ppxscal/embedding_network huggingface dataset:
Reconstruction of Tree Crown Shape from Scanned Data
Authors: Chao Zhu, Xiaopeng Zhang, Baogang Hu, Marc Jaeger
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses are made on main branch structures and overall shape of a tree, reconstructions are still not satisfactory in terms of silhouette and details. We do think that 3D reconstruction of the tree crown shapes may help to constrain accurate reconstruction of complete real tree geometry. We propose here a novel approach for tree crown reconstruction based on an improvement of alpha shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only. The result is an extracted silhouette mesh model, a concave closure of the input data. We suggest an appropriate scope of proper alpha values, so that the reconstruction of the silhouette mesh is a valid manifold surface. Experimental results show that our technique works well in extracting the crown shapes of real trees.
John von Neumann's Influence on Electronic Digital Computing, 1944-1946
Authors: Nancy Stern
Venue: IEEE Annals of the History of Computing (1980)

The work of John von Neumann is acknowledged to be of singular importance to the computing field. Yet his work has not been studied to any appreciable degree by historians. This paper focuses on von Neumann's early involvement with the ENIAC and the EDVAC during the years 1944 to 1946 while he was a consultant to the Moore School of Electrical Engineering of the University of Pennsylvania. The paper considers some of the issues relating to von Neumann's influence on the Moore School work, on the stored-program concept, and on the future of electronic digital computers in general. 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
A Density-Biased Sampling Technique to Improve Cluster Representativeness
Authors: Ana Paula Appel, Adriano Arantes Paterlini, Elaine P. Sousa, Agma J. Traina, Caetano Traina,  Jr.
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

The volume and complexity of data collected by modern applications has grown significantly, leading to increasingly costly operations for both data manipulation and analysis. Sampling is an useful technique to support manager a more sensible volume in the data reduction process. Uniform sampling has been widely used but, in datasets exhibiting skewed cluster distribution, biased sampling shows better results. This paper presents the BBS - Biased Box Sampling algorithm which aims at keeping the skewed tendency of the clusters from the original data. We also present experimental results obtained with the proposed BBS algorithm.
Loopy Propagation in a Probabilistic Description Logic
Authors: Fabio Gagliardi Cozman, Rodrigo Bellizia Polastro
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

This paper introduces a probabilistic description logic that adds probabilistic inclusions to the popular logic $\mathcal{ALC}$, and derives inference algorithms for inference in the logic. The probabilistic logic, referred to as cr $\mathcal{ALC}$ ("credal" $\mathcal{ALC}$), combines the usual acyclicity condition with a Markov condition; in this context, inference is equated with calculation of (bounds on) posterior probability in relational credal/Bayesian networks. As exact inference does not seem scalable due to the presence of quantifiers, we present first-order loopy propagation methods that seem to behave appropriately for non-trivial domain sizes.
A Rough Set-Based Multiple Criteria Linear Programming Approach for Classification
Authors: Zhiwang Zhang, Yong Shi, Peng Zhang, Guangxia Gao
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, support vector machine and so on. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows platform. Finally, many experiments show that RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining.
Devolved Ontology for Smart Applications
Authors: Iain Duncan Stalker, Nikolay Mehandjiev, Martin Carpenter
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

Many smart applications allow enterprises to communicate effectively with and through interconnected computing resources, however, successful communication presupposes a shared understanding; a so-called semantic alignment. Devolved ontology was developed to promote semantic alignment in agile partnerships. We consider the approach to be promising for any environment where multiple contexts interface and co-locate, including, for example, the Pragmatic Web, virtual organisations and indeed smart applications. We motivate and introduce devolved ontology and show how to use this to foster semantic alignment.
Changes in the Web from 2000 to 2007
Authors: Ramin Sadre, Boudewijn R. Haverkort
Venue: Proceedings of the 19th IFIP/IEEE international workshop on Distributed Systems: Operations and Management: Managing Large-Scale Service Deployment (2008)

