ppxscal/embedding_network huggingface dataset:
Dynamic Resizing of Parallel Scientific Simulations: A Case Study Using LAMMPS
Authors: Rajesh Sudarsan, Calvin J. Ribbens, Diana Farkas
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

Large-scale computational science simulations are a dominant component of the workload on modern supercomputers. Efficient use of high-end resources for these large computations is of considerable scientific and economic importance. However, conventional job schedulers limit flexibility in that they are `static', i.e., the number of processors allocated to an application can not be changed at runtime. In earlier work, we described ReSHAPE, a system that eliminates this drawback by supporting dynamic resizability in distributed-memory parallel applications. The goal of this paper is to present a case study highlighting the steps involved in adapting a production scientific simulation code to take advantage of ReSHAPE. LAMMPS, a widely used molecular dynamics code, is the test case. Minor extensions to LAMMPS allow it to be resized using ReSHAPE, and experimental results show that resizing significantly improves overall system utilization as well as performance of an individual LAMMPS job.
An Italian high speed train noise analysis in an open country environment
Authors: J. Quartieri, A. Troisi, C. Guarnaccia, T. Lenza, P. D'Agostino, S. D'Ambrosio, G. Iannone
Venue: Proceedings of the 10th WSEAS international conference on Acoustics & music: theory & applications (2009)

In a recent paper of some of us, an experimental activity regarding the detection of noise produced by a high velocity railway has been performed and compared to a semi-quantitative theoretical model which gives the shape of train noise in certain running condition. In this paper we further develop this analysis, taking into account prediction coming from the software CadnaA by Datakustik, and checking its response with respect to the experimental data. This study can allow to test the commercial software performances in some particular contexts, in order to exploit its powerful tools to calculate noise maps of more complex frameworks. In addition we investigate the noise prediction of high velocity trains, descending by a suitable geometrical model which considers the acoustical emission of a linear source endowed with a suitable acoustical power. In this sense, we have even checked a more complicate scheme describing the acoustic emission of a train with the combination of a pointlike source and a linear one, in order to describe respectively the railcar and the train vehicles.
Research a Novel Optimization Mechanism of Parameters Based on Hybrid NN and GA
Authors: Yansong Liu, Rulong Wang, Gang Yi
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

In this paper, an optimization system is established based on a hybrid neural network and genetic algorithm approach. The application program is compiled in Matlab engineering computing language, which is used in calculating the parameter value predicted by neural network and the result of genetic algorithm optimization .The comparison and error analysis has been carried out between the results predicted by network and CAE simulated results, which shows that the BP network is stable and reliable. The optimized outcome verified by CAE simulation and tested by experiment has been proved to be correct. It has been bean indicated that the injection parameter optimization method based on the hybrid neural network and genetic algorithm approach is feasible.
Plenary lecture 10: computational intelligence techniques in solving power system problems
Authors: Ismail Musirin
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

This plenary lecture presents the application of computational intelligence techniques in solving power system problems. In power system, the transmission grid network is normally very large and bulky; therefore the on-line study is normally not recommended due to their complexity. In order to solve this, offline study is the most recommended technique; representing the system models into their mathematical relationship and solve them using any optimization techniques. Amongst the p?pular computational intelligence techniques are Genetic Algorithm (GA), Evolutionary Programming (EP), Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Ant Colony Optimization Technique (ACO). In this lecture, the applications of EP, AIS, PSO and ACO in solving various power system optimization problems will be addressed, such as; maximum loadability identification for power system load, reactive power planning schemes and economic power dispatch. In maximum loadability identification, the maximum permissible load is optimized for several selected load buses; which allows the power system operators to identify the weak and secure buses in the system. Reactive power planning is a scheme used to alleviate voltage limit in a system through voltage stability improvement technique and/or loss minimization. Implementations on reliability test systems revealed their feasibility for further implementation on practical systems. On the other hand, economic power dispatch is used to determine the amount of power to be dispatched by the generators within the acceptable economic constraints. Comparative studies of various optimization techniques highlighted the most suitable computational intelligence optimization techniques for a particular power system problem.
In the Virtual Extension
Authors: CACM Staff
Venue: Communications of the ACM (2009)

