ppxscal/embedding_network huggingface dataset:
Semisupervised Regression with Cotraining-Style Algorithms
Authors: Zhi-Hua Zhou, Ming Li
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning has attracted much attention. Previous research on semi-supervised learning mainly focuses on semi-supervised classification. Although regression is almost as important as classification, semi-supervised regression is largely understudied. In particular, although co-training is a main paradigm in semi-supervised learning, few works has been devoted to co-training style semi-supervised regression algorithms. In this paper, a co-training style semi-supervised regression algorithm, i.e. Coreg, is proposed. This algorithm uses two regressors each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean square error over the labeled neighborhood of that example. Analysis and experiments show that Coreg can effectively exploit unlabeled data to improve regression estimates.
Locality of Reference in Hierarchical Database Systems
Authors: J. P. Kearns
Venue: IEEE Transactions on Software Engineering (1983)

Localized information referencing is a long-known and much-exploited facet of program behavior. The existence of such behavior in the data accessing patterns produced by database management systems is not currently supported by empirical results. We present experimental results which demonstrate that in certain environments and under certain important applications, locality of reference is an undeniable characteristic of the information accessing behavior of a hierarchical database management system. Furthermore, database locality of reference is in a sense more regular, predictable, and hence, more exploitable than the localized reference activity found in programs in general. The implications of these results for the performance enhancement and workload characterization of database management systems are discussed.
ProICET -- A Cost-Sensitive System for the Medical Domain
Authors: Rodica Potolea, Camelia Vidrighin, Cristina Savin
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

In recent years, data mining has started to receive increasing interest as a method of complementing domain specific expertise in various spheres of human activity. Apart from data specific issues, a key particularity of many real world problems, such as medical diagnosis, are the costs involved, the most important being the test and the misclassification costs. This paper evaluates ProICET, a new system built around the ICET algorithm. The system has been previously benchmarked on classical medical data sets. Here, we use a real medical dataset to test the current version of our system. The comparative analysis confirms that ProICET is the best at cost minimization out of several successful classifiers, while keeping a good accuracy rate.
Comparison of advanced learning algorithms for short-term load forecasting
Authors: V. S. Kodogiannis
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2000)

Neural Networks are currently finding practical applications, ranging from 'soft' regulatory control in consumer products to accurate modelling of non-linear systems. This paper presents the development of improved neural networks based on short-term electric load forecasting models for the Power System of the Greek Island of Crete. Several approaches including radial basis function networks, dynamic neural networks and fuzzy-neural-type networks have been proposed and discussed in this paper. Their performances are evaluated through a simulation study, using metered data provided by the Greek Public Power Corporation. The results indicate that the load forecasting models developed provide more accurate forecasts compared to the conventional backpropagation network forecasting models. Finally, the embedding of the new models capability in a modular-constructed forecasting system is presented.
Simple off the shelf abstractions for XML schema
Authors: Wim Martens, Frank Neven, Thomas Schwentick
Venue: ACM SIGMOD Record (2007)

Although the advent of XML Schema [25] has rendered DTDs obsolete, research on practical XML optimization is mostly biased towards DTDs and tends to largely ignore XSDs (some notable exceptions non-withstanding). One of the underlying reasons is most probably the perceived simplicity of DTDs versus the alleged impenetrability of XML Schema. Indeed, optimization w.r.t. DTDs has a local flavor and usually reduces to reasoning about the accustomed formalism of regular expressions. XSDs, on the other hand, even when sufficiently stripped down, are related to the less pervious class of unranked regular tree automata [6, 19, 20, 21]. Recent results on the structural expressiveness of XSDs [19], however, show that XSDs are in fact much closer to DTDs than to tree automata, leveraging the possibility to directly extend techniques for DTD-based XML optimization to the realm of XML Schema. The goal of the present paper is to present the results in [19] in an easy and accessible way. At the same time, we discuss possible applications, related research, and future research directions. Throughout the paper, we try to restrict notation to a minimum. We refer to [19] for further details.
SADAT An Automated Testing Tool
Authors: U. Voges
Venue: IEEE Transactions on Software Engineering (1980)

This paper describes the automated testing tool SADAT, which supports the testing of single Fortran modules. The different functions which are integrated in this system are explained, the usage of the tool is demonstrated, and some output results are presented. The special benefits of the SADAT system are summarized. The history and the present status of the system are outlned. Finaly, a listing of further reference material and information on the program availability are included.
An approach to hybrid probabilistic models
Authors: E. Di Tomaso, J. F. Baldwin
Venue: International Journal of Approximate Reasoning (2008)

