ppxscal/embedding_network huggingface dataset:
A Framework for Modeling B2B Applications
Authors: Zakaria Maamar, Philippe Thiran, Nanjangud C. Narendra, Sattanathan Subramanian
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications (2008)

This paper presents a Web services-based framework for modeling Business-to-Business (B2B) applications. This framework consists of three levels namely strategic, application, and resource, with focus here on the first two levels. The strategic level is about the common vision that independent businesses define as part of their decision to join forces. And the application level is about the business processes that get virtually integrated as result of this common vision. To implement the strategic and application levels, this paper leverages from our earlier two projects referred to as Context-Aware Web Services and Coordination Model for Web Services, respectively. Hence in line with these projects we model B2B applications as context-aware Web services. Context-based modeling is used for modeling and tracking interactions among B2B applications, with emphasis on how conflicts among the applications are identified and managed. The application of our framework to a B2B scenario relies on a case study from the purchasing domain. We also present a prototype implementation of our framework on this domain.
Performance Comparison of Three Types of Autoencoder Neural Networks
Authors: Chun Chet Tan, C. Eswaran
Venue: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) (2008)

This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM and the stacked autoencoder with RBM. The performances are compared based on the reconstruction error for face images and using the same values for the parameters such as the number of neurons in the hidden layers, the training method, and the learning rate. The results show that the RBM stacked autoencoder gives better performance in terms of the reconstruction error compared to the other two architectures.
Algorithms for Differential Invariants of Symmetry Groups of Differential Equations
Authors: Jeongoo Cheh, Peter J. Olver, Juha Pohjanpelto
Venue: Foundations of Computational Mathematics (2008)

We develop new computational algorithms, based on the method of equivariant moving frames, for classifying the differential invariants of Lie symmetry pseudo-groups of differential equations and analyzing the structure of the induced differential invariant algebra. The Korteweg-deVries (KdV) and Kadomtsev-Petviashvili (KP) equations serve to illustrate examples. In particular, we deduce the first complete classification of the differential invariants and their syzygies of the KP symmetry pseudo-group.
Discrete second order adjoints in atmospheric chemical transport modeling
Authors: Adrian Sandu, Lin Zhang
Venue: Journal of Computational Physics (2008)

Atmospheric chemical transport models (CTMs) are essential tools for the study of air pollution, for environmental policy decisions, for the interpretation of observational data, and for producing air quality forecasts. Many air quality studies require sensitivity analyses, i.e., the computation of derivatives of the model output with respect to model parameters. The derivatives of a cost functional (defined on the model output) with respect to a large number of model parameters can be calculated efficiently through adjoint sensitivity analysis. While the traditional (first order) adjoint models give the gradient of the cost functional with respect to parameters, second order adjoint models give second derivative information in the form of products between the Hessian of the cost functional and a vector (representing a perturbation in sensitivity analysis, a search direction in optimization, an eigenvector, etc.). In this paper we discuss the mathematical foundations of the discrete second order adjoint sensitivity method and present a complete set of computational tools for performing second order sensitivity studies in three-dimensional atmospheric CTMs. The tools include discrete second order adjoints of Runge-Kutta and of Rosenbrock time stepping methods for stiff equations together with efficient implementation strategies. Numerical examples illustrate the use of these computational tools in important applications like sensitivity analysis, optimization, uncertainty quantification and the calculation of directions of maximal error growth in three-dimensional atmospheric CTMs.
Reinforcement learning in the presence of rare events
Authors: Jordan Frank, Shie Mannor, Doina Precup
Venue: Proceedings of the 25th international conference on Machine learning (2008)

We consider the task of reinforcement learning in an environment in which rare significant events occur independently of the actions selected by the controlling agent. If these events are sampled according to their natural probability of occurring, convergence of conventional reinforcement learning algorithms is likely to be slow, and the learning algorithms may exhibit high variance. In this work, we assume that we have access to a simulator, in which the rare event probabilities can be artificially altered. Then, importance sampling can be used to learn with this simulation data. We introduce algorithms for policy evaluation, using both tabular and function approximation representations of the value function. We prove that in both cases, the reinforcement learning algorithms converge. In the tabular case, we also analyze the bias and variance of our approach compared to TD-learning. We evaluate empirically the performance of the algorithm on random Markov Decision Processes, as well as on a large network planning task.
DataMeadow: a visual canvas for analysis of large-scale multivariate data
Authors: Niklas Elmqvist, John Stasko, Philippas Tsigas
Venue: Information Visualization (2008)