Communications' Virtual Extension brings more quality articles to ACM members. These articles are now available in the ACM Digital Library.
DynaMMo: mining and summarization of coevolving sequences with missing values
Authors: Lei Li, James McCann, Nancy S. Pollard, Christos Faloutsos
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values. We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.
Study on Steering Control Strategy of Electric Vehicles Driven by Hub-Motors
Authors: Yong Chen, Zhongkui Lu, Daming Zhang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

A control strategy applied to steering control was used to an electric vehicle driven by two hub-motors, which distributes the driving torque of the driving wheels according to control of actual yaw rate. The proposed fuzzy PID controller adjusts the actual yaw rate to the desired yaw rate. Comparisons between fuzzy PID controller, PID controller and no controller are performed. Simulation results show that the performance of fuzzy PID controller is satisfactory, which is helpful to improve the steering stability.
From Algorithm to Agent
Authors: Krzysztof Cetnarowicz
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Although the notion of an agent has been used in computer science for a dozens of years now it is still not very well defined. It seems that there is a lack of formal definition of such concepts as an "object" and an "agent". It makes difficult formal analysis of algorithms developed with their use. We should find more formal description that has connection with the basic definition of the algorithm.In the paper we will propose an approach that may help to develop more formal definitions of an agent and an object with the use of algorithm concept. Starting from the notion of the algorithm and using the observation that complex algorithm should be developed with the use of its decomposition we propose some ideas how we can consider such notions as object and agent.Proposed approach takes into consideration the necessity of the autonomy and of an agent and object and the problems of the interactions between them and suggest the resolution of the problems by communication and observation process.Presented concept of an object and an agent makes possible to find further more formal definitions of these notions and find the crucial properties of these concepts and the main difference between the notion of an object and the notion of an agent.
Knowledge Management to Support Situation-aware Risk Management in Autonomous, Self-managing Agents
Authors: Martin Lorenz, Jan D. Gehrke, Joachim Hammer, Hagen Langer, Ingo J. Timm
Venue: Proceeding of the 2005 conference on Self-Organization and Autonomic Informatics (I) (2005)

We present a novel approach to enable decision-making in a highly distributed multiagent environment where individual agents need to act in an autonomous fashion. Our architecture framework integrates risk management, knowledge management, and agent deliberation to enable sophisticated, autonomous decision-making. Instead of a centralized knowledge repository, our approach supports a highly distributed knowledge base in which each agent manages a fraction of the knowledge needed by the entire system. Our approach also addresses the fact that the desired knowledge is often highly dynamic, context-sensitive, incomplete, or uncertain. Thus risk management becomes an integral component which enables context-based, situation-aware decision making, which in turn supports autonomous, self-managing behavior of the agents. A prototype system demonstrating the feasibility of our approach is being developed as part of an ongoing funded research project.
Method for land consolidation progress assessment based on GPS and PDA
Authors: Guangming Zhu, Daoliang Li
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

Field survey is a traditional method in the process of land consolidation. But it has such as low efficiency, long time, and some other shortcomings in measuring length, area and calculating the amount of small object, especially for the project area with more complex terrain. The paper puts forward using GPS/PDA to calculate progress for project of land consolidation, which propels the application of GPS/PDA in the fields of land consolidation. The paper introduces the principle of GPS, interface of GPS and PDA, realize real time interaction between the field of project area and the electronic maps by using GPS's location function, on the basis of which accomplishes real time judgment for project land features' progress, quantities and some other accuracy of information.
Handbook of Research on Instructional Systems and Technology
Authors: Terry Kidd, Terry Kidd, Holim Song
Venue:  (2007)

There is substantial effort and research within various disciplines to tackle the current issues and trends in instructional systems and technology that deal with the improvement of computer- and technology-assisted learning settings and environments, online and distance education, technology and cognition, as well as the multicultural and cross-cultural aspects of instructional technology and learning. The Handbook of Research on Instructional Systems and Technology addresses these issues by providing information on the different styles of instructional design methodologies, tips, and strategies on how to use technology to facilitate active learning as well as techniques to help faculty and researchers develop online instructional and teaching materials. This Handbook of Research enables libraries to provide a foundational reference to meet the information needs of researchers, educators, practitioners, administrators, and all other stakeholders in the context of instructional systems and technology.
Software-based Memory Protection on OSEK/VDX- The KESO Multi-JVM
Authors: Michael Stilkerich
Venue:  (2007)