This paper is concerned with a development of a theory on probabilistic models, and in particular Bayesian networks, when handling continuous variables. While it is possible to deal with continuous variables without discretisation, the simplest approach is to discretise them. A fuzzy partition of continuous domains will be used, which requires an inference procedure able to deal with soft evidence. Soft evidence is a type of uncertain evidence, and it is also a result of the type of discretisation used. An algorithm for inference in multiply connected networks will be proposed and exploited for filtering and abduction in dynamic, time-invariant models, when continuous variables are present.
ENIO: An Enterprise Application Integration Ontology
Authors: Athanasios Bouras, Panagiotis Gouvas, Gregoris Mentzas
Venue: Proceedings of the 18th International Conference on Database and Expert Systems Applications (2007)

If we try to increase the level of automation in Enterprise Application Integration (EAI) scenarios, we confront challenges related to the resolution of data heterogeneities, service discovery and process composition. In this paper, we propose the Enterprise Interoperability Ontology (ENIO) that provides a shared, common understanding of data, services and processes within enterprise application integration scenarios. ENIO consists of an Upper EAI ontology, which is based on the DOLCE-SUMO alignment, with extensions called facets that cover several dimensions of the EAI domain. Each facet contains a relative meta-model that utilizes widely adopted standards
Reliability Modeling of Compensating Module Failures in Majority Voted Redundancy
Authors: D. P. Siewiorek
Venue: IEEE Transactions on Computers (1975)

The classical reliability model for N-modular redundancy (NMR) assumes the network to be failed when a majority of modules which drive the same voter fail. It has long been known that this model is pessimistic since there are instances, termed compensating module failures, where a majority of the modules fail but the network is nonfailed. A different module reliability model based on lead reliability is proposed which has the classical NMR reliability model as a special case. Recent results from the area of test generation are employed to simplify the module reliability calculation under the lead reliability model. First a fault equivalent technique, based on functional equivalence of faults, is developed to determine the effect of compensating module failures on system reliability. This technique can increase the predicted mission time (the time the system is to operate at or above a given reliability) by at least 40 percent over the classical model prediction for simple networks. Since the fault equivalent technique is too complex for modeling of large circuits a second, computational simpler technique, based on fault dominance, is derived. It is then shown to yield results comparable to the fault equivalent technique. A more complex example circuit analyzed by the fault dominance model shows at least a 75 percent improvement in mission time due to modeling compensating module failures. A commercialy available 31 gate integrated circuit chip is also modeled to demonstrate the applicability of the technique to large circuits.
Fast Buffer Insertion for Yield Optimization Under Process Variations
Authors: Ruiming Chen, Hai Zhou
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

With the emerging process variations in fabrication, the traditional corner-based timing optimization techniques become prohibitive. Buffer insertion is a very useful technique for timing optimization. In this paper, we propose a buffer insertion algorithm with the consideration of process variations. We use the solutions from the deterministic buffering that sets all the random variables at their nominal values to guide the statistical buffering algorithm. Our algorithm keeps the solution lists short, and always achieves higher yield than the deterministic buffering. The experimental results demonstrate that the exiting approaches cannot handle large cases efficiently or effectively, while our algorithm handles large cases very efficiently, and improves the yield more than 12% on average.
An error term and uniqueness for Hermite-Birkhoff interpolation involving only function values and/or first derivative values
Authors: W. F. Finden
Venue: Journal of Computational and Applied Mathematics (2008)

This paper discusses various aspects of Hermite-Birkhoff interpolation that involve prescribed values of a function and/or its first derivative. An algorithm is given that finds the unique polynomial satisfying the given conditions if it exists. A mean value type error term is developed which illustrates the ill-conditioning present when trying to find a solution to a problem that is close to a problem that does not have a unique solution. The interpolants we consider and the associated error term may be useful in the development of continuous approximations for ordinary differential equations that allow asymptotically correct defect control. Expressions in the algorithm are also useful in determining whether certain specific types of problems have unique solutions. This is useful, for example, in strategies involving approximations to solutions of boundary value problems by collocation.
A DataMatrix-based Mutant Code Design and Recognition Method Research
Authors: Leng Biao
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