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.
Convergence studies of the time accurate and non-linear frequency domain methods for optimum shape design
Authors: Siva K. Nadarajah
Venue: International Journal of Computational Fluid Dynamics (2007)

This paper presents convergence studies of the time accurate and non-linear frequency domain (NLFD) methods for optimum shape design. The NLFD method provides an attractive alternative for aerodynamic shape optimisation for unsteady flows. It dramatically reduces the number of time steps required to resolve the periodic flow field. In this work, the convergence of the gradients are investigated as the number of time steps are increased to determine the required number of modes that is adequate for shape optimisation for unsteady flows. The formulation of the adjoint equations for both the time accurate and NLFD techniques are presented. The techniques are employed for the redesign of a pitching airfoil to reduce the time-averaged drag coefficient while maintaining the lift coefficient.
Evaluating dead reckoning variations with a multi-player game simulator
Authors: Wladimir Palant, Carsten Griwodz, Pål Halvorsen
Venue: Proceedings of the 2006 international workshop on Network and operating systems support for digital audio and video (2006)

One of the most difficult tasks when creating an online multi-player game is to provide the players with a consistent view of the virtual world despite the network delays. Most current games use prediction algorithms to achieve this, but usually it does not go beyond applying the DIS [2] dead reckoning algorithm proposed in the mid-90s. In this paper we introduce a simulator called GLS that allows us to evaluate different aspects of DIS and its variations. We examine the impact of prediction and clock synchronization on game consistency. We also evaluate the convergence algorithm we introduce here. Furthermore we look into ways for compensating increasing delays to keep the player's view of the game state sufficiently consistent with other players.
A genetic algorithm for joint replenishment based on the exact inventory cost
Authors: Sung-Pil Hong, Yong-Hyuk Kim
Venue: Computers and Operations Research (2009)

Given the order cycles of items in joint replenishment, no closed-form formula or efficient method is known to compute the exact inventory cost. Previous studies avoid the difficulty by restricting the replenishment policy to the cases where the order cycle of each item is a multiple of the cycle of the most frequently ordered item. This simplifies the computation but may entail sub-optimality of a solution. To cope with this, we devise an unbiased estimator of the exact cost which is computable in time polynomial of the problem input size and 1/@e, where @e is a pre-specified relative error of estimation. We then develop a genetic algorithm based on this new cost evaluation, report the experimental results in comparison to the ''RAND'' [Kaspi M, Rosenblatt MJ. An improvement of Silver's algorithm for the joint replenishment problem. IIE Transactions 1983; 15: 264-9] which has been known as a state-of-the-art method for joint replenishment, and discuss their implications.
Fast, memory efficient flow rate estimation using runs
Authors: Fang Hao, Murali Kodialam, T. V. Lakshman, Shantidev Mohanty
Venue: IEEE/ACM Transactions on Networking (TON) (2007)

Per-flow network traffic measurements are needed for effective network traffic management, network performance assessment, and detection of anomalous network events such as incipient denial-of-service (DoS) attacks. Explicit measurement of per-flow traffic statistics is difficult in backbone networks because tracking the possibly hundreds of thousands of flows needs correspondingly large high-speed memories. To reduce the measurement overhead, many previous papers have proposed the use of random sampling and this is also used in commercial routers (Cisco's NetFlow). Our goal is to develop a new scheme that has very low memory requirements and has quick convergence to within a pre-specified accuracy. We achieve this by use of a novel approach based on sampling two-runs to estimate per-flow traffic. (A flow has a two-run when two consecutive samples belong to the same flow). Sampling two-runs automatically biases the samples towards the larger flows thereby making the estimation of these sources more accurate. This biased sampling leads to significantly smaller memory requirement compared to random sampling schemes. The scheme is very simple to implement and performs extremely well.
Proximal Connectedness
Authors: D. S. Bridges, L. S. Vîţă
Venue: Fundamenta Informaticae (2008)

A notion of connectedness is introduced into the theory of apartness spaces, and various fundamental properties of connected sets are established.
Ranking the Best Instances
Authors: Stéphan Clémençon, Nicolas Vayatis
Venue: The Journal of Machine Learning Research (2007)

