ppxscal/embedding_network huggingface dataset:
Dynamically adapting file domain partitioning methods for collective I/O based on underlying parallel file system locking protocols
Authors: Wei-keng Liao, Alok Choudhary
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

Collective I/O, such as that provided in MPI-IO, enables process collaboration among a group of processes for greater I/O parallelism. Its implementation involves file domain partitioning, and having the right partitioning is a key to achieving high-performance I/O. As modern parallel file systems maintain data consistency by adopting a distributed file locking mechanism to avoid centralized lock management, different locking protocols can have significant impact to the degree of parallelism of a given file domain partitioning method. In this paper, we propose dynamic file partitioning methods that adapt according to the underlying locking protocols in the parallel file systems and evaluate the performance of four partitioning methods under two locking protocols. By running multiple I/O benchmarks, our experiments demonstrate that no single partitioning guarantees the best performance. Using MPI-IO as an implementation platform, we provide guidelines to select the most appropriate partitioning methods for various I/O patterns and file systems.
Detection of Lung Nodule Candidates in Chest Radiographs
Authors: Carlos S. Pereira, Hugo Fernandes, Ana Maria Mendonça, Aurélio Campilho
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

This paper presents an automated method for the selection of a set of lung nodule candidates, which is the first stage of a computer-aided diagnosis system for the detection of pulmonary nodules. An innovative operator, called sliding band filter (SBF), is used for enhancing the lung field areas. In order to reduce the influence of the blood vessels near the mediastinum, this filtered image is multiplied by a mask that assigns to each lung field point an a priori probability of belonging to a nodule. The result is further processed with a watershed segmentation method that divides each lung field into a set of non-overlapping areas. Suspicious nodule locations are associated with the regions containing the highest regional maximum values. The proposed method, whose result is an ordered set of lung nodule candidate regions, was evaluated on the 247 images of the JSRT database with very promising results.
Extensible Universes for Object-Oriented Data Models
Authors: Achim D. Brucker, Burkhart Wolff
Venue: Proceedings of the 22nd European conference on Object-Oriented Programming (2008)

We present a datatype package that enables the shallow embedding technique to object-oriented specification and programming languages. This datatype package incrementally compiles an object-oriented data model to a theory containing object-universes, constructors, accessors functions, coercions between dynamic and static types, characteristic sets, their relations reflecting inheritance, and the necessary class invariants. The package is conservative, i.e., all properties are derived entirely from axiomatic definitions. As an application, we use the package for an object-oriented core-language called imp++, for which correctness of a Hoare-Logic with respect to an operational semantics is proven.
An Agent-Oriented Quantum Key Distribution for Wi-Fi Network Security
Authors: Xu Huang, Dharmendra Sharma
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

There are a large variety of kinds of mobile wireless networks, Wi-Fi, based on the IEEE 802.11 standard, is a wireless local area network, mainly used in offices and campus at universities, meeting rooms, halls in hotels or in airports. For such limited coverage area, IEEE 802.11 standard may be observed as building-oriented environment, which potentially offers a chance to let quantum key distribution (QKD) play a role in the security of wireless communications. In fact, secured data transmission is one of the prime aspects of wireless networks as they are much more vulnerable to security attacks. In this paper, we explore the possibility of using an agent-oriented Quantum Key Distribution for authentication and data encryption for IEEE 802.11 standard based on our previous research. It will focus on some basic concept that how QKD merges the wireless communication, in particular the IEEE 802.11 standard. The software implementation of the first two phases of QKD, namely (a) raw key extraction and (b) error estimation, will be carefully investigated in this paper. A Wi-Fi based concept has been extended to the implement the communication between two users in C++ language in this paper.
Biologically Motivated Face Selective Attention Model
Authors: Woong-Jae Won, Young-Min Jang, Sang-Woo Ban, Minho Lee
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In this paper, we propose a face selective attention model, which is based on biologically inspired visual selective attention for human faces. We consider the radial frequency information and skin color filter to localize a candidate region of human face, which is to reflect the roles of the V4 and the infero-temporal (IT) cells. The ellipse matching based on symmetry axis is applied to check whether the candidate region contain a face contour feature. Finally, face detection is conducted by face form perception model implemented by an auto-associative multi-layer perceptron (AAMLP) that mimics the roles of faces selective cells in IT area. Based on both the face-color preferable attention and face-form perception mechanism, the proposed model shows plausible performance for localizing face candidates in real time.
Parallel Lattice Boltzmann Flow Simulation on Emerging Multi-core Platforms
Authors: Liu Peng, Ken-Ichi Nomura, Takehiro Oyakawa, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

