ppxscal/embedding_network huggingface dataset:
Generalized Extreme Value for Smooth Component Analysis in Prediction Improvement
Authors: Ryszard Szupiluk, Piotr Wojewnik, Tomasz Ząbkowski
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

In this paper we propose a new preprocessing method for smooth component analysis (SmCA). The smoothness measure used in SmCA depends on the signal extreme values directly. We propose the min/max transformation based on the extreme value distribution providing the more realistic and useful signal characteristic in terms of the smoothness. The full methodology is applied as an ensemble method for the energy load prediction improvement.
Fast Support Vector Data Description Using K-Means Clustering
Authors: Pyo Jae Kim, Hyung Jin Chang, Dong Sung Song, Jin Young Choi
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDD method using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed sub-problems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. The proposed method has a similar result to the original SVDD and reduces computational cost. Through experiments, we show efficiency of our method.
Readable Formal Proofs
Authors: John C. Reynolds
Venue: Proceedings of the 2nd international conference on Verified Software: Theories, Tools, Experiments (2008)

The need to integrate the processes of programming and program verification requires notations for formal proofs that are easily readable. We discuss this problem in the context of Hoare logic and separation logic.It has long been the custom to describe formal proofs in these logics informally by means of "annotated specifications" or "proof outlines". For simple programs, these annotated specifications are essentially similar to the annotated flow charts introduced by Floyd and Naur. For more elaborate programs, a richer notation has evolved for dealing with procedure calls and various structural rules, such as the frame axiom, as well as various rules for concurrency.Our goal is to devise a formalism for insuring that annotated specifications actually determine valid formal proofs (modulo the correctness of verification conditions), while providing as much flexibility as possible. For this purpose, we give inference rules for "annotation definitions" that assert that an annotated specification determines a particular Hoare triple. We consider verification algorithms in a wide sense. The outcome of a verification algorithm can be a definite (yes or no) answer, a "don't know" answer, or a conditional answer or no answer at all (divergence). We obtain these kinds of verification algorithms if we apply the existing technology of abstraction to least-fixpoint checking, i.e., checking whether the least fixpoint of a given operator in a given lattice is smaller than a given bound. The formulation of the verification algorithm as least-fixpoint checking is classical for the class of correctness properties that are reducible to non-reachability (validity of assertions, partial correctness, safety properties). We need to investigate the approach also for the class of correctness properties that are reducible to termination (validity of intermittent assertions, total correctness, liveness properties), for all classes of programs including procedural (recursive) programs and concurrent programs.
Global Bifurcation Analysis of a Pyramidal Cell Model of the Primary Visual Cortex: Towards a Construction of Physiologically Plausible Model
Authors: Tatsuya Ishiki, Satoshi Tanaka, Makoto Osanai, Shinji Doi, Sadatoshi Kumagai, Tetsuya Yagi
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Many mathematical models of different neurons have been proposed so far, however, the way of modeling Ca2 + regulation mechanisms has not been established yet. Therefore, we try to construct a physiologically plausible model which contains many regulating systems of the intracellular Ca2 + , such as Ca2 + buffering, Na + /Ca2 + exchanger and Ca2 + pump current. In this paper, we seek the plausible values of parameters by analyzing the global bifurcation structure of our temporary model.
Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization
Authors: Pradyumn Kumar Shukla
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.
A Multiagent Model for Supporting Tourism Policy-Making by Market Simulations
Authors: Arnaldo Cecchini, Giuseppe A. Trunfio
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Sustainable tourism development at a destination requires an understanding of the many interrelated factors that influence risks and returns. For this reason, operators in tourism management and public policy makers are increasingly interested in effective tools for exploring the consequences of their decisions. With this purpose, this paper describes a multiagent system which enables evaluation of the impact of some possible interventions and policies at a destination by computer simulations and provides an insight into the functioning of the market.
Topic 6 Grid and Cluster Computing
Authors: Rosa M. Badia, Christian Pérez, Artur Andrzejak, Alvaro Arenas
Venue: Proceedings of the 13th European international conference on Parallel Processing (2007)