We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stage-wise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.
Path-a-Way: A Strategy for Network Analysis of Microarray Data
Authors: Dhivya Arasappan, Aurelien Mazurie, J. Alves, Danail Bonchev, Gregory A. Buck
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01 (2008)

Microarray technology allows for high throughput gene expression studies. However, because of the large amounts of data produced, additional work is required to put the data into biological context. An emerging approach to provide this context is pathway and network analysis. The objective of this work was to design and implement such pathway analysis strategies. Our strategy identifies enriched biological pathways when given a list of genes found to be differentially expressed in a microarray experiment. After identification of these pathways, our strategy generates scores to quantify how significant and functional the pathways are. These scores include statistical significance and unique graph theory scores such as pathway port connectedness, adjacency ratio,and completeness. Enzyme-centric scores are also generated to quantify the importance of each enzyme coding gene in the pathway it participates in. Pathways in un-annotated organisms are inferred based on homology and several methods of visualizing the pathways are also provided. This approach can also be extended to proteomic data, or comparative genomic data to assess the relevance of a pathway or network in an organism or group of organisms.
Optimality and improvement of dynamic voltage scaling algorithms for multimedia applications
Authors: Zhen Cao, Brian Foo, Lei He, Mihaela van der Schaar
Venue: Proceedings of the 45th annual Design Automation Conference (2008)

The time-varying workload for multimedia applications poses a great challenge for the efficient performance of dynamic voltage scaling (DVS) algorithms. While many DVS algorithms have been proposed for real-time applications, there does not yet exist a systematic method for evaluating the optimality of such DVS algorithms. In this paper, we propose an offline linear programming (LP) method to determine the minimum energy consumption for processing multimedia tasks under stringent delay deadlines. Based on this lower bound, we evaluate the efficiency of various existing DVS algorithms. Furthermore, we modify the LP formulation to construct an online robust sequential linear programming DVS algorithm for real-time multimedia processing. Simulation results from decoding over a wide range of video sequences shows that on average, our online algorithm consumes less than 1% more energy than the optimal lower bound while dropping only 0.1% of all scheduled decoding jobs, while the existing best algorithm consumes roughly 3% more energy at the same miss rate.
Boosting multi-label hierarchical text categorization
Authors: Andrea Esuli, Tiziano Fagni, Fabrizio Sebastiani
Venue: Information Retrieval (2008)

Hierarchical Text Categorization (HTC) is the task of generating (usually by means of supervised learning algorithms) text classifiers that operate on hierarchically structured classification schemes. Notwithstanding the fact that most large-sized classification schemes for text have a hierarchical structure, so far the attention of text classification researchers has mostly focused on algorithms for "flat" classification, i.e. algorithms that operate on non-hierarchical classification schemes. These algorithms, once applied to a hierarchical classification problem, are not capable of taking advantage of the information inherent in the class hierarchy, and may thus be suboptimal, in terms of efficiency and/or effectiveness. In this paper we propose TreeBoost.MH, a multi-label HTC algorithm consisting of a hierarchical variant of AdaBoost.MH, a very well-known member of the family of "boosting" learning algorithms. TreeBoost.MH embodies several intuitions that had arisen before within HTC: e.g. the intuitions that both feature selection and the selection of negative training examples should be performed "locally", i.e. by paying attention to the topology of the classification scheme. It also embodies the novel intuition that the weight distribution that boosting algorithms update at every boosting round should likewise be updated "locally". All these intuitions are embodied within TreeBoost.MH in an elegant and simple way, i.e. by defining TreeBoost.MH as a recursive algorithm that uses AdaBoost.MH as its base step, and that recurs over the tree structure. We present the results of experimenting TreeBoost.MH on three HTC benchmarks, and discuss analytically its computational cost.
Sketching freeform meshes using graph rotation functions
Authors: Bin Sheng, Enhua Wu, Hanqiu Sun
Venue: The Visual Computer: International Journal of Computer Graphics (2008)

We present a new approach for sketching free form meshes with topology consistency. Firstly, we interpret the given 2D curve to be the projection of the 3D curve with the minimum curvature. Then we adopt a topology-consistent strategy based on the graph rotation system, to trace the simple faces on the interconnecting 3D curves. With the face tracing algorithm, our system can identify the 3D surfaces automatically. After obtaining the boundary curves for the faces, we apply Delaunay triangulation on these faces. Finally, the shape of the triangle mesh that follows the 3D boundary curves is computed by using harmonic interpolation. Meanwhile our system provides real-time algorithms for both control curve generation and the subsequent surface optimization. With the incorporation of topological manipulation into geometrical modeling, we show that automatically generated models are both beneficial and feasible.
How contingent should a lifelike robot be? The relationship between contingency and complexity
Authors: F. Yamaoka, T. Kanda, H. Ishiguro, N. Hagita
Venue: Connection Science (2007)