A parallel Lattice Boltzmann Method (pLBM), which is based on hierarchical spatial decomposition, is designed to perform large-scale flow simulations. The algorithm uses critical section-free, dual representation in order to expose maximal concurrency and data locality. Performances of emerging multi-core platforms--PlayStation3 (Cell Broadband Engine) and Compute Unified Device Architecture (CUDA)--are tested using the pLBM, which is implemented with multi-thread and message-passing programming. The results show that pLBM achieves good performance improvement, 11.02 for Cell over a traditional Xeon cluster and 8.76 for CUDA graphics processing unit (GPU) over a Sempron central processing unit (CPU). The results provide some insights into application design on future many-core platforms.
Combining Weighted SVMs and Spectrum-Based kNN for Multi-classification
Authors: Ling Ping, Lu Nan, Wang Jian-Yu, Zhou Chun-Guang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper presents a Multi-Classification Schema (MCS) which combines Weighted SVMs (WSVM) and Spectrum-based kNN (SkNN). Basic SVM is equipped with belief coefficients to reveal its capacity in identifying classes. And basic SVM is built in individual feature space to bring adaptation to diverse training data context. Coupled with a weighted voting strategy and a local informative metric, SkNN is used to address the case rejected by all basic classifiers. The local metric is derived from most discriminant directions carried by data spectrum information. Two strategies of MCS benefit computational cost: training dataset reduction, and pre-specification of SkNN working set. Experiments on real datasets show MCS improves classification accuracy with moderate cost compared with the state of the art.
Tag Recommendations in Folksonomies
Authors: Robert Jäschke, Leandro Marinho, Andreas Hotho, Lars Schmidt-Thieme, Gerd Stumme
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Collaborative tagging systems allow users to assign keywords--so called "tags"--to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.In this paper we evaluate and compare two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank. We show that both provide better results than non-personalized baseline methods. Especially the graph-based recommender outperforms existing methods considerably.
Semantic Descriptions in an Enterprise Search Solution
Authors: Uwe Crenze, Stefan Köhler, Kristian Hermsdorf, Gunnar Brand, Sebastian Kluge
Venue: Reasoning Web: Third International Summer School 2007, Dresden, Germany, September 3-7, 2007, Tutorial Lectures (2007)

Today customers want to use powerful search engines for their huge and increasing content repositories. Full-text-only products with simple result lists are not enough to satisfy this community. Different content sources require different analyzing and indexing strategies and a content-specific result set presentation. There is a lot of research in the field of using semantic web technologies for information retrieval. A wide range of useful standard vocabularies and powerful frameworks have been developed that can be used to gather, transform and store metadata. However, in practise we see a gap between the state of art of information retrieval and customer needs with a defined prise-performance relation. It is a challenge to index a large file server with heterogeneous content annotated with metadata from different vocabularies, to provide an ontology-based navigation, to produce semantic annotated search results, to use faceted browsers as powerful filtering mechanism and do that with an out-of-the-box solution, which is stable, has a good performance and provides a simple way to configure it. With this viewpoint we present in this paper the usage of RDF-based semantic descriptions in an enterprise search solution developed at interface:projects.This paper covers lessons learned from developing a metadata-focused information retrieval system called inter:gator. Especially we discuss the challenges and possible solutions in an enterprise (-wide) search scenario, and show the place where semantic descriptions matter in such a solution.
Instruction scheduling using evolutionary programming
Authors: Anjali Mahajan, M. S. Ali, Mamta Patil
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

Instruction scheduling is the problem of scheduling the assembly instructions output from the code generator to increase the efficiency of the final code. The instruction scheduling problem is mainly solved heuristically since finding an optimal solution requires significant computational resources and, in general, the problem of optimally scheduling instructions is known to be NP-Complete. In this paper, the specific problem of automatically creating instruction scheduling heuristics is addressed.
Expressing Dublin Core in Topic Maps
Authors: Steve Pepper
Venue: Scaling Topic Maps: Third International Conference on Topic Maps Research and Applications, TMRA 2007 Leipzig, Germany, October 11-12, 2007 Revised Selected Papers (2008)