The World Wide Web has undergone major changes in recent years. The idea to see the Web as a platform for services instead of a one-way source of information has come along with a number of new applications, such as photo and video sharing portals and Wikis.In this paper, we study how these changes affect the nature of the data distributed over the World Wide Web. To do so, we compare two data traces collected at the web proxy server of the RWTH Aachen. The first trace was recorded in 2000, the other more than seven years later in 2007. We show the major differences, and the similarities, between the two traces and compare our observations with other work. The results indicate that traditional proxy caching is no longer effective in typical university networks.
Preparing area average rainfall on a regular grid from scattered raingauge data
Authors: O. P. Sharma, H. C. Upadhyaya, A. Harzallah, R. Sadourny
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

The problem of obtaining the area average rainfall from scattered raingauge data on to a regular grid has been addressed. A new interpolation formula has been obtained using finite element method giving due consideration to missing observations in any raingauge station data time series. An important feature of this method is that there is no need to redesign the triangulation of the given region in case: (a) if number of observations increase in this region, or (b) if observations are missing in the data time series of any station chosen as a node in triangulating this region. Their contribution to the areal precipitation has been incorporated in a mathematically consistent manner. As an application of this method, daily rainfall time series of 119 raingauge stations for four successive summer monsoons (1986-1989) are utilized to obtain the daily precipitation values on a spatial grid with 1.8&deg; &times; 3.6&deg; (lat/lon) resolution. From the daily gridded rainfall, pentads (5-day averages) of rainfall are obtained. These pentads are arranged in a time series and a principal component analysis (PCA) is performed. The first four principal components explain 63% of the total variance of rainfall over India. The oscillations depicted by these modes agree well with earlier studies. It is concluded that a few dominant PCs of rainfall calculated from pentads (as compared to seasonal and annual average rainfall) explain a larger part of the total variance of rainfall. This method is essentially related to large-scale rainfall depiction. It could, therefore, easily be used to produce global gridded precipitation fields from raingauge measurements. The spatial interpolation code is available on request from the first author.
Statistical Fusion Approach on Keystroke Dynamics
Authors: Pin Shen Teh, Andrew Beng Jin Teoh, Thian Song Ong, Han Foon Neo
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Keystroke dynamics refers to a user’s habitual typing characteristics. These typing characteristics are believed to be unique among large populations. In this paper, we present a novel keystroke dynamic recognition system by using a fusion method. Firstly,we record the dwell time and the flight time as the feature data. We then calculate their mean and standard deviation values and stored. The test feature data will be transformed into the scores via Gaussian probability density function. On the other hand, we also propose a new technique, known as Direction Similarity Measure (DSM) to measure the differential of sign among each coupled characters in a phrase. Lastly, a weighted sum rule is applied byfusing the Gaussian scores and the DSM to enhance the final result. The best result of equal error rate 6.36% is obtained by using our home-made dataset.
A Neural Network-Based Approach to Robot Motion Control
Authors: Uli Grasemann, Daniel Stronger, Peter Stone
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

The joint controllers used in robots like the Sony Aibo are designed for the task of moving the joints of the robot to a given position. However, they are not well suited to the problem of making a robot move through a desired trajectory at speeds close to the physical capabilities of the robot, and in many cases, they cannot be bypassed easily. In this paper, we propose an approach that models both the robot's joints and its built-in controllers as a single system that is in turn controlled by a neural network. The neural network controls the entire trajectory of a robot instead of just its static position. We implement and evaluate our approach on a Sony Aibo ERS-7.
Rejection Strategies Involving Classifier Combination for Handwriting Recognition
Authors: Jose A. Rodríguez, Gemma Sánchez, Josep Lladós
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

This paper introduces a general methodology for detecting and reducing the errors in a handwriting recognition task. The methodology is based on confidence modeling and its main difference is the use of two parallel classifiers for error assessment. The experimental benchmark associated with this approach is described as well as exhaustive results are provided for two real world recognizers on a large database.
Reducing Concurrent Analysis Under a Context Bound to Sequential Analysis
Authors: Akash Lal, Thomas Reps
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

