ppxscal/embedding_network huggingface dataset:
The Approaches for Oasis Desert Vegetation Information Abstraction Based on Medium-Resolution Lansat TM Image: A Case Study in Desert wadi Hadramut Yemen
Authors: Ayoub Ahmed Almhab, Ibrahim Busu
Venue: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) (2008)

This paper present two issues namely; first is oasis desert brightness inversion correction, and secondly, the classifying method of oasis desert vegetation through remote sensing image data. Oasis desert brightness inversion is known reduce the classification accuracy in medium-resolution images. In this study, the radiation correction and the brightness inversion adjustment models was analysis. The model’s parameters were obtained from the image pixel values. The result of brightness inversion correction shows that the model can correct oasis desert brightness inversion. After brightness inversion correction, the vegetation’s pixel value in brightness inversion area is similar with the pixel value of vegetation in other area. Brightness inversion correction increases classification accuracy. In the second part of this study, three methods are studied to derive oasis desert vegetations information, including vegetation index method, back propagation neural network method, and texture method. Three methods’ classification accuracies are calculated and appraised. And a conclusion is drawn, which is the texture classification method is a good classification method. The accuracy of texture classification method can reach up to 82.31%.
Evaluating the Correctness and Effectiveness of a Middleware QoS Configuration Process in Distributed Real-Time and Embedded Systems
Authors: Amogh Kavimandan, Anantha Narayanan, Aniruddha Gokhale, Gabor Karsai
Venue: Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing (2008)

Recent advances in software processes and artifacts for automating middleware configurations in distributed real-time and embedded (DRE) systems are starting to address the complexities faced by system developers in dealing with the flexibility and configurability provided by contemporary middleware. Despite the benefits of these new processes, there remain significant challenges in verifying their correctness, and validating their effectiveness in meeting the end-to-end quality of service (QoS) requirements of DRE systems. This paper addresses this problem by describing how model-checking and structural correspondence can be used to verify the correctness of a middleware QoS configuration process that uses model-based graph transformations at its core. Next, it provides empirical proof to validate the effectiveness of our technique to meet the end-to-end QoS requirements in the context of a representative DRE system.
Greedy Tikhonov regularization for large linear ill-posed problems
Authors: L. Reichel, H. Sadok, A. Shyshkov
Venue: International Journal of Computer Mathematics (2007)

Several numerical methods for the solution of large linear ill-posed problems combine Tikhonov regularization with an iterative method based on partial Lanczos bidiagonalization of the operator. This paper discusses the determination of the regularization parameter and the dimension of the Krylov subspace for this kind of method. A method that requires a Krylov subspace of minimal dimension is referred to as greedy.
Log Depth Circuits For Division And Related Problems
Authors: P. W. Beame
Venue: Proceedings of the 25th Annual Symposium onFoundations of Computer Science, 1984 (1984)

We present optimal depth Boolean circuits (depth O(log n)) for integer division, powering, and multiple products. We also show that these three problems are of equivalent uniform depth and space complexity. In addition, we describe an algorithm for testing divisibility that is optimal for both depth and space.
Adaptive Stochastic Collocation Method (ASCM) for Parameterized Statistical Timing Analysis with Quadratic Delay Model
Authors: Yi Wang, Xuan Zeng, Jun Tao, Hengliang Zhu, Xu Luo, Changhao Yan, Wei Cai
Venue: Proceedings of the 9th international symposium on Quality Electronic Design (2008)

In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also show great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.
A direct simulation method for flows with suspended paramagnetic particles
Authors: Tae Gon Kang, Martien A. Hulsen, Jaap M. J. den Toonder, Patrick D. Anderson, Han E. H. Meijer
Venue: Journal of Computational Physics (2008)

A direct numerical simulation method based on the Maxwell stress tensor and a fictitious domain method has been developed to solve flows with suspended paramagnetic particles. The numerical scheme enables us to take into account both hydrodynamic and magnetic interactions between particles in a fully coupled manner. Particles are assumed to be non-Brownian with negligible inertia. Rigid body motions of particles in 2D are described by a rigid-ring description implemented by Lagrange multipliers. The magnetic force, acting on the particles due to magnetic fields, is represented by the divergence of the Maxwell stress tensor, which acts as a body force added to the momentum balance equation. Focusing on two-dimensional problems, we solve a single-particle problem for verification. With the magnetic force working on the particle, the proper number of collocation points is found to be two points per element. The convergence with mesh refinement is verified by comparing results from regular mesh problems with those from a boundary-fitted mesh problem as references. We apply the developed method to two application problems: two-particle interaction in a uniform magnetic field and the motion of a magnetic chain in a rotating field, demonstrating the capability of the method to tackle general problems. In the motion of a magnetic chain, especially, the deformation pattern at break-up is similar to the experimentally observed one. The present formulation can be extended to three-dimensional and viscoelastic flow problems.
A New Congestion Control Algorithm Based on RBFNN
Authors: Shan Weifeng, Meng Baohong, Zhu Wei
Venue: Proceedings of the Fifth International Conference on Information Technology: New Generations (2008)