Electronic support functions pose the current major area of innovation in the automotive environment. The number of ECUs has reached an upper boundary with about 80 ECUs deployed in a well-equipped car. The industry aims at consolidating the number of ECUs by shifting from a federated architecture to an integrated architecture. The integration of multiple applications on an ECU raises the need for isolation mechanisms that allow for a safe coexistance. The embedded Multi-JVM KESO is a purely software-based approach to provide memory protection through the use of the type-safe Java language along with other isolation machanisms. KESO is based on an OSEK/VDX operating system, the defacto standard in the automotive industry, and compiles Java code to C-Code ahead-of-time, which allows the use of existing compiler and analyzing tools. This work covers the Java interface to the OSEK/VDX system services and a heap management strategy featuring low-latency incremental garbage collection, but also gives a general KESO overview. 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
Using task context to achieve effective information delivery
Authors: Jose Manuel Gomez-Perez, Marko Grobelnik, Carlos Ruiz, Marcel Tilly, Paul Warren
Venue: Proceedings of the 1st Workshop on Context, Information and Ontologies (2009)

The use of task context to guide the delivery of information to knowledge workers is valuable for improving their efficiency and effectiveness. Moreover, the sharing of context between individuals can aid the sharing of knowledge. This paper describes research in the ACTIVE project which uses context to support information delivery and sharing. Machine intelligence techniques are used to learn the association between information objects and context; and to learn how to partition a user's information objects into a set of contexts. Informal processes are also important to knowledge workers, and another research challenge is to understand how context influences the choice of steps in a process. Other research questions relate to the user interface for context-driven information delivery. Chief amongst these questions is whether the user wishes or needs to be aware of the concepts of context and process; or whether only the system should be aware.
Performance Evaluation of Weighted Fair Queuing System Using Matrix Geometric Method
Authors: Amina Al-Sawaai, Irfan Awan, Rod Fretwell
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

This paper analyses a multiple class single server M/M/1/K queue with finite capacity under weighted fair queuing (WFQ) discipline. The Poisson process has been used to model the multiple classes of arrival streams. The service times have exponential distribution. We assume each class is assigned a virtual queue and incoming jobs enter the virtual queue related to their class and served in FIFO order.We model our system as a two dimensional Markov chain and use the matrix-geometric method to solve its stationary probabilities. This paper presents a matrix geometric solution to the M/M/1/K queue with finite buffer under (WFQ) service. In addition, the paper shows the state transition diagram of the Markov chain and presents the state balance equations, from which the stationary queue length distribution and other measures of interest can be obtained. Numerical experiments corroborating the theoretical results are also offered.
On the satisfiability threshold of formulas with three literals per clause
Authors: J. Díaz, L. Kirousis, D. Mitsche, X. Pérez-Giménez
Venue: Theoretical Computer Science (2009)

In this paper we present a new upper bound for randomly chosen 3-CNF formulas. In particular we show that any random formula over n variables, with a clauses-to-variables ratio of at least 4.4898 is, as n grows large, asymptotically almost surely unsatisfiable. The previous best such bound, due to Dubois in 1999, was 4.506. The first such bound, independently discovered by many groups of researchers since 1983, was 5.19. Several decreasing values between 5.19 and 4.506 were published in the years between. We believe that the probabilistic techniques we use for the proof are of independent interest.
Study on Rough set-based Clustering Result Presentation Method for High Dimensional Data Space
Authors: Jianbin Chen, Yinmin Gao
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Since the internal structure of data set is unknown before clustering, the clusters should be presented properly, so that user can get the result completely and accomplish the task of knowledge discovering. The presentation and explanation of the clustering result play an important role in the clustering process. Based on the study of rough set, the rough set theory on attribute space is imported and a new clustering result presentation method is advanced, with the different property consideration of the object space and attribute space of high dimensional data set. This method can provide relatively synthesis information of clustering result from object space and attribute space, reflect the clustering knowledge with rules, enable users to capture more useful pattern and to hold the internal structure of data sets.
Video Compression and Communications: From Basics to H.261, H.263, H.264, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers, 2nd edition
Authors: Lajos Hanzo, Peter Cherriman, Jurgen Streit
Venue:  (2007)