Based on DataMatrix 2D barcode, a DataMatrix mutant barcode which was also a matrix 2D barcode was designed. A distorted image of a mutant barcode was obtained by simulating actual environment. The barcode area in the image was acquired by barcode localizing. After the barcode area being analyzed, these barcode characteristic parameters were abstracted and the distorted image fitting formula was gained by exercise, which set up the correspondence between distorted barcode image point coordinates and the standard one. Thus the 01 code of mutant barcode of the distorted image can be abstracted. The implicit information in the 01 code can be decoded with DataMatrix decode method. From the actual testing result, the barcode recognizing speed is faster than others by this way, and this method was robust.
Improved kernel principal component analysis for fault detection
Authors: Peiling Cui, Junhong Li, Guizeng Wang
Venue: Expert Systems with Applications: An International Journal (2008)

This paper improves kernel principal component analysis (KPCA) for fault detection from two aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KPCA when the number of samples becomes large. Secondly, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of these improvements for fault detection performance in terms of low computational cost and high fault detection rate.
MPEG-2 to H.264 Transcoding for DVB-H Applications
Authors: M. Jiang, D. Crookes
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

The H.264-based video communication systems usually require an adaptive transcoding from MPEG-2 to H.264 for video transmission on the heterogeneous network, such as DVB-H, WiMAX and UMTS channels. In this paper, an adaptive transcoder of MPEG-2 to H.264 was implemented for different DVB-H capability classes.
Emergency Evacuation using Wireless Sensor Networks
Authors: Matthew Barnes, Hugh Leather, D. K. Arvind
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

This paper presents a distributed algorithm to direct evacuees to exits through arbitrarily complex building layouts in emergency situations. The algorithm finds the safest paths for evacuees taking into account predictions of the relative movements of hazards, such as fires, and evacuees. The algorithm is demonstrated on a 64 node wireless sensor network test platform and in simulation. The results of simulations are shown to demonstrate the navigation paths found by the algorithm.
Kensho: a dynamic tasking architecture for sensor networks
Authors: James Horey, Arthur B. Maccabe, Angela Mielke
Venue: ACM SIGBED Review (2007)

Recent research on sensor network programming architectures has managed to alleviate many common programming burdens, but has not fully addressed the problems associated with tasking and deployment. This has impeded the development of new multi-purpose sensor networks that require new software abstractions and mechanisms. We present the design of a new software architecture for sensor networks that provides abstractions to explicitly address the issues of tasking and deployment. We also present an initial implementation of our architecture in simulation to investigate the potential memory overheads. Finally, we present a mobile tracking application to demonstrate how to use the abstractions provided by our architecture.
Knowledge work artifacts: kernel cousins for free/open source software development
Authors: Margaret Elliott, Mark S. Ackerman, Walt Scacchi
Venue: Proceedings of the 2007 international ACM conference on Supporting group work (2007)

Most empirical studies of peer production have focused on the final products of these efforts (such as software in Free/Open Source projects), but there are also many other knowledge artifacts that improve the effectiveness of the project. This paper presents a study of an intermediate work product, or informalism, used in a Free/Open Source Software project, GNUe. A digest-like artifact called the Kernel Cousin (KC) was used extensively in the project. These KCs allowed critical coordination and memory, but at the cost of considerable effort. The paper presents two examples of the KCs' use in the project as well as an analysis of their benefits and costs.
Fountain reprogramming protocol (FRP): a reliable data dissemination scheme for wireless sensor networks using fountain codes
Authors: Riccardo Crepaldi, Albert Harris,  III, Michele Rossi, Giovanni Zanca, Michele Zorzi
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

Wireless sensor network technologies enable a wide variety of applications (e.g., environmental monitoring). Such sensor networks are often deployed in regions that make it difficult to collect and redistribute the nodes for maintenance. However, there is often a need to reprogram all of the nodes in the network, either during application test phases on deployed networks, or to support software upgrades. Therefore, a reliable method of sending a relatively large amount of data to each node in the network is required to support these functions.
Tardiness Bounds for EDF Scheduling on Multi-Speed Multicore Platforms
Authors: Hennadiy Leontyev, James H. Anderson
Venue: Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2007)

Multicore platforms, which include several processing cores on a single chip, are being widely touted as a solution to heat and energy problems that are impediments to single-core chip designs. To accommodate both parallelizable and inherently-sequential applications on the same platform, heterogeneous multicore designs with faster and slower cores have been proposed. In this paper, we consider the problem of scheduling soft real-time workloads on such a platform.
MTCLAB: A MATLAB®-based program for traveltime quality analysis and pre-inversion velocity tuning in 2D transmission tomography
Authors: J. L. Fernández-Martínez, J. P. Fernández-Alvarez, L. M. Pedruelo-González
Venue: Computers Geosciences (2008)