This paper presents a new sequential learning algorithm for radial basis function neural network (RBFNN) based on local projection named Local Projection Network (LPN). Earlier studies have shown that RBFNN is well suited for online adaptive control of nonlinear time varying systems as it adjust its size by adding and pruning the hidden neurons based on the input data. Simulation results indicate that RBF with the new algorithm controller performs better in reducing the congestion episodes and maintaining the desirable QoS.
An extension of fault-prone filtering using precise training and a dynamic threshold
Authors: Hideaki Hata, Osamu Mizuno, Tohru Kikuno
Venue: Proceedings of the 2008 international working conference on Mining software repositories (2008)

Fault-prone module detection in source code is important for assurance of software quality. Most previous fault-prone detection approaches have been based on software metrics. Such approaches, however, have difficulties in collecting the metrics and in constructing mathematical models based on the metrics. To mitigate such difficulties, we have proposed a novel approach for detecting fault-prone modules using a spam-filtering technique, named Fault-Prone Filtering. In our approach, fault-prone modules are detected in such a way that the source code modules are considered as text files and are applied to the spam filter directly. In practice, we use the training only errors procedure and apply this procedure to fault-prone. Since no pre-training is required, this procedure can be applied to an actual development field immediately. This paper describes an extension of the training only errors procedures. We introduce a precise unit of training, "modified lines of code," instead of methods. In addition, we introduce the dynamic threshold for classification. The result of the experiment shows that our extension leads to twice the precision with about the same recall, and improves 15% on the best F1 measurement.
Gateway Placement with QoS Constraints in Wireless Mesh Networks
Authors: Y. Drabu, H. Peyravi
Venue: Proceedings of the Seventh International Conference on Networking (2008)

Several factors greatly influence the quality of service (QoS) in multihop wireless mesh networks (WMNs). These include the transmission range, number of gateways, number of nodes served by each gateway, gateway location, relay load and access fairness. While finding an optimal solution to simultaneously satisfy the above constraints is known to be NP-hard, near optimal solutions can be found within the feasibility region in polynomial time using heuristic algorithms. In this paper, first we present a near optimal heuristics algorithm for gateway placement, and later we compare its performance with some previously known sub-optimal solutions.
Group Decision Making Method with Incomplete Linguistic 2-tuple Judgement Matrix Based on Additive Consistency
Authors: Xixiang Zhang, Jianxun Liu, Jing Lei, Bao'an Yang
Venue: Proceedings of the First International Workshop on Knowledge Discovery and Data Mining (2008)

This paper studies the additive consistency of linguistic 2-tuple judgement matrix and use the additive transitivity to estimate the missing values in an incomplete linguistic 2-tuple judgement matrix. Then, it puts forward a group decision making method with incomplete linguistic 2-tuple judgement matrix and gives an example to illustrate it.
Transfer of samples in batch reinforcement learning
Authors: Alessandro Lazaric, Marcello Restelli, Andrea Bonarini
Venue: Proceedings of the 25th international conference on Machine learning (2008)

The main objective of transfer in reinforcement learning is to reduce the complexity of learning the solution of a target task by effectively reusing the knowledge retained from solving a set of source tasks. In this paper, we introduce a novel algorithm that transfers samples (i.e., tuples &lang;s, a, s', r&rang;) from source to target tasks. Under the assumption that tasks have similar transition models and reward functions, we propose a method to select samples from the source tasks that are mostly similar to the target task, and, then, to use them as input for batch reinforcement-learning algorithms. As a result, the number of samples an agent needs to collect from the target task to learn its solution is reduced. We empirically show that, following the proposed approach, the transfer of samples is effective in reducing the learning complexity, even when some source tasks are significantly different from the target task.
Animation of Orthogonal Texture Patterns for Vector Field Visualization
Authors: Sven Bachthaler, Daniel Weiskopf
Venue: IEEE Transactions on Visualization and Computer Graphics (2008)