With some years of research and development Grid computing is starting to be a mature subject with relevant methodologies, software and tools available both for the academia and industry. Grid computing represents the culmination of truly general distributed computing across various resources in a ubiquitous, open-ended infrastructure to support a wide range of different application areas. However, there is still many areas in which further research is required to achieve a user-friendly, efficient, secure and reliable grid.
Improving Robot Self-localization Using Landmarks' Poses Tracking and Odometry Error Estimation
Authors: Pablo Guerrero, Javier Ruiz-Del-Solar
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

In this article the classical self-localization approach is improved by estimating, independently from the robot's pose, the robot's odometric error and the landmarks' poses. This allows using, in addition to fixed landmarks, dynamic landmarks such as temporally local objects (mobile objects) and spatially local objects (view-dependent objects or textures), for estimating the odometric error, and therefore improving the robot's localization. Moreover, the estimation or tracking of the fixed-landmarks' poses allows the robot to accomplish successfully certain tasks, even when having high uncertainty in its localization estimation (e.g. determining the goal position in a soccer environment without directly seeing the goal and with high localization uncertainty). Furthermore, the estimation of the fixed-landmarks' pose allows having global measures of the robot's localization accuracy, by comparing the real map, given by the real (a priori known) position of the fixed-landmarks, with the estimated map, given by the estimated position of these landmarks. Based on this new approach we propose an improved self-localization system for AIBO robots playing in a RoboCup soccer environment, where the odometric error estimation is implemented using Particle Filters, and the robot's and landmarks' poses are estimated using Extended Kalman Filters. Preliminary results of the system's operation are presented.
Non-flat Function Estimation Using Orthogonal Least Squares Regression with Multi-scale Wavelet Kernel
Authors: Meng Zhang, Lihua Fu, Tingting He, Gaofeng Wang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Estimating the non-flat function which comprises both the steep variations and the smooth variations is a hard problem. The existing kernel methods with a single common variance for all the regressors can not achieve satisfying results. In this paper, a novel multi-scale model is constructed to tackle the problem by orthogonal least squares regression (OLSR) with wavelet kernel. The scheme tunes the dilation and translation of each wavelet kernel regressor by incrementally minimizing the training mean square error using a guided random search algorithm. In order to prevent the possible over-fitting, a practical method to select termination threshold is used. The experimental results show that, for non-flat function estimation problem, OLSR outperforms traditional methods in terms of precision and sparseness. And OLSR with wavelet kernel has a faster convergence rate as compared to that with conventional Gaussian kernel.
Haptic Interaction with Virtual Avatars
Authors: François Keith, Paul Evrard, Jean-Rémy Chardonnet, Sylvain Miossec, Abderrahmane Kheddar
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In this paper we present an interactive dynamic simulator for virtual avatars. It allows creation and manipulation of objects in a collaborative way by virtual avatars or between virtual avatars and users. The users interact with the simulation environment using a haptic probe which provides force feedback. This dynamic simulator uses fast dynamics computation and constraint-based methods with friction. It is part of a general framework that is being devised for studies of collaborative scenarios with haptic feedback.
Application of Data Mining in the Financial Data Forecasting
Authors: Jie Wang, Hong Wang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

A method of processing financial data based on wavelet transformation is presented. The data of the financial is essentially an unfixed time sequence. Based on the wavelet transform, the series obtained after decomposition contains information. Basically, the wavelet decomposition uses a pair of filters to decompose iteratively the original time series. It results in a hierarchy of new time series that are easier to model and predict. Regarded as a signal, the time sequence is decomposed into different frequency channels (as a filtering step) .These filters must satisfy some constraints such as causality, information lossless, etc. And reconstruction is used to analyze and forecast the time sequence. Examples show that the new method is more effective than the traditional AR model forecast in some aspects.
Language Decompositions, Primality, and Trajectory-Based Operations
Authors: Kai Salomaa
Venue: Proceedings of the 13th international conference on Implementation and Applications of Automata (2008)

We consider the decomposability of languages and the notion of primality with respect to catenation, as well as, more general operations. We survey recent results and discuss open problems.
Low-Cost Authentication Protocol of the RFID System Using Partial ID
Authors: Yong-Zhen Li, Yoon-Su Jeong, Ning Sun, Sang-Ho Lee
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Previous RFID techniques cause serious privacy infringements such as excessive information exposure and user's location information tracking due to the wireless characteristics and the limitation of RFID systems. Especially the information security problem of read only tag has been solved by physical method. In this paper, we propose a low-cost mutual authentication protocol which can be adapted to read-only tag using XOR computation and Partial ID concept. The proposed protocol is secure against reply attacking, eavesdropping, spoofing attacking and location tracking, therefore it can be used to support secure and low cost RFID system.
Adaptive Information Dissemination: An Extended Wireless Data Broadcasting Scheme with Loan-Based Feedback Control
Authors: Chih-Lin Hu Ming-Syan Chen
Venue: IEEE Transactions on Mobile Computing (2003)

