ppxscal/embedding_network huggingface dataset:
Linearly scaling 3D fragment method for large-scale electronic structure calculations
Authors: Lin-Wang Wang, Byounghak Lee, Hongzhang Shan, Zhengji Zhao, Juan Meza, Erich Strohmaier, David H. Bailey
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

We present a new linearly scaling three-dimensional fragment (LS3DF) method for large scale ab initio electronic structure calculations. LS3DF is based on a divide-and-conquer approach, which incorporates a novel patching scheme that effectively cancels out the artificial boundary effects due to the subdivision of the system. As a consequence, the LS3DF program yields essentially the same results as direct density functional theory (DFT) calculations. The fragments of the LS3DF algorithm can be calculated separately with different groups of processors. This leads to almost perfect parallelization on over one hundred thousand processors. After code optimization, we were able to achieve 60.3 Tflop/s, which is 23.4% of the theoretical peak speed on 30,720 Cray XT4 processor cores. In a separate run on a BlueGene/P system, we achieved 107.5 Tflop/s on 131,072 cores, or 24.2% of peak. Our 13,824-atom ZnTeO alloy calculation runs 400 times faster than a direct DFT calculation, even presuming that the direct DFT calculation can scale well up to 17,280 processor cores. These results demonstrate the applicability of the LS3DF method to material simulations, the advantage of using linearly scaling algorithms over conventional O(N3) methods, and the potential for petascale computation using the LS3DF method.
Evolving fuzzy classifiers using different model architectures
Authors: P. Angelov, E. Lughofer, X. Zhou
Venue: Fuzzy Sets and Systems (2008)

In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called eClass and FLEXFIS-Class. Both methods can be applied with different model architectures, including single model (SM) with class labels as consequents, classification hyper-planes as consequents, and multi-model (MM) architecture. Additionally, eClass can have a multi-input-multi-output (MIMO) architecture with multiple hyper-planes as consequents of each fuzzy rule. The difference between MM and MIMO architectures is that the former one applies one separate and independent fuzzy rule-based (FRB) classifier for each class and is using an indicator labelling scheme, while the latter one applies a single FRB where the rules are MIMO rather than MISO. Both, eClass and FLEXFIS-Class methods are designed to work on a per-sample basis and are thus one-pass, incremental. Additionally, their structure (FRB) is evolving rather than fixed. It adapts their parameters in antecedent and consequent parts with any newly loaded sample. A special emphasis is placed on advanced issues for improving accuracy and robustness, including a thorough comparison between global and local learning of consequent functions, a novel approach for detecting of and reacting on drifts in the data streams and an enhanced outlier treatment strategy. The methods and their extensions according to the advanced issues are evaluated on one benchmark problem of handwritten images recognition as well as on a real-life problem of image classification framework, where images should be classified into good and bad ones during an on-line and interactive production process.
Global Asymptotical Stability for Neural Networks with Multiple Time-Varying Delays
Authors: Jianlong Qiu, Jinde Cao, Zunshui Cheng
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, the global uniform asymptotical stability is studied for neural networks with multiple time-varying delays by constructing appropriate Lyapunov-Krasovskii functional and using the linear matrix inequality (LMI) approach. The restriction on the derivative of the time-varying delay function ¿ ij (t) to be less than unit is removed by using slack matrix method. A numerical example is provided to demonstrate the effectiveness and applicability of the proposed criteria.
Kinetic Pseudo-energy History for Human Dynamic Gestures Recognition
Authors: Luis Unzueta, Oscar Mena, Basilio Sierra, Ángel Suescun
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

In this paper we present a new approach, based on the kinetic status history, to automatically determine the starting and ending instants of human dynamic gestures. This method opens up the possibility to distinguish static or quasi-static poses from dynamic actions, during a real-time human motion capture. This way a more complex Human-Computer Interaction (HCI) can be attained. Along with this procedure, we also present a novel method to recognize dynamic gestures independently from the velocity with which they have been performed. The efficiency of this approach is tested with gestures captured with a triple axis accelerometer, and recognized with different statistical classifiers, obtaining satisfactory results for real-time applications.
FORTRAN codes to implement enhanced local wave number technique to determine the depth and location and shape of the causative source using magnetic anomaly
Authors: B. N. P. Agarwal, Shalivahan Srivastava
Venue: Computers Geosciences (2008)