This paper introduces orthogonal vector field visualization on 2D manifolds: a representation by lines that are perpendicular to the input vector field. Line patterns are generated by line integral convolution (LIC). This visualization is combined with animation based on motion along the vector field. This decoupling of the line direction from the direction of animation allows us to choose the spatial frequencies along the direction of motion independently from the length scales along the LIC line patterns. Vision research indicates that local motion detectors are tuned to certain spatial frequencies of textures, and the above decoupling enables us to generate spatial frequencies optimized for motion perception. Furthermore, we introduce a combined visualization that employs orthogonal LIC patterns together with conventional, tangential streamline LIC patterns in order to benefit from the advantages of these two visualization approaches. In addition, a filtering process is described to achieve a consistent and temporally coherent animation of orthogonal vector field visualization. Different filter kernels and filter methods are compared and discussed in terms of visualization quality and speed. We present respective visualization algorithms for 2D planar vector fields and tangential vector fields on curved surfaces, and demonstrate that those algorithms lend themselves to efficient and interactive GPU implementations.
Inter-Image Statistics for 3D Environment Modeling
Authors: Luz A. Torres-Méndez, Gregory Dudek
Venue: International Journal of Computer Vision (2008)

In this article we present a method for automatically recovering complete and dense depth maps of an indoor environment by fusing incomplete data for the 3D environment modeling problem. The geometry of indoor environments is usually extracted by acquiring a huge amount of range data and registering it. By acquiring a small set of intensity images and a very limited amount of range data, the acquisition process is considerably simplified, saving time and energy consumption. In our method, the intensity and partial range data are registered first by using an image-based registration algorithm. Then, the missing geometric structures are inferred using a statistical learning method that integrates and analyzes the statistical relationships between the visual data and the available depth on terms of small patches. Experiments on real-world data on a variety of sampling strategies demonstrate the feasibility of our method.
Omnivore: Integration of Grid Meta-Scheduling and Peer-to-Peer Technologies
Authors: Michael Heidt, Tim Dörnemann, Kay Dörnemann, Bernd Freisleben
Venue: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (2008)

Dedicated servers remain to be a common constituent of Grid job scheduling architectures, forcing site administrators to make compromises between administrative expenses and system reliability. Apart from requiring administrative attention, dedicated servers create single points of failure and should not be subjected to network churn. This paper presents the design and implementation of Omnivore, a fully decentralized job scheduling system, built on a peer-to-peer based meta-scheduler. Omnivore is able to cope both with node failures and network churn, eliminating the need for central administration and continuous resource availability. It is integrated into the Grid landscape (especially the Globus Toolkit 4) by means of the GridWay meta-scheduler toprovide scalable distributed scheduling, replicated storage and system monitoring capabilities. Results obtained from an experimental evaluation of our implementation show that Omnivore is both scalable and resilient in the presence of node failures and network churn.
Tissue Cell Boundaries Detection based on Curvelet-based Snake Model in Electrorotation Bio-chip Control System
Authors: Qihua Yang, Qiang Wang
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01 (2008)

During cell motility tracking process, to suppress background interference and separate the clustering cells contour, a novel cell boundary feature extraction algorithm based on snake model and curvelet transform(CT) is proposed. The CT has higher time frequency resolution, high degree of directionality and anisotropy. Microscope cell image is denoised by discrete CT to improve SNR. Then curvelet-based Snake model in multiscale space is applied to identify cell contour, which obtains cell boundaries under the effect of both the original image force and the modified nonlinear distance image force. It is effective for electrorotation cell contour extraction corrupted by noise, with weak edges. Experimental results show good performances of the proposed method to extract the shape of edges and quantification of cell motility.
ZAP: a knowledge-based FEA modeling method for highly coupled variable topology multi-body problems
Authors: Sai Zeng, Russell S. Peak, Angran Xiao, Suresh Sitaraman
Venue: Engineering with Computers (2008)

Some of the most significant challenges in automated CAD&#x2013;FEA integration are information and model transformations between CAD and FEA tools. These are especially labor-intensive and time-consuming in a newly characterized class of problems termed highly coupled variable topology multi-body (HCVTMB) problems. This paper addresses these challenges with a knowledge-based FEA modeling method called ZAP that consists of three stepping-stone information models and the mapping processes between these models. The information and knowledge of a typical FEA modeling process are explicitly captured in semantically rich information models to achieve benefits including knowledge sharing, system extension, and model modification. ZAP mapping processes automatically transform abstract analytical concepts into tool-specific commands and functions that accomplish HCVTMB model generation and solution management. This method enhances flexibility and reusability in FEA modeling and enables CAD&#x2013;FEA integration at the knowledge level. To demonstrate the efficacy of ZAP, we overview a sample HCVTMB problem&#x2014;an electronic chip package plastic ball grid array (PBGA) thermal analysis case study. Experience indicates that ZAP increases knowledge capture and decreases modeling time from days/hours to hours/minutes compared to conventional methods, thus providing a key enabler toward design optimization.
A new algorithm for estimating chlorophyll-a concentration from multi-spectral satellite data in case II waters: a simulation based on a controlled laboratory experiment
Authors: Y. Oyama, B. Matsushita, T. Fukushima, T. Nagai, A. Imai
Venue: International Journal of Remote Sensing (2007)

