ppxscal/embedding_network huggingface dataset:
Estimation of a change-point in the mean function of functional data
Authors: Alexander Aue, Robertas Gabrys, Lajos Horváth, Piotr Kokoszka
Venue: Journal of Multivariate Analysis (2009)

The paper develops a comprehensive asymptotic theory for the estimation of a change-point in the mean function of functional observations. We consider both the case of a constant change size, and the case of a change whose size approaches zero, as the sample size tends to infinity. We show how the limit distribution of a suitably defined change-point estimator depends on the size and location of the change. The theoretical insights are confirmed by a simulation study which illustrates the behavior of the estimator in finite samples.
Research on Traffic Monitoring Network and Its Traffic Flow Forecast and Congestion Control Model Based on Wireless Sensor Networks
Authors: Laisheng Xiao, Xiaohong Peng, Zhengxia Wang, Bing Xu, Pengzhi Hong
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

In this paper, we have made a comprehensive study about the key technologies including applying wireless sensor networks to the traffic monitoring network, its traffic flow forecast based on gray forecasting model and traffic congestion control. According to the features that wireless sensor networks have no space constraints, flexible distribution, mobile convenience and quick reaction, we present a scheme that uses wireless sensor networks to monitor city transport vehicles and have designed a traffic monitoring system based on wireless sensor network that is applicable to all types of city environment. With the system, we can monitor the important roads that are easily blocked and find out the time changing law of traffic congestion, and then put the focus on monitoring them in order to greatly reduce the investment and achieve high efficiency. As far as the traffic flow forecasting methods concerned, we use Adaptive GM (1, 1) Model which have a real-time rolling forecast for city traffic and have a better forecast results. Because of the fewer study about the traffic congestion control in the current academia, we make a deep study about the traffic congestion control issues in this article. Learn from mature congestion control algorithm in computer network, we have designed an algorithm of traffic flow congestion control and scheduling for traffic network, which is called TRED. We have used it for real-time traffic scheduling and have opened up a new way to study and solve traffic congestion control problems.
Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs
Authors: Silvia Acid, Luis M. de Campos
Venue: Journal of Artificial Intelligence Research (2003)

Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.
A Model-Driven Approach for Generating Business Processes and Process Interaction Semantics
Authors: Chenting Zhao, Zhenhua Duan, Man Zhang
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Business process modeling (BPM) is one of the key technologies to benefit enterprises with higher efficiencies and greater business values. However, BPM requires intensive business expertise and human resources. Thus, it is valuable and challenging to enable fast delivery of reusable business process designs, regarding the existence of various popular business processes modeling languages and fast changing markets. In this paper, we generate language-specific business process models and process interaction semantics from UML 2.0 process models. A WS-BPEL metadata model and a process interaction semantics metadata model is defined to annotate UML 2.0 business process model as stereotypes; with a set of transformation patterns, we can generate a WS-BPEL business process model and a process interaction semantics file from each annotated UML 2.0 process model. Our approach can also be similarly applied to other process modeling languages to highly reuse the business process model in UML 2.0.
Statistical properties of differences between low and high resolution CMAQ runs with matched initial and boundary conditions
Authors: Chae Young Lim, Michael Stein, Jason Ching, Ruen Tang
Venue: Environmental Modelling Software (2010)