The total field magnetic anomaly is analyzed to compute the depth and location and geometry of the causative source using two FORTRAN source codes, viz., FRCON1D and ELW. No assumption on the nature of source geometry, susceptibility contrast, etc. has been made. The source geometry is estimated by computing the structural index from previously determined depth and location. A detailed procedure is outlined for using these codes through a theoretical anomaly. The suppression of high-frequency noise in the observed data is tackled by designing a box-car window with cosine termination. The termination criterion is based on the peak position of the derivative operator computed for a pre-assumed depth of a shallow source below which the target is situated. The applicability of these codes has been demonstrated by analyzing a total field aeromagnetic anomaly of the Matheson area of northern Ontario, Canada.
Inference of differential equation models by genetic programming
Authors: Hitoshi Iba
Venue: Information Sciences: an International Journal (2008)

This paper describes an evolutionary method for identifying a causal model from the observed time-series data. We use a system of ordinary differential equations (ODEs) as the causal model. This approach is known to be useful for practical applications, e.g., bioinformatics, chemical reaction models, control theory, etc. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by genetic programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe an extension of the approach to the inference of differential equation systems with transcendental functions.
Modeling and managing collective cognitive convergence
Authors: H. V. Parunak, T. C. Belding, R. Hilscher, S. Brueckner
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

When the same people interact frequently, they come to think alike, a phenomenon we call "collective cognitive convergence" (C3). We discuss instances and practical consequences of this phenomenon; review previous work in sociology, computational social science, and evolutionary biology that sheds light on C3; define a computational model and metrics for the convergence process; report on experiments with this model and metrics; and suggest how insights from the model can help manage C3.
Preprocessing Expression-Based Constraint Satisfaction Problems for Stochastic Local Search
Authors: Sivan Sabato, Yehuda Naveh
Venue: Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2007)

This work presents methods for processing a constraint satisfaction problem (CSP) formulated by an expression-based language, before the CSP is presented to a stochastic local search solver. The architecture we use to implement the methods allows the extension of the expression language by user-defined operators, while still benefiting from the processing methods. Results from various domains, including industrial processor verification problems, show the strength of the methods. As one of our test cases, we introduce the concept of random-expression CSPs as a new form of random CSPs. We believe this form emulates many real-world CSPs more closely than other forms of random CSPs. We also observe a satisfiability phase transition in this type of problem ensemble.
Blind Separation of Positive Signals by Using Genetic Algorithm
Authors: Mao Ye, Zengan Gao, Xue Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

When the source signals are known to be independent, positive and well-grounded which means that they have a non-zero pdf in the region of zero, a few algorithms have been proposed to separate these positive sources. However, in many practical cases, the independent assumption is not always satisfied. In this paper, a new approach is proposed to separate a class of positive sources which are not required to be independent. These source signals can be separated very quickly by using genetic algorithm. The objective function of genetic algorithm is derived from uncorrelated and some special assumptions on such positive source signals. Simulations are employed to illustrate the good performance of our algorithm.
A fuzzy logic based hierarchical driver aid for parallel parking
Authors: Tarik Ozkul, Mohammed Mukbil, Suheyl Al-Dafri
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

This paper is about a fuzzy logic based driver aid which helps drivers perform flawless parallel parking. The tool gets its input from two low resolution cameras placed strategically outside the vehicle. The images are processed by an on-board CPU and crucial information necessary for parking is extracted from the scenery. A fuzzy logic based algorithm processes the information and make decisions about how the vehicle should be steered and what the speed of movement should be. The aid is a real time tool which provides recommendations on the fly as the vehicle moves. In case recommendations are not executed perfectly by the driver, instructions are reevaluated, updated and presented until the vehicle is parked acceptably.
X3D-UML: 3D UML State Machine Diagrams
Authors: Paul Mcintosh, Margaret Hamilton, Ron Schyndel
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

X3D-UML utilises X3D (eXtensible 3D) to enable standards-based advanced 3D UML visualisations. Using X3D-UML, 3D UML State Machine Diagrams have been evaluated against actual user tasks and data, using the Sequential Evaluation methodology. The results of User Task Analysis, Heuristic Evaluation and Formative Evaluation phases provide clear evidence that the use of UML extended with 3D is a practical solution for visualising complex system behaviour. RoseRT model metrics show between 56%-90% of state machine diagram work would benefit from such 3D UML extensions; hence the 3D improvement can deliver considerable benefit to organisations.
Flexible and robust three-dimensional face recognition
Authors: Kevin W. Bowyer, Patrick J. Flynn
Venue:  (2008)