This paper addresses the analysis of concurrent programs with shared memory. Such an analysis is undecidable in the presence of multiple procedures. One approach used in recent work obtains decidability by providing only a partial guarantee of correctness: the approach bounds the number of context switches allowed in the concurrent program, and aims to prove safety, or find bugs, under the given bound. In this paper, we show how to obtain simple and efficient algorithms for the analysis of concurrent programs with a context bound. We give a general reduction from a concurrent program P, and a given context bound K, to a sequential program $P_s^K$ such that the analysis of $P_s^K$ can be used to prove properties about P. We give instances of the reduction for common program models used in model checking, such as Boolean programs and pushdown systems.
Model-based composition of aspects for the development of robust software systems
Authors: Tzilla Elrad
Venue:  (2007)

This work presents a methodology, a supporting language and a compiler, called WEAVR, which address causes of software rigidity and fragility. Current software development practices create a systematic disconnect between the requirements of a system and its implementation. Changes in the system requirements cannot be automatically accommodated by reconfiguring the system implementation accordingly. This disconnect is caused by the lack of techniques to isolate different features or use-cases of an application into separate units of modularity, and compose them automatically according to the requirements of the system. Techniques that accomplish to modularize such features, such as Aspect-Oriented Software Development techniques, tightly couple these modules, called aspects, to other modules of a system. Changes in the implementation of the system are susceptible to break the implementation of the aspects. The WEAVR method and tools enable fine-grained features and use-cases to be modularized and composed in flexible ways, according to specifications that capture the system requirements and operating context. This is achieved by maintaining a strong traceability relationship between the implementation modules of the system and its specification. Implementation modules are obtained through iterative refinement of system models expressed as state machine diagrams. These models define the interface of the module and are interpreted as constraints on the possible future states of the system implementation. This method provides a flexible, yet intuitive mapping relationship between system specification and implementation. Second, the WEAVR language allows implementation modules to be composed according to the concepts defined in their specification models. The composition of module specifications automatically triggers corresponding composition mechanisms at the level of the module implementations, using Aspect-Oriented Modeling techniques. Finally, The WEAVR compiler overcomes the aspect-oriented "fragile pointcut" problem by automatically inferring implementation-level join points from pointcuts expressed in terms of the system specification. The WEAVR tool and methodology were developed as part of a study of Model-Driven Software Development methods and techniques deployed in an industrial context for the development of network control software at Motorola. The WEAVR tools are evaluated in this context, with respect to the robustness, flexibility and traceability of the artifacts of the system.
Bite: Workflow Composition for the Web
Authors: Francisco Curbera, Matthew Duftler, Rania Khalaf, Douglas Lovell
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

Service composition is core to service oriented architectures. In the Web, mainstream composition is practiced in client-side or server-side mashups, such as providing visual widgets on top of Google Maps results. This paper presents an explicit, workflow based composition model for Web applications called Bite. In contrast with prior attempts to bring workflow capabilities to the Web environment, Bite can deal with data integration as well as interactive, asynchronous workflows with multi-party interactions, and is architected to support protocols currently in use by Web applications. The Bite development model is designed for simplicity and short development cycle by taking a scripting approach to workflow development.
Generating Maude formal specifications from AUML diagrams
Authors: Farid Mokhati, Noura Boudiaf, Linda Badri, Mourad Badri
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

In this paper, we present a formal and systematic approach allowing translating the specification of the interactions between agents, described using AUML formalism, in a Maude specification. Based on rewriting logic, the formal and object-oriented language Maude offers an interesting way for concurrent systems formal specification and programming. The main objectives of our approach are essentially: (1) to specify formally multi-agent systems behavior and, (2) to provide a healthy description preserving the consistency in their behavior and supporting their verification and validation process. The Maude descriptions, generated in the context of the developed framework, have been validated using the platform supporting the Maude language.
An approximation algorithm for sequential rectangle placement
Authors: Amos Israeli, Oran Sharon
Venue: Information Processing Letters (2008)