Numerical simulations of air quality models provide unique and different outputs for different choices of grid size. Thus, an important task is to understand the characteristics of model outcomes as a function of grid size in order to assess the quality of the model as to its fitness for meeting a specific design objective. This type of assessment is somewhat different than that of traditional operational performance and diagnostic type model evaluation. There, the objective is towards assessing errors in numerical models of air quality and utilizing concentration measurements from monitors to provide the bases for guidance towards model improvement and for their assessment of ability to predict and retrospectively map air quality. However, observations used as ''truth'' to assess model performance have themselves properties unique to the data collection protocols, siting and spatial density of deployment. In the data assimilation community, the term ''model error'' is used for the difference between model output given perfect inputs and the ''truth'' (Kalnay, 2003). In this paper, we are concerned with one aspect of this ''model error'', the discrepancy due to discretization of space by choice of grid size in the model. To understand discrepancy due to discretization, outputs from the Community Multiscale Air Quality model (CMAQ) at two resolutions are studied. The lower resolution run is carried out so that its initial and boundary conditions are as similar as possible to those for the higher resolution run, thus minimizing this source of discrepancies and allowing us to isolate discrepancies due to discretization. Differences are analyzed from a statistical perspective by comparing marginal distributions of two outputs and considering spatial variation of the differences. Results indicate sharp increases in spatial variation of the differences for the first few hours of running the model, followed by small increases thereafter. The spatial variation of the differences depends on the individual spatial structure of the original processes, which we show varies with the time of day. We also show that the spatial variations on sub-regions depend on whether the sub-region is in a rural or an urban area.
Modelling Normative Frameworks Using Answer Set Programing
Authors: Owen Cliffe, Marina Vos, Julian Padget
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

Norms and regulations play an important role in the governance of human society. Social rules such as laws, conventions and contracts prescribe and regulate our behaviour, however it is possible for us to break these rules at our discretion and face the consequences. By providing the means to describe and reason about norms in a computational context, normative frameworks may be applied to software systems allowing for automated reasoning about the consequences of socially acceptable and unacceptable behaviour. In this paper, we outline our mathematical formulation for normative frameworks and describe how its semantics can be represented in ASP, thus enabling the construction of models of normative systems that can be subjected to formal verification and that can act as functional repositories of normative knowledge for the software components that participate in them.
Process Arrival Pattern and Shared Memory Aware Alltoall on InfiniBand
Authors: Ying Qian, Ahmad Afsahi
Venue: Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2009)

Recent studies have shown that processes in real applications can arrive at the collective calls at different times. This imbalanced process arrival pattern can significantly affect the performance of the collective operations. MPI_Alltoall() is a communication-intensive collective operation that is used in many parallel scientific applications. Its efficient implementation under different process arrival patterns is critical to the performance of applications that use them frequently. In this paper, we propose novel RDMA-based process arrival pattern aware MPI_Alltoall() algorithms over InfiniBand clusters. We extend the algorithms to be shared memory aware for small to medium size messages. The micro-benchmark and application results indicate that the proposed algorithms outperform the native implementation as well as their non-process arrival pattern aware counterparts when processes arrive at different times.
101 Quick and Easy Secrets to Create Winning Photographs, 1st edition
Authors: Matthew Bamberg
Venue:  (2009)

Discover how to take compelling photographs utilizing all the awesome features of your digital camera. 101 Quick and Easy Secrets to Create Winning Photographs shows you how to use composition and technical methods and creative and artistic applications to achieve amazing images, using both in-camera and post-processing techniques. Every chapter covers a different overall theme, with each technique laid out in a two-page spread that features easy-to-follow instructions to achieve the shot and stunning, full-color photographs showing the results. Youll learn how to manipulate images with light and shadow, make people and animals look more lifelike, create a mood using the weather, make colors pop, and even add unusual effects to your photographs. Each image incorporates different photography skills or specific camera features so you really discover how to get the most out of your digital camera. One part photo essay collection and one part how-to, 101 Quick and Easy Secrets to Create Winning Photographs is perfect for new digital camera owners who are eager to learn how to get amazing photographs. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Selective attention and the acquisition of spatial semantics
Authors: James M. Hogan, Joachim Diederich, Gerard D. Finn
Venue: Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning (1998)

The acquisition of the semantics of natural language spatial terms is considered within the cognitive framework introduced by (Langacker, 1987), and the computational framework of the Berkeley Lo project (Feldman et. al., 1990). We describe a computational model which incorporates selective attention mechanisms to facilitate the identification of significant objects within the visual field, and their consequent binding to linguistic relational identifiers (for example, the trajector and landmark) according to the conventions of the input language. In contrast to previous work in this area, the approach allows extension of the system to more sophisticated (potentially cluttered and feature-laden) input scenes and referential linguistic phenomena, without a major redesign of the system. The application of the model to lexemes describing static concepts such as the English above, below and in is discussed, as are extensions to dynamic concepts.
Efficient structure learning in factored-state MDPs
Authors: Alexander L. Strehl, Carlos Diuk, Michael L. Littman
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