Face recognition is one of the least intrusive modalities in biometrics. Our research shows that using 3D face data for recognition provides a promising route to improved performance. In this dissertation, we introduce a new system for 3D face recognition that addresses many of the current challenges preventing this technology from becoming viable. The first challenge is to determine the feasibility of combining data from multiple sensors. Next, we create a technique named Region Ensemble for FacE Recognition (REFER) that is capable of matching 3D face images in the presence of expressions and occlusion. Accurate feature detection and pose recognition is accomplished through a novel technique that we have created, named Rotated Profile Signatures (RPS). Scalability issues are mitigated by combining a feature-based indexing technique with desktop grid processing to greatly reduce the amount of time required for recognition experiments. Finally, we investigate the potential benefits of using a multi-instance enrollment approach that can be used to further increase the performance of our system. These solutions result in a final system that is capable of deployment in a variety of realistic biometric scenarios.
Fixed-Parameter Algorithms for Kemeny Scores
Authors: Nadja Betzler, Michael R. Fellows, Jiong Guo, Rolf Niedermeier, Frances A. Rosamond
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

The Kemeny Score problem is central to many applications in the context of rank aggregation. Given a set of permutations (votes) over a set of candidates, one searches for a "consensus permutation" that is "closest" to the given set of permutations. Computing an optimal consensus permutation is NP-hard. We provide first, encouraging fixed-parameter tractability results for computing optimal scores (that is, the overall distance of an optimal consensus permutation). Our fixed-parameter algorithms employ the parameters "score of the consensus", "maximum distance between two input permutations", and "number of candidates". We extend our results to votes with ties and incomplete votes, thus, in both cases having no longer permutations as input.
A WeVoS-CBR Approach to Oil Spill Problem
Authors: Emilio Corchado, Bruno Baruque, Aitor Mata, Juan M. Corchado
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The hybrid intelligent system presented here, forecasts the presence or not of oil slicks in a certain area of the open sea after an oil spill using Case-Based Reasoning methodology. The proposed CBR includes a novel network for data classification and data retrieval. Such network works as a summarization algorithm for the results of an ensemble of Visualization Induced Self-Organizing Maps. This algorithm, called Weighted Voting Superposition (WeVoS), is mainly aimed to achieve the lowest topographic error in the map. The system uses information obtained from various satellites such as salinity, temperature, pressure, number and area of the slicks. WeVoS-CBR system has been able to accurately predict the presence of oil slicks in the north west of the Galician coast, using historical data.
Specifying Service Composition Using UML 2.x and Composition Policies
Authors: Judith E. Rossebø, Ragnhild Kobro Runde
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

In the current and future service environment, service parts are being developed separately while being dynamically combined at runtime. In this paper we address the problem of defining a model-driven process for enabling dynamic composition of services. Composition policies are used to define choices in behaviour under which service roles involved in a composite service can be dynamically combined at runtime. We model policy-ruled choreography of collaboration components using a policy enforcement state machine (PESM). We also define transformation rules for translating a global PESM diagram into a set of local PESM diagrams, one for each role. As an example, we consider the case of dynamically composing an existing service with a set of authentication and authorization collaborations. The approach is supported by a formal syntax and semantics.
Relationally Periodic Sequences and Subword Complexity
Authors: Julien Cassaigne, Tomi Kärki, Luca Q. Zamboni
Venue: Proceedings of the 12th international conference on Developments in Language Theory (2008)

By the famous theorem of Morse and Hedlund, a word is ultimately periodic if and only if it has bounded subword complexity, i.e., for sufficiently large n, the number of factors of length n is constant. In this paper we consider relational periods and relationally periodic sequences, where the relation is a similarity relation on words induced by a compatibility relation on letters. We investigate what would be a suitable definition for a relational subword complexity function such that it would imply a Morse and Hedlund-like theorem for relationally periodic words. We consider strong and weak relational periods and two candidates for subword complexity functions.
Instant Revocation
Authors: Jon A. Solworth
Venue: Proceedings of the 5th European PKI workshop on Public Key Infrastructure: Theory and Practice (2008)