The Dublin Core Metadata Initiative is an open organization engaged in the development of interoperable online metadata standards that support a broad range of purposes and business models. Its most important standard is the Dublin Core Metadata Element Set [1], a vocabulary of fifteen proper ties for use in resource description, which was approved as ISO 15836:2003 [2]. This and other vocabularies developed by Dublin Core are defined as abstract models which may be expressed in any number of different syntaxes. This paper presents a proposal for expressing such metadata using Topic Maps.
ADIFOR-Generating Derivative Codes from Fortran Programs
Authors: Christian Bischof, Alan Carle, George Corliss, Andreas Griewank, Paul Hovland
Venue: Scientific Programming (1992)

The numerical methods employed in the solution of many scientific computing problems require the computation of derivatives of a function f $R^N$&rarr;$R^m$. Both the accuracy and the computational requirements of the derivative computation are usually of critical importance for the robustness and speed of the numerical solution. Automatic Differentiation of FORtran (ADIFOR) is a source transformation tool that accepts Fortran 77 code for the computation of a function and writes portable Fortran 77 code for the computation of the derivatives. In contrast to previous approaches, ADIFOR views automatic differentiation as a source transformation problem. ADIFOR employs the data analysis capabilities of the ParaScope Parallel Programming Environment, which enable us to handle arbitrary Fortran 77 codes and to exploit the computational context in the computation of derivatives. Experimental results show that ADIFOR can handle real-life codes and that ADIFOR-generated codes are competitive with divided-difference approximations of derivatives. In addition, studies suggest that the source transformation approach to automatic differentiation may improve the time to compute derivatives by orders of magnitude.
ReMauve: A Relational Model Tree Learner
Authors: Celine Vens, Jan Ramon, Hendrik Blockeel
Venue: Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (2007)

Model trees are a special case of regression trees in which linear regression models are constructed in the leaves. Little attention has been paid to model trees in relational learning, mainly because the task of learning linear regression equations in this context involves dealing with non-determinacy of predictive attributes. Whereas existing approaches handle this non-determinacy issue either by selecting a single value or by aggregating over all values, in this paper we present a model tree learning system that combines both.
Adaptation to Force in the Haptic Rendering of Virtual Environments
Authors: Mehrdad H. Zadeh, David Wang, Eric Kubica, Jeffery K. Hovis
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

Realistic haptic rendering is one of the most challenging issues in the field of virtual reality. The intent is for the user to experience the same kinesthetic sensations in the virtual realm as they would in the real world. Therefore, we need to know the capabilities of the human haptic system. The issue investigated in this study was the adaptation to force feedbacks in a haptic-enabled virtual environment (VE). Psychophysical experiments are conducted to study how adaptation is influenced by changes in the force intensity and direction. The results indicate that the users definitely adapt to forces in a VE. However, the force direction and force increment/decrement do not affect adaptation. This information can be used to create haptic models with much less detail.
On the Merits of Temporal Testers
Authors: A. Pnueli, A. Zaks
Venue: 25 Years of Model Checking: History, Achievements, Perspectives (2008)

The paper discusses the merits of temporal testers, which can serve as a compositional basis for automata construction corresponding to temporal formulas in the context of ltl, psl, and mitl logics. Temporal testers can be viewed as (non-deterministic) transducers that, at any point, output a boolean value which is 1 iff the corresponding temporal formula holds starting at the current position.The main advantage of testers, compared to acceptors (such asBüchi automata) is their compositionality. Namely, a tester for a compound formula can be constructed out of the testers for its sub-formulas. Besides providing the construction of testers for formulas specified in ltl, psl, and mitl, the paper also presents a general overview of the tester methodology, and highlights some of the unique features and applications of transducers including compositional deductive verification of ltl properties.
Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project, 1 edition
Authors: Manfred Nagl, Wolfgang Marquardt
Venue: Lecture Notes In Computer Science (2008)