We consider the problem of reinforcement learning in factored-state MDPs in the setting in which learning is conducted in one long trial with no resets allowed. We show how to extend existing efficient algorithms that learn the conditional probability tables of dynamic Bayesian networks (DBNs) given their structure to the case in which DBN structure is not known in advance. Our method learns the DBN structures as part of the reinforcement-learning process and provably provides an efficient learning algorithm when combined with factored Rmax.
Two-sided tolerance intervals in the exponential case: Corrigenda and generalizations
Authors: Arturo J. Fernández
Venue: Computational Statistics Data Analysis (2010)

Exact two-sided guaranteed-coverage tolerance intervals for the exponential distribution which satisfy the traditional ''equal-tailedness'' condition are derived in the failure-censoring case. The available empirical information is provided by the first r ordered observations in a sample of size n. A Bayesian approach for the construction of equal-tailed tolerance intervals is also proposed. The degree of accuracy of a given tolerance interval is quantified. Moreover, the number of failures needed to achieve the desired accuracy level is predetermined. The Bayesian perspective is shown to be superior to the frequentist viewpoint in terms of accuracy. Extensions to other statistical models are presented, including the Weibull distribution with unknown scale parameter. An alternative tolerance interval which coincides with an outer confidence interval for an equal-tailed quantile interval is also examined. Several important computational issues are discussed. Three censored data sets are considered to illustrate the results developed.
Revenue maximization and distributed power allocation in cognitive radio networks
Authors: Shaolei Ren, Mihaela van der Schaar
Venue: Proceedings of the 2009 ACM workshop on Cognitive radio networks (2009)

Cognitive radio is an enabling technology that allows unlicensed users to opportunistically access the spectrum in order to enhance the spectrum efficiency. In this paper, we consider a cognitive system wherein there exists a primary relay network and a secondary network. In order to efficiently exploit the available spectrum and gain revenues whenever the primary relay infrastructure is not utilized, the primary network leases its unused bandwidth and the idle relay node to the secondary users. As a reimbursement, the secondary users make payments to the primary network based on the service they receive. We first characterize the interactions between the primary and secondary users using a buyer/seller model. Specifically, the price is determined by the primary network such that the revenue is maximized. On the buyer side, given the specified price, the secondary users competitively access the spectrum and employ the primary relay node to forward their packets. Then, we model each secondary user as a selfish player, which aims at maximizing its own benefit through power allocation, and analyze the competition among the secondary users within the framework of non-cooperative game theory. It is shown that, in the game played by the secondary network, there always exists a unique Nash equilibrium point that can be achieved through distributed iterations. Next, we propose a low-complexity algorithm, in which the primary network charges the secondary users at a sub-optimal price and gains close-to-optimal revenues. Extensive simulations are conducted to verify the performance of the proposed methods from both a primary as well as a secondary network perspective.
Cube Based Incremental Data Mining: Using Multi-Dimensional Cubes for Incremental Data Mining
Authors: Ziv Pollak
Venue:  (2009)

This work presents a cube-based approach for incremental data mining that operates on the data, rather than on the data mining algorithms. The idea was to build a compressed replica of the full-blown database by representing the database by means of multi-dimensional cubes, and then applying the original data mining algorithms on the cube-based data. This way, the storage requirement to accommodate the database is not affected by the new data. Yet, the fact that we used the original data mining algorithms on the cube-based data makes our incremental data mining approach very general as it can be applied to all types of data mining models. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Study on Motivation in Healthcare Treatment Using a Networked Healthcare Guidance System
Authors: Kaori Fujimura, Masahiro Shiraishi, Kenji Ogura, Yuji Maeda
Venue: Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services (2009)

