdf.org.
Agent-community-network-based business matching and collaboration support system
Authors: Tsunenori Mine, Kosaku Kimura, Satoshi Amamiya, Ken'ichi Takahashi, Makoto Amamiya
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track (2008)

Business matching and collaboration support systems are useful, in particular for small-and-medium sized companies. Most of them developed so far are based on the server-client architecture and provide their services on Web servers. They require special administrative facilities, ask users to upload their data for matching between business needs and seeds, and leave to themselves peer-to-peer communication or negotiation between matched companies. Considering these problems, we have been developing an agent-community-network-based business matching and collaboration support system. Our system requires neither any special administrative facilities nor uploading user data to a special server. It furthermore supports secure peer-to-peer communication between users. It is implemented with multi-agent Kodama framework.
Optimal Field Splitting with Feathering in Intensity-Modulated Radiation Therapy
Authors: Xiaodong Wu, Xin Dou
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

In this paper, we study an interesting geometric partition problem, called optimal field splitting with feathering (OFSF), which arises in Intensity-Modulated Radiation Therapy (IMRT). In current clinical practice, a multi-leaf collimator (MLC) with a maximum field size is used to deliver the prescribed intensity maps (IMs). However, the maximum field size of an MLC may require to split a large intensity map into several overlapping sub-IMs each being delivered separately, which may result in sacrificed treatment quality. Few IM splitting techniques reported in the literature have addressed the issue of treatment delivery accuracy for large IMs. We develop a new algorithm for solving the OFSF problem while minimizing the total delivery error. Our basic idea is to formulate the OFSF problem as computing a d-link shortest path in a directed acyclic graph, which expresses a special "layered" structure. The edge weights of the graph satisfy the Monge property, which enables us to solve this d-link shortest path problem by examining only a small portion of the graph, yielding an optimal linear time algorithm for the OFSF problem.
Plasticity in the adult human auditory brainstem following short-term linguistic training
Authors: Judy H. Song, Erika Skoe, Patrick C. M. Wong, Nina Kraus
Venue: Journal of Cognitive Neuroscience (2008)

Peripheral and central structures along the auditory pathway contribute to speech processing and learning. However, because speech requires the use of functionally and acoustically complex sounds which necessitates high sensory and cognitive demands, long-term exposure and experience using these sounds is often attributed to the neocortex with little emphasis placed on subcortical structures. The present study examines changes in the auditory brainstem, specifically the frequency following response (FFR), as native English-speaking adults learn to incorporate foreign speech sounds (lexical pitch patterns) in word identification. The FFR presumably originates from the auditory midbrain and can be elicited preattentively. We measured FFRs to the trained pitch patterns before and after training. Measures of pitch tracking were then derived from the FFR signals. We found increased accuracy in pitch tracking after training, including a decrease in the number of pitch-tracking errors and a refinement in the energy devoted to encoding pitch. Most interestingly, this change in pitch-tracking accuracy only occurred in the most acoustically complex pitch contour (dipping contour), which is also the least familiar to our English-speaking subjects. These results not only demonstrate the contribution of the brainstem in language learning and its plasticity in adulthood but also demonstrate the specificity of this contribution (i.e., changes in encoding only occur in specific, least familiar stimuli, not all stimuli). Our findings complement existing data showing cortical changes after second-language learning, and are consistent with models suggesting that brainstem changes resulting from perceptual learning are most apparent when acuity in encoding is most needed.
Multi-source Data Modelling: Integrating Related Data to Improve Model Performance
Authors: Paul R. Trundle, Daniel C. Neagu, Qasim Chaudhry
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Traditional methods in Data Mining cannot be applied to all types of data with equal success. Innovative methods for model creation are needed to address the lack of model performance for data from which it is difficult to extract relationships. This paper proposes a set of algorithms that allow the integration of data from multiple datasets that are related, as well as results from the implementation of these techniques using data from the field of Predictive Toxicology. The results show significant improvements when related data is used to aid in the model creation process, both overall and in specific data ranges. The proposed algorithms have potential for use within any field where multiple datasets exist, particularly in fields combining computing, chemistry and biology.
An Interleave Principle for Demonstrating concurrent Programs
Authors: T. G. Lewis
Venue: IEEE Software (1984)

Although they want to know whether their concurrent programs are working properly, programmers may not need a rigorous proof of correctness.
An Adaptive Privacy Preserving Data Mining Model under Distributed Environment
Authors: Feng Li, Jin Ma, Jian-hua Li
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Privacy preserving becomes an important issue in the development progress of data mining techniques, especially in distributed data mining. Secure multiparty computation methods are proposed to protect the privacy in distributed environment, but shows low performance under massive nodes. This paper presents an adaptive privacy preserving data mining model based on data perturbation method to improve the efficiency while preserving the privacy. Security capability of basic data perturbation is firstly analyzed and an adaptive enhancement method is proposed according to the eigen value decomposition based attacks. A light-weight protocol with homomorphic technique is proposed to perform the perturbation process under distributed environments. The experiment results show that the model has high controllable security and shows more efficiency in large scale distribution environment comparing to secure multiparty related methods.