We present a constant factor, polynomial time approximation algorithm for the problem of scheduling a sequence of rectangles on a matrix. The approximation is on the area covered by the rectangles, and a rectangle is placed on the matrix only if all its preceding rectangles in the sequence were already placed.
Graph-Based Knowledge Representations for Decision Support Systems
Authors: Roman Simiński
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

Expert systems are problem solvers for specialized domains of competence in which effective problem solving normally requires human expertise. Rough set theory is intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, reduces all redundant objects and attributes. Moreover, it is being used for the extraction of rules from databases. Expert systems are often implemented using knowledge discovered in data bases, for example, using rough set based rule generation method. When we try to analyze large, possibly hierarchical rule sets, we often seek useful graphical representation.A proposition of such graphical representation decision networks we can find in [4]. The main aim of this work is to present own graph-based rule base representation method and its utilization for verification task. The paper firstly presents the decision units conception needed to establish our verification approach. Next we present the usage of decision units net in global verification and modeling issues, and some remarks on decision units and data mining. The last chapter draws the main conclusions.
Adaptive Request Scheduling for Parallel Scientific Web Services
Authors: Heshan Lin, Xiaosong Ma, Jiangtian Li, Ting Yu, Nagiza Samatova
Venue: Proceedings of the 20th international conference on Scientific and Statistical Database Management (2008)

Scientific web services often possess data models and query workloads quite different from commercial ones and are much less studied. Individual queries have to be processed in parallel by multiple server nodes, due to the computation- and data-intensiveness of the processing. Meanwhile, each query is performed against portions of a large, common dataset. Existing scheduling policies from traditional environments (namely cluster web servers and supercomputers) consider only the data or the computation aspect alone and are therefore inadequate for this new type of workload.In this paper, we systematically investigate adaptive scheduling for scientific web services, by taking into account parallel computation scalability, data locality, and load balancing. Our case study focuses on high-throughput query processing on biological sequence databases, a fundamental task performed daily by millions of scientists, who increasingly prefer to use web services powered by parallel servers. Our research indicates that intelligent resource allocation and scheduling are crucial in improving the overall performance of a parallel sequence database search server. Failure to consider either the parallel computation scalability or the data locality issues can significantly hurt the system throughput and query response time. Also, no single static strategy works best for all request workloads or all resources settings. In response, we present several dynamic scheduling techniques that automatically adapt to the request workload and system configuration in making scheduling decisions. Experiments on a cluster using 32 processors show the combination of these techniques delivers a several-fold improvement in average query response time across various workloads.
The Impact of Taxes on Intra-week Stock Return Seasonality
Authors: Virgilijus Sakalauskas, Dalia Kriksciuniene
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

In this paper we explore the impact of trading taxes (commissions) on day-of-the-week effect in the Lithuanian Stock market. We applied the computational model for processing trading activities only on the particular days of the week. The suggested algorithm of trading shares not only reveals presence of the day-of-the-week anomaly, but allows comparing it to the influence of the trading taxes by estimating the final return of the selected shares. As the taxes of each transaction depend on the investment sum, therefore the suggested algorithm had to optimize the number of operations for ensuring the biggest gain. The research revealed significance of intra-week stock return seasonality for majority of shares (17 out of total 24). The advantages of the suggested method include its ability to better specify the shares for performing intra-week seasonality-based transactions, even though embracing of the trading commissions reduces visibility of the effect.
A Dual-Mode Learning Mechanism Combining Knowledge-Education and Machine-Learning
Authors: Yichang Chen, Anpin Chen
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

From 1956, the definitions of learning according to Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism of AI.This work tries to discuss the learning process from the traditional AI learning models which are almost based on trial and error style. Furthermore, some relative literatures have pointed out that teaching-base education would increase the learning efficiency better than trial and error style. That is the reason we enhance the learning process to propose a dual-perspective learning mechanism, E R-R XCS. As for XCS is a better accuracy model of AI, we have applied it as a basement and proposed to develop an intelligence-learning model. Finally, this work will give the inference discussion about the accuracy and accumulative performance of XCS, R-R XCS, and E R-R XCS respectively, and the obvious summary would be concluded. That is, the proposed dual-learning mechanism has enhanced successfully.