PKI has a history of very poor support for revocation. It is both too expensive and too coarse grained, so that private keys which are compromised or otherwise become invalid remain in use long after they should have been revoked. This paper considers Instant Revocation, or revocations which take place within a second or two.A new revocation scheme, Certificate Push Revocation (CPR) is described which can support instant revocation. CPR can be hundreds to thousands of times more Internet-bandwidth efficient than traditional and widely deployed schemes. It also achieves significant improvements in cryptographic overheads. Its costs are essentially independent of the number of queries, encouraging widespread use of PKI authentication.Although explored in the context of instant revocation, CPR is even more efficient--both in relative and absolute terms--when used with coarser grain (non-instant) revocations.
A Probabilistic Observation Model for Stereo Vision Systems: Application to Particle Filter-Based Mapping and Localization
Authors: Francisco Angel Moreno, Jose Luis Blanco, Javier Gonzalez
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

In this paper we propose a probabilistic observation model for stereo vision systems which avoids explicit data association between observations and the map by marginalizing the observation likelihood over all the possible associations. We define observations as sets of landmarks composed of their 3D locations, assumed to be normally distributed, and their SIFT descriptors. Our model has been integrated into a particle filter to test its performance in map building and global localization, as illustrated by experiments with a real robot.
Protocol-based classification for intrusion detection
Authors: Kun-Ming Yu, Ming-Feng Wu, Wai-Tak Wong
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

A lightweight network intrusion detection system is more efficient and effective for real world requirements. Higher performance may result if insignificant and/or useless features are eliminated. Logistic Regression is one feature selection method. In this study, protocol type and Logistic Regression were used to pick up the feature sets which can get nearly the same performance as the full feature using a Support Vector Machine. Evaluation was done over a benchmark dataset used KDD CUP'99. In terms of time efficiency, the proposed method performs more than seven times better than other feature selection methods.
Learning-Based Thermal Rendering in Telepresence
Authors: Mohamed Guiatni, Abdelaziz Benallegue, Abderrahmane Kheddar
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

We propose a new method for thermal rendering in telepresence systems which allows an operator to feel most transparently the thermal behavior of the remote object. It is based on learning and thermal heat flux generation. Two databases are constructed from real measurements recorded during direct contact between operator's finger and different materials. One database is used in order to identify the material in the slave side, the other database is used to generate desired heat flux for the thermal display loop. The identification bloc is based on Principal Component Analysis and Neural Network. Experimental results validating the proposed method are discussed.
Measuring the Related Properties of Linearity and Elongation of Point Sets
Authors: Milos Stojmenovic, Amiya Nayak
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

The concept of elongation is generally well understood. However, there is no clear, precise, mathematical definition of elongation in any dictionary we could find. We propose that the definition of elongation should overlap with the definition of linearity since we will show that these two measures produce results that are highly correlated when applied to different types of 2D shapes. Our experiments consist of testing known methods of linearity and elongation on sets of closed shapes contours, shapes whose areas are filled, and shapes with open contours. We tested each algorithm on 25 different shapes in each category. It was found that the Average Orientations linearity measure from [10] best correlates to the elongation measures found in literature. It has a correlation value of above 0.9 with measures of elongation for open and closed curves. Also, we have discovered that the standard measure of elongation, applied to its intended area based shapes, gives almost identical results when it is applied to just the boundary pixels of the same area based shapes. They are over .98 correlated. This leads to a new linearity/elongation measure which is fast, applicable to both open and closed shapes, is given by a closed formula, and highly agrees with existing measures.
Implementation of LDPC Decoder in DVB-S2 Using Min-Sum Algorithm
Authors: Haeseong Jeong, Jong Tae Kim
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

A new market on digital broadcasting is opening because of the adoption of digital video broadcasting second generation as a digital broadcasting standard in Europe. The DVB-S2 uses a low density parity check, so DVB-S2 has much fast communication speed than conventional DVB which uses reed-solomon code and convolutional code for error correction. In this paper, we valuate performance and implement LDPC decoder of FEC which is important sub-system of DVB-S2 using VHDL. The simulated and implemented LDPC decoder is based on the Min-Sum algorithm.
Complexity of Verifying Game Equilibria
Authors: Emmanuel M. Tadjouddine
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

We consider the problem of verifying game equilibria in multi-agent systems. We first identify a certain class of games where Nash or Bayesian Nash equilibria can be verified in polynomial time. Second, we show that verifying a dominant strategy equilibrium is NP-complete even for normal form games. Eventually, we consider general games and discuss the complexity of equilibrium verification.