To support the continuing effort towards improving healthcare, we have developed a computer system that enables patients and healthcare counselors to access data such as weight and the number of steps walked, which is uploaded by patients allowing counselors to advise patients based on their patients' healthcare records using a video phone. A six-month trial was performed to evaluate the effectiveness of the system. From the daily-uploaded data, 70% of the patients continued to upload their data until the end of the trial. According to the results of the questionnaire, half of the patients underwent a behavioral change based on the Transtheoretical Model [1]. We found that having a clear goal, checking data daily, and continuous support provided by healthcare counselors motivate patients to continue a particular regimen.
Design for All Approach with the Aim to Support Autonomous Living for Elderly People in Ordinary Residences --- An Implementation Strategy
Authors: Claes Tjäder
Venue: Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments (2009)

Most elderly want to remain in their ordinary home. There are products and services available which make it possible to support an autonomous living with a high quality of life. To finds ways how to implement DfA supportive technology in co-operation with housing enterprises is dealt with in this paper. Methods used at some workshops to single out measures from different perspectives are described.
Lattice-based minimum error rate training for statistical machine translation
Authors: Wolfgang Macherey, Franz Josef Och, Ignacio Thayer, Jakob Uszkoreit
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

Minimum Error Rate Training (MERT) is an effective means to estimate the feature function weights of a linear model such that an automated evaluation criterion for measuring system performance can directly be optimized in training. To accomplish this, the training procedure determines for each feature function its exact error surface on a given set of candidate translations. The feature function weights are then adjusted by traversing the error surface combined over all sentences and picking those values for which the resulting error count reaches a minimum. Typically, candidates in MERT are represented as N-best lists which contain the N most probable translation hypotheses produced by a decoder. In this paper, we present a novel algorithm that allows for efficiently constructing and representing the exact error surface of all translations that are encoded in a phrase lattice. Compared to N-best MERT, the number of candidate translations thus taken into account increases by several orders of magnitudes. The proposed method is used to train the feature function weights of a phrase-based statistical machine translation system. Experiments conducted on the NIST 2008 translation tasks show significant runtime improvements and moderate BLEU score gains over N-best MERT.
A system-centric scheduling policy for optimizing objectives of application and resource in grid computing
Authors: Li Chunlin, Li Layuan
Venue: Computers and Industrial Engineering (2009)

In grid computing, grid users who submit applications and resources providers who provide resources have different motivations when they join the grid. Application-centric scheduling aims to optimize the performance of individual application. Resource-centric scheduling aims to optimize the resource utilization of resources provider. Due to autonomy both in grid users and resource providers, the objectives of application-centric and resource-centric scheduling often conflict. The paper proposes a system-centric scheduling that provides a solution of joint optimization of the objectives for both the grid resource and grid application. Utility functions are used to express the objectives of grid resource and application. The system-centric scheduling policy can be formulated as joint optimization of utilities of grid applications and grid resources, which combine both application centric and resource-centric scheduling benefits. Simulations are conducted to study the performance of the system-centric scheduling algorithm. The experiment results show that the system-centric scheduling algorithm yields significantly better performance than application-centric scheduling algorithm and resource-centric scheduling algorithm.
Manufacturing Capability Evaluation of E-HUB Based on Harmony Theory
Authors: Xie Qingsheng, Pan Weijie, Li Shaobo, Yang Guanci
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Harmony theory summarizes and abstracts the mechanism of social economic development. Using harmony theory to analyze the harmoniousness of virtual enterprise, it can realize the co-adaptation degree more between enterprises which compose to a virtual enterprise set and improve its success probability. Based on the methods in manufacturing information theory, taking regional small and medium-sized enterprises as application background, the author studies modeling and application of networked manufacturing system, designs the evaluation index system of dynamic alliance, proposes a new kind of evaluation index system based on the fuzzy comprehensive evaluation algorithm, implements the algorithm and synthetic evaluation model of dynamic alliance manufacturing resource based on fuzzy algorithm, studies and evaluates the harmoniousness of dynamic alliance combined with harmony theory and constructs a harmonious, steady dynamic alliance enterprises set. The experiment proves the method in this article has some advantages such as small codes in programming, high precision in evaluation, and so on.
A Business Model for Mobile Ad-Hoc Communities
Authors: Stefan Stieglitz, Christoph Fuchss, Christoph Lattemann
Venue: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (2009)

Portable devices with Bluetooth interfaces, such as mobile phones and PDAs, are enabled for near field communication (