The dissemination of numerous information broadcast services gives rise to the scalability issue in wireless networks. Previous researchers have shown that the push-based data broadcast mechanism is efficient in reducing message traffic. However, most research efforts are dedicated to the dissemination of static information contents. In practice, information broadcast services can produce and deliver dynamic information contents. To efficiently convey the dynamic data, we devise, in this paper, an adaptive information dissemination mechanism by exploiting the functionality of data broadcasting, to support the dissemination of static and dynamic information services simultaneously. In our design, both static and dynamic information services are subsumed as service groups, i.e., the building blocks with the uniform representation of structure and group popularity and, thus, the conventional scenario becomes a special case of our framework. Furthermore, in order to tolerate the broadcast traffic dynamics, we design an online loan-based slot allocation and feedback control technique to deal with the adaptation of the service group classification, bandwidth allocation, and broadcast schedule so as to avoid performance degradation. It is shown by the experimental study that the proposed adaptive information dissemination mechanism associated with the online loan-based feedback control is able to achieve a substantial reduction of message traffic for dynamic information dissemination in wireless networks.
Characterizing application sensitivity to OS interference using kernel-level noise injection
Authors: Kurt B. Ferreira, Patrick Bridges, Ron Brightwell
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

Operating system noise has been shown to be a key limiter of application scalability in high-end systems. While several studies have attempted to quantify the sources and effects of system interference using user-level mechanisms, there are few published studies on the effect of different kinds of kernel-generated noise on application performance at scale. In this paper, we examine the sensitivity of real-world, large-scale applications to a range of OS noise patterns using a kernel-based noise injection mechanism implemented in the Catamount lightweight kernel. Our results demonstrate the importance of how noise is generated, in terms of frequency and duration, and how this impact changes with application scale. For example, our results show that 2.5% net processor noise at 10,000 nodes can have no impact or can result in over a factor of 20 slowdown for the same application, depending solely on how the noise is generated. We also discuss how the characteristics of the applications we studied, for example computation/communication ratios, collective communication sizes, and other characteristics, related to their tendency to amplify or absorb noise. Finally, we discuss the implications of our findings on the design of new operating systems, middleware, and other system services for high-end parallel systems.
Distributed Connected Dominating Set Construction in Geometric k-Disk Graphs
Authors: Kai Xing, Wei Cheng, E. K. Park, Shmuel Rotenstreich
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

In this paper, we study the problem of minimum connected dominating set in geometric $k$-disk graphs. This research is motivated by the problem of virtual backbone construction in wireless ad hoc and sensor networks, where the coverage area of nodes are disks with different radii. We derive the size relationship of any maximal independent set and the minimum connected dominating set in geometric $k$-disk graphs, and apply it to analyze the performances of two distributed connected dominating set algorithms we propose in this paper. These algorithms have a bounded performance ratio and low communication overhead, and therefore have the potential to be applied in real ad hoc and sensor networks.
A Steganographic Algorithm for Hiding Data in PDF Files Based on Equivalent Transformation
Authors: Xingtong Liu, Quan Zhang, Chaojing Tang, Jingjing Zhao, Jian Liu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

To amend the defects of low perceptual transparency, low capacity, low security of the present information hiding technology in PDF files, we present a novel steganographic algorithm which is better in perceptual transparency property, capacity and security. Attributive to the insensitiveness of PDF files to the seriation of entries, the algorithm helps to realize the information hiding in high degree of perceptual transparency and in large capacity without writing new data in the original file. And as the initial value of the chaotic map, the key is generated by applying the secure hash algorithm, to keep the system meet Kerckhoffs rules and enable integrity service. In addition, this algorithm is equipped strong anti-statistical attack ability.
The norm inequalities for the weighted Cesaro mean operators
Authors: Jichang Kuang
Venue: Computers Mathematics with Applications (2008)