Since the publication of Wireless Video Communications five years ago, the area of video compression and wireless transceivers has evolved even further. This new edition addresses a range of recent developments in these areas, giving cognizance to the associated transmission aspects and issues of error resilience. Video Compression and Communications has been updated and condensed yet remains all-encompassing, giving a comprehensive overview of the subject. Covering compression issues, coding delay, implementational complexity and bitrate, the book also looks at the historical perspective to video communication. New edition of successful and informative text, Wireless Video Communications Substantial new material has been added on areas such as H.264, MPEG4 coding and transceivers Clear presentation and broad scope make it essential for anyone interested in wireless communications Systematically converts the lessons of Shannon's information theory into design principles applicable to practical wireless systems. This book is ideal for postgraduates and researchers in communication systems but will also be a valuable reference to undergraduates, development and systems engineers of video compression applications as well as industrialists, managers and visual communications practitioners.
Pose Normalization for Local Appearance-Based Face Recognition
Authors: Hua Gao, Hazım Kemal Ekenel, Rainer Stiefelhagen
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

We focused this work on handling variation in facial appearance caused by 3D head pose. A pose normalization approach based on fitting active appearance models (AAM) on a given face image was investigated. Profile faces with different rotation angles in depth were warped into shape-free frontal view faces. Face recognition experiments were carried out on the pose normalized facial images with a local appearance-based approach. The experimental results showed a significant improvement in accuracy. The local appearance-based face recognition approach is found to be robust against errors introduced by face model fitting.
Architectures for dynamic data scaling in 2/4/8K pipeline FFT cores
Authors: Thomas Lenart, Viktor Öwall
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2006)

This paper presents architectures for supporting dynamic data scaling in pipeline fast Fourier transforms (FFTs), suitable when implementing large size FFTs in applications such as digital video broadcasting and digital holographic imaging. In a pipeline FFT, data is continuously streaming and must, hence, be scaled without stalling the dataflow. We propose a hybrid floating-point scheme with tailored exponent datapath, and a co-optimized architecture between hybrid floating point and block floating point (BFP) to reduce memory requirements for 2-D signal processing. The presented co-optimization generates a higher signal-to-quantization-noise ratio and requires less memory than for instance convergent BFP. A 2048-point pipeline FFT has been fabricated in a standard-CMOS process from AMI Semiconductor (Lenart and Öwall, 2003), and a field-programmable gate array prototype integrating a 2-D FFT core in a larger design shows that the architecture is suitable for image reconstruction in digital holographic imaging.
Scalable join processing on very large RDF graphs
Authors: Thomas Neumann, Gerhard Weikum
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

With the proliferation of the RDF data format, engines for RDF query processing are faced with very large graphs that contain hundreds of millions of RDF triples. This paper addresses the resulting scalability problems. Recent prior work along these lines has focused on indexing and other physical-design issues. The current paper focuses on join processing, as the fine-grained and schema-relaxed use of RDF often entails star- and chain-shaped join queries with many input streams from index scans. We present two contributions for scalable join processing. First, we develop very light-weight methods for sideways information passing between separate joins at query run-time, to provide highly effective filters on the input streams of joins. Second, we improve previously proposed algorithms for join-order optimization by more accurate selectivity estimations for very large RDF graphs. Experimental studies with several RDF datasets, including the UniProt collection, demonstrate the performance gains of our approach, outperforming the previously fastest systems by more than an order of magnitude.
Teaching the evolution of behavior with SuperDuperWalker
Authors: Lee Spector, Jon Klein, Kyle Harrington, Raymond Coppinger
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

SuperDuperWalker is a software-based framework for experiments on the evolution of locomotion. It simulates the behavior of evolving agents in a 3D physical simulation environment and displays this behavior graphically in real time. A genetic algorithm controls the evolution of the agents. Students manipulate parameters with a graphical user interface and plot outputs using standard utilities. The software supports an inquiry cycle that has been piloted in CS193T: Biocomputational Developmental Ecology at Hampshire College.