A MATLAB^(R)-based computer code that analyses the traveltime distribution and performs quality analysis at the pre-inversion stage for 2D transmission experiments is presented. The core tools of this approach are the so-called mean traveltime curves. For any general recording geometry, the user may select any pair of subsets of contiguous sources and receivers. The portion of the domain swept by the implied rays defines a zone of analysis, and for each source (receiver) the outcoming (incoming) ray fan is named a source (receiver) gather. The empirical mean traveltime curves are constructed, for each zone, by assigning the average and the standard deviation of the traveltimes in the gathers to the positions of the sources (receivers). The theoretical expressions assume isotropic homogeneous velocity inside each zone. The empirical counterparts use the observed traveltimes and make no assumptions. Isotropic velocity in each zone is inferred by least-squares fitting of the empirical mean traveltime curves. The user may refine the analysis considering different zones (multi-zone analysis). Initially the whole domain is modelled as a single zone. The procedure compares empirical versus theoretical curves. In addition, residuals can be plotted using source-receiver positions as plane coordinates. The results are used to unravel the possible presence of anomalous gathers, heterogeneities, anisotropies, etc. Depending on the kind of anomalies, velocity estimation and mean time residuals are different in the source and receiver gather curves. This software helps to grasp a better understanding of the data variability before the inversion and provides to the geophysicist an approximate zonal isotropic model and a range of velocity variation that can be used in the inverse problem as a priori information (regularization term). Its use is described through tutorial examples. A guided user interface leads the user through the algorithm steps.
Building fuzzy graphs: Features and taxonomy of learning for non-grid-oriented fuzzy rule-based systems
Authors: Rafael Alcalá, Jorge Casillas, Oscar Cordón, Francisco Herrera
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2001)

The use of Mamdani-type fuzzy rule-based systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired. This fact relates to the restriction imposed when using linguistic variables, which forces the membership functions considered in each fuzzy linguistic rule to belong to a common set of them, i.e., to use a global grid. To solve this problem, in the last few years a new variant has been proposed working directly with fuzzy variables in the fuzzy rules instead of linguistic terms, thus ignoring the said restriction. Therefore, these systems, which are totally equivalent to fuzzy graphs (defined by Zadeh as granular representations of functional dependencies and relations), do not consider a global grid and could be named {\it non-grid-oriented} (NGO) FRBSs. Of course, the main objective of these models is the accuracy of the system instead its interpretability. Until now, NGO FRBSs have been little considered and developed in the literature. However, and due to their good accuracy, their use is increasing thus making necessary a wide analysis on the features and associated learning methods in the NGO domain. This contribution aims at analyzing the structure and framework of NGO FRBSs, as well as making a taxonomy of learning methods considering the constrains imposed on the fuzzy sets in the generation process. Some automatic learning techniques and methods proposed in the literature to build these fuzzy graphs will be also reviewed and analyzed when solving several applications of different nature.
An Approximate Reasoning Model for Situation and Threat Assessment
Authors: Yawei Liang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

This paper examines the use of a fuzzy knowledge- based system (KBS) in assisting naval operators in managing the situation and threat assessment problem in a littoral environment. Under the scenario created involving air contacts in a littoral environment, the assessment of the KBS model is conducted and the results showed that the correlation between assessment and human operation officers' judgment was high. The fuzzy KBS with its corresponding toolkits is much more flexible to be integrated with other components of a command and control system, while the fine-tuning of membership functions and fuzzy inference rules are intensive; this makes any further modification a non- trivial task.
Single-machine scheduling with deteriorating jobs under a series-parallel graph constraint
Authors: Ji-Bo Wang, C. T. Ng, T. C. E. Cheng
Venue: Computers and Operations Research (2008)

This paper considers single-machine scheduling problems with deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting times. In addition, the jobs are related by a series-parallel graph. It is shown that for the general linear problem to minimize the makespan, polynomial algorithms exist. It is also shown that for the proportional linear problem of minimization of the total weighted completion time, polynomial algorithms exist, too.
Free algebras in varieties of Stonean residuated lattices
Authors: Roberto Cignoli
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2007)

Given a variety  of bounded residuated lattices satisfying the Stone identity &not;x&or;&not;&not;x = T, the free algebras in  over a set X of cardinality |X| are represented as weak Boolean products over the Cantor space 2|X| of a family of free algebras in an associated variety of (not necessarily bounded) residuated lattices with a bottom added.