IMPROVE stands for "Information Technology Support for Collaborative and Distributed Design Processes in Chemical Engineering" and is a large joint project of research institutions at RWTH Aachen University. This volume summarizes the results after 9 years of cooperative research work. The focus of IMRPOVE is on understanding, formalizing, evaluating, and, consequently, improving design processes in chemical engineering. In particular, IMPROVE focuses on conceptual design and basic engineering, where the fundamental decisions concerning the design or redesign of a chemical plant are undertaken. Design processes are analyzed and evaluated in collaboration with industrial partners.
No mining, no meaning: relating documents across repositories with ontology-driven information extraction
Authors: Víctor Codocedo, Hernán Astudillo
Venue: Proceeding of the eighth ACM symposium on Document engineering (2008)

Far from eliminating documents as some expected, the Internet has lead to a proliferation of digital documents, without a centralized control or indexing. Thus, identifying relevant documents becomes simultaneously more important and much harder, since what users require may be dispersed across many documents and many repositories. This paper describes Ontologic Anchoring, a technique to relate documents in domain ontologies, using named entity recognition (a natural-language processing approach) and semantic annotation to relate individual documents to elements in ontologies. This approach allows document retrieval using domain-level inferences, and integration of repositories with heterogeneous media, languages and structure. Ontological anchoring is a two-way street: ontologies allow semantic indexing of documents, and simultaneously new documents enrich ontologies. The approach is illustrated with an initial deployment for heritage documents in Spanish.
Frequent Itemset Mining from Databases Including One Evidential Attribute
Authors: Mohamed Anis Bach Tobji, Boutheina Ben Yaghlane, Khaled Mellouli
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

Frequent Itemset Mining (FIM) problem has been extensively tackled in the context of perfect data. However, real applications showed that data are often imperfect (incomplete and/or uncertain) which leads to the need of FIM algorithms that process imperfect databases. In this paper we propose a new algorithm for mining frequent itemsets from databases including exactly one evidential attribute. An evidential attribute is an attribute that could have uncertain values modelled via the evidence theory, i.e., a basic belief assignment. We introduce in this paper a variant of the structure Belief Itemset Tree (BIT) for mining frequent itemsets from evidential data and we lead some experiments that showed efficiency of our mining algorithm compared to the existing ones.
Density functional calculations of the frequency-dependent optical rotation: Comparison of theory and experiment for the gas phase
Authors: C. Diedrich, S. Causemann, S. Grimme
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

The optical rotations (OR) of five chiral organic molecules have been calculated by time-dependent density functional response theory (TDDFT) employing four different density functionals. The results are compared with experimental gas phase data in order to explore the inherent accuracy of the functionals. The theoretical results obtained with the BHLYP hybrid functional including 50% "exact" Hartree-Fock exchange compare very well with the experimental data and also better with those derived from the gas-phase than from solution. Non-hybrid functionals show a tendency to overestimate the OR. The anomalous behavior of the methyloxirane molecule which shows sign changes with solvent and excitation frequency is discussed.
A Simple P-Matrix Linear Complementarity Problem for Discounted Games
Authors: Marcin Jurdziński, Rahul Savani
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

The values of a two-player zero-sum binary discounted game are characterized by a P-matrix linear complementarity problem (LCP). Simple formulas are given to describe the data of the LCP in terms of the game graph, discount factor, and rewards. Hence it is shown that the unique sink orientation (USO) associated with this LCP coincides with the strategy valuation USO associated with the discounted game. As an application of this fact, it is shown that Murty's least-index method for P-matrix LCPs corresponds to both known and new variants of strategy improvement algorithms for discounted games.
A New Constructive Algorithm for Designing and Training Artificial Neural Networks
Authors: Md. Abdus Sattar, Md. Monirul Islam, Kazuyuki Murase
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

This paper presents a new constructive algorithm, called problem dependent constructive algorithm (PDCA), for designing and training artificial neural networks (ANNs). Unlike most previous studies, PDCA puts emphasis on architectural adaptation as well as function level adaptation. The architectural adaptation is done by determining automatically the number of hidden layers in an ANN and of neurons in hidden layers. The function level adaptation, is done by training each hidden neuron with a different training set. PDCA uses a constructive approach to achieve both the architectural as well as function level adaptation. It has been tested on a number of benchmark classification problems in machine learning and ANNs. The experimental results show that PDCA can produce ANNs with good generalization ability in comparison with other algorithms.