This paper presents the spectral decomposition algorithm (SDA), a new algorithm for estimating chlorophyll-a concentration in case II waters using multi-spectral satellite data, which is based on a simulation in a controlled laboratory experiment. The SDA is composed of two key steps. The first of these is to consider the mixed reflectance spectrum of a given pixel as a linear combination of three basic components: clear water, non phytoplankton suspended sediments (NPSS), and phytoplankton. The second step is to use a decomposition coefficient (Cp ) obtained from the first step as an independent variable in the chlorophyll-a estimation model, instead of the single band reflectance, band ratio or arithmetic calculation of bands used in conventional methods. The simulated results for the Landsat TM data showed that bands 1, 3 and 4 are useful wavelengths for estimating chlorophyll-a concentrations. In the case of a water body with chlorophyll-a concentrations ranging from 0 to 105 µg l-1 and NPSS concentrations ranging from 0 to 100 mg l-1, the RMSE of the estimation model of chlorophyll-a concentrations based on the SDA was 13.7 µg l-1, reduced by nearly half of that for conventional methods (the RMSE was 25.6 µg l-1 for the band ratio, and 25.5 µg l-1 for the arithmetic calculation of bands). The results of a two-factor ANOVA (without replication) highlight that the decomposition coefficient Cp contains information from phytoplankton far more than from NPSS. However, Cp values were still changed with the addition of NPSS, due mainly to the influence of the interaction of optical properties among phytoplankton, NPSS and water, which occurred in both horizontal and vertical directions in the water bodies. Considering the basic components as a nonlinear combination in a water area may reduce the effect of NPSS on Cp values from that of their linear combination. In addition, the influence of coloured dissolved organic matter (CDOM), which is generally considered as one of the optically active substances in case II waters, was ignored according to the practical conditions of our study area, Lake Kasumigaura, Japan in this paper. Users can set the basic components (or endmembers) freely according to the conditions of their own study area when the SDA is used (e.g. considering the CDOM as a basic component in the SDA).
An o(log2 k)-approximation algorithm for the k-vertex connected spanning subgraph problem
Authors: Jittat Fakcharoenphol, Bundit Laekhanukit
Venue: Proceedings of the 40th annual ACM symposium on Theory of computing (2008)

We present an O(log n• log k)-approximation algorithm for the problem of finding k-vertex connected spanning subgraph of minimum cost, where n is the number of vertices in the input graph, and k is the connectivity requirement. Our algorithm works for both directed and undirected graphs. The best known approximation guarantees for these problems are O(ln k• min{√k,n/n-k ln k}) by Kortsarz and Nutov, and O(ln{k}) in the case of undirected graphs where n≥ 6k2 by Cheriyan, Vempala, and Vetta. Our algorithm is the first that has a polylogarithmic guarantee for all values of k. Combining our algorithm with the algorithm of Kortsarz and Nutov in case of small k, e.g., k2 k)-approximation algorithm. As in previous work, we use the Frank-Tardos algorithm for finding k-outconnected subgraphs as a subroutine. However, with a structural lemmas that we proved, we are able to show that we need only partial solutions returned by the Frank-Tardos algorithm; thus, we can avoid paying the whole cost of the optimal solution every time the algorithm is applied.
Poogle and the unknown-answer assignment: open-ended, sharable cs1 assignments
Authors: Christopher C. D. Head, Steven A. Wolfman
Venue: Proceedings of the 39th SIGCSE technical symposium on Computer science education (2008)

Most CS1 assignments are "known answer" assignments, requiring students to implement already-solved problems with no user but their grader and themselves. In this paper, we present Poogle: a freely available framework for designing unknown-answer assignments. Poogle assignments are open-ended, graphical, and multi-user. Poogle makes it easy for students to share their creations with their users: fellow students and the general public. We present two freely available CS1 assignments based on Poogle and discuss outcomes from use of one of them in a CS1 course.
Interior-point methods in parallel computation
Authors: A. V. Goldberg
Venue: Proceedings of the 30th Annual Symposium on Foundations of Computer Science (1989)

Interior-point methods for linear programming, developed in the context of sequential computation, are used to obtain a parallel algorithm for the bipartite matching problem. The algorithm runs in O*( square root m) time. The results extend to the weighted bipartite matching problem and to the zero-one minimum-cost flow problem, yielding O*( square root m log C) algorithms. This improves previous bounds on these problems and illustrates the importance of interior-point methods in parallel algorithm design.