We believe that lifelikeness is important to make it possible for a communication robot to interact naturally with people. In this research, the relationship between contingency and complexity in a lifelike robot was investigated. We have developed a robot control system to control experimentally contingency and complexity in interaction by combining a humanoid robot with a motion-capturing system. Two independent experiments were conducted with different levels of interaction complexity: simple interaction and complex interaction. Experiments in the simple interaction situation indicated that subjects felt that the robot behaved autonomously when contingency was low, and there was no significant relationship between contingency and a lifelike impression. On the other hand, experiments in the complex interaction situation showed that the robot gave the subjects the impression of being more autonomous and lifelike when contingency was high.
Self-* software architectures and component middleware in pervasive environments
Authors: George Edwards, Chiyoung Seo, Daniel Popescu, Sam Malek, Nenad Medvidovic
Venue: Proceedings of the 5th international workshop on Middleware for pervasive and ad-hoc computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference (2007)

Software systems that execute in embedded and pervasive environments are frequently required to be self-monitoring, self-adapting, and self-healing. However, supporting these self-* capabilities in pervasive environments creates a number of unique engineering challenges. This paper first describes the challenges that we believe to be the most significant based on our experience developing real-world pervasive software applications with self-* capabilities. We then discuss each challenge in the context of four strategies commonly employed in self-* systems: dynamic software update, service discovery, transparent replication, and logical mobility. Finally, we explain how each strategy is implemented in our architectural middleware platform, Prism-MW.
Large sets of Hamilton cycle decompositions of complete bipartite graphs
Authors: Qingde Kang, Hongtao Zhao
Venue: European Journal of Combinatorics (2008)

Let K"2"t"+"1","2"t"+"1-I denote the complete bipartite graph K"2"t"+"1","2"t"+"1 minus a 1-factor. In this paper, we prove that there exist a large set of Hamilton cycle decomposition of K"2"t","2"t and a large set of Hamilton cycle decomposition of K"2"t"+"1","2"t"+"1-I.
A standards based approach to enabling legacy applications on the Grid
Authors: A. Stephen McGough, William Lee, Shikta Das
Venue: Future Generation Computer Systems (2008)

The Grid holds great potential for users, software developers and resource owners. Users of the Grid are abstracted away from its complexity, while software developers are being provided with a rich middleware in which to develop their applications without the need for a large investment in resources. Resource owners are able to expose their resources, potentially for financial reward, without the need to invest in software. However, as most of the applications which currently exist pre-date the concept of the Grid, they lack the appropriate functionality and/or modularization to exploit it. In this paper we propose the use of a standards based job submission and monitoring system which is capable of deploying legacy applications onto existing resources within the Grid, environment containers for mapping applications into Grid applications, along with the use of web-based portals to expose these applications to the end user. We further propose that applications which comprise a number of legacy tasks can be handled through the use of a workflow enactment service submitting each of these tasks through the standards based job submission system. We exemplify our approach through the e-Protein project by taking their existing proteome annotation software and exposing it as a Grid service. We show how the use of this technique over the Grid can significantly reduce the makespan required for the application.
Preservation of proximity privacy in publishing numerical sensitive data
Authors: Jiexing Li, Yufei Tao, Xiaokui Xiao
Venue: Proceedings of the 2008 ACM SIGMOD international conference on Management of data (2008)

We identify proximity breach as a privacy threat specific to numerical sensitive attributes in anonymized data publication. Such breach occurs when an adversary concludes with high confidence that the sensitive value of a victim individual must fall in a short interval --- even though the adversary may have low confidence about the victim's actual value. None of the existing anonymization principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent proximity breach. We remedy the problem by introducing a novel principle called (ε, m)-anonymity. Intuitively, the principle demands that, given a QI-group G, for every sensitive value x in G, at most 1/m of the tuples in G can have sensitive values "similar" to x, where the similarity is controlled by ε. We provide a careful analytical study of the theoretical characteristics of (ε, m)-anonymity, and the corresponding generalization algorithm. Our findings are verified by experiments with real data.