This paper gives some necessary and sufficient conditions for the weighted Cesaro mean operators to be bounded on Herz spaces. The corresponding new operator norm inequalities are obtained.
Multispectral and Panchromatic Image Fusion Algorithm Based on Local Spatial Recovery Model
Authors: Yifan Zhang, Mingyi He
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In this paper, Local Spatial Recovery Model (LSRM) between images of the same scene with different spatial resolutions is constructed and utilized in multispectral and panchromatic image fusion to propose a new algorithm (LSRM-MPF algorithm). The newly proposed fusion algorithm employs HIS Transform to separate spectral and spatial information, Redundant Wavelet Transform to separate low- and high-frequency spatial information of source images. Experimental results illustrate that LSRM-MPF algorithm is capable of preserving both spectral and spatial features of source images effectively with adequate calculation time. Soft/hard threshold can also be set to control properties of the fused image according to the practical requirements or application fields.
Tool Support for Representing Task Models, Dialog Models and User-Interface Specifications
Authors: D. Reichart, A. Dittmar, P. Forbrig, M. Wurdel
Venue: Interactive Systems. Design, Specification, and Verification: 15th International Workshop, DSV-IS 2008 Kingston, Canada, July 16-18, 2008 Revised Papers (2008)

This paper focuses on the visualization of task models. Models in general can be presented in different ways. We focus on tool support for different editors working on the same instance of a task model.
Towards a Model Driven Process for Multi-Agent System
Authors: Tarek Jarraya, Zahia Guessoum
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

We propose a new multi-agent development method, named MDAD (Model Driven Agent Development). It is based on the MDA (Model Driven Architecture) paradigm. The aim of MDAD method is to reduce the cost of building MAS applications by starting from abstract specification of system thanks to MAS meta-models, and producing the final system by means of transformations of this specification into computational entities. We present in this paper the application of MDAD to the INAF framework. First we give an overview of MDA approach and its application to MAS. Thus, several abstraction levels are determined and a set of meta-models is introduced. Then, we give the transformation rules used to produce INAF compliant models. MDAD method is illustrated with the timetable management benchmark.
Design, Implementation and Deployment of State Machines Using a Generative Approach
Authors: Graham N. Kirby, Alan Dearle, Stuart J. Norcross
Venue: Architecting Dependable Systems V (2008)

We describe an approach to designing and implementing a distributed system as a family of related finite state machines, generated from a single abstract model. Various artefacts are generated from each state machine, including diagrams, source-level protocol implementations and documentation. The state machine family formalises the interactions between the components of the distributed system, allowing increased confidence in correctness. Our methodology facilitates the application of state machines to problems for which they would not otherwise be suitable.We illustrate the technique with the example of a Byzantine-fault-tolerant commit protocol used in a distributed storage system, showing how an abstract model can be defined in terms of an abstract state space and various categories of state transitions. We describe how such an abstract model can be deployed in a concrete system, and propose a general methodology for developing systems in this style.
Minimizing earliness-tardiness penalties for common due date single-machine scheduling problems by a recovering beam search algorithm
Authors: Kuo-Ching Ying
Venue: Computers and Industrial Engineering (2008)

This study considers the NP-hard problem of scheduling jobs on a single-machine against common due dates with respect to earliness and tardiness penalties. An effective and efficient recovering beam search (RBS) algorithm is proposed to solve this problem. To validate and verify the developed algorithm, computational experiments are conducted on a well-known benchmark problem set, and the results are compared with nine meta-heuristics from the relevant literature. The experimental comparison results reveal that the proposed RBS algorithm is a state-of-the-art approach to the single-machine scheduling problem with earliness and tardiness penalties. In terms of both solution quality and computational effort, this study successfully develops a near-optimal approach that will hopefully encourage practitioners to apply it to real-world problems.
Application of Neural Network on Rolling Force Self-learning for Tandem Cold Rolling Mills
Authors: Jingming Yang, Haijun Che, Fuping Dou, Shuhui Liu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

All the factors that influence the rolling force are analyzed, and the neural network model which uses the back propagation (BP) learning algorithm for the calculation of rolling force is created. The initial network's weights corresponding to the input material grades are taught by the traditional theoretical model, and saved in the database. In order to increase the prediction accuracy of rolling force, we use the measured rolling force data to teach the neural network after several coils of the same input material are rolled down.