ppxscal/embedding_network huggingface dataset:
Vertical mining with incomplete data
Authors: Faris Alqadah, Zhen Hu, Lawrence J. Mazlack
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

Mining frequent patterns is essential in many data mining methods. Frequent patterns lead to the discovery of association rules, strong rules, sequential episodes, and multi-dimensional patterns. Patterns should be discovered in a time and space efficient manner. Vertical mining algorithms key advantage is that they can outperform their horizontal counterparts in terms of both time and space efficiency. Little work has addressed how incomplete data influences vertical data mining. Therefore, the quality and utility of vertical mining algorithms results remains uncertain as real data sets often contain incomplete data. This paper considers establishing methodologies that deal with incomplete data in vertical mining.
Concurrent number cruncher: a GPU implementation of a general sparse linear solver
Authors: Luc Buatois, Guillaume Caumon, Bruno Levy
Venue: International Journal of Parallel, Emergent and Distributed Systems (2009)

A wide class of numerical methods needs to solve a linear system, where the matrix pattern of non-zero coefficients can be arbitrary. These problems can greatly benefit from highly multithreaded computational power and large memory bandwidth available on graphics processor units (GPUs), especially since dedicated general purpose APIs such as close-to-metal (CTM) (AMD-ATI) and compute unified device architecture (CUDA) (NVIDIA) have appeared. CUDA even provides a BLAS implementation, but only for dense matrices (CuBLAS). Other existing linear solvers for the GPU are also limited by their internal matrix representation. This paper describes how to combine recent GPU programming techniques and new GPU dedicated APIs with high performance computing strategies (namely block compressed row storage (BCRS), register blocking and vectorization), to implement a sparse general-purpose linear solver. Our implementation of the Jacobi-preconditioned conjugate gradient algorithm outperforms by up to a factor of 6.0 × leading-edge CPU counterparts, making it attractive for applications which are content with single precision.
Automating the complete sample management in a biotech laboratory with a mobile manipulator: a real world application for service robotics
Authors: Martin Wojtczyk, Alois Knoll, Rüdiger Heidemann, Klaus Joeris, Chun Zhang, Mark Burnett, Tom Monica
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

Both Robots and Personal Computers established new markets about 30 years ago and were enabling factors in Automation and Information Technology. However, while you can see Personal Computers in almost every home nowadays, the domain of Robots in general still is mostly restricted to industrial automation. Due to the physical impact of robots, a safe design is essential, which most robots still lack of and therefore prevent their application for personal use, although a slow change can be noticed by the introduction of dedicated robots for specific tasks, which can be classified as service robots. Our approach to service robots was driven by the idea for supporting lab personnel in a biotechnology laboratory. That resulted in the combination of a manipulator with a mobile platform, extended with the necessary sensors to carry out a complete sample management process in a mammalian cell culture plant. After the initial development in Germany, the mobile manipulator was shipped to Bayer HealthCare in Berkeley, CA, USA, a global player in the sector of biopharmaceutical products, located in the San Francisco bay area. The platform was installed and successfully tested there in a pilot plant. This project demonstrates the successful combination of both key technologies: Information Technology and Robotics - and its application in a Life Science pilot plant.
Automatic segmentation of magnetic resonance images of the brain
Authors: John Tyler
Venue:  (2005)

Magnetic resonance imaging (MRI) is a technique used primarily in medical settings to produce high quality images of the human body's internal anatomy. Each image is of a thin slice through the body, with the typical distance between slices being a few millimeters. Brain segmentation is the delineation of one or more anatomical structures within images of the brain. It promotes greater understanding of spatial relationships to aid in such tasks as surgical planning and clinical diagnoses, particularly when the segmented outlines from each image slice are displayed together as a surface in three-dimensions. A review of the literature indicates that current brain segmentation methods require a trained human expert to inspect the images and decide appropriate parameters, thresholds, or regions of interest to achieve the proper segmentation. This is a tedious time-consuming task because of the large number of images involved. A truly automatic method is needed to transform brain segmentation into a practical clinical tool. This dissertation describes a novel pattern classification approach to the problem of automatically segmenting magnetic resonance images of the brain. Based on this approach, algorithms were designed and implemented to automatically segment a number of anatomical structures. These algorithms were applied to several standard image data sets of human subjects obtained from the Internet Brain Segmentation Repository (IBSR). The resulting segmentations of the lateral ventricles and the caudate nuclei were compared to reference manual segmentations done by expert radiologists. The Tanimoto similarity coefficient was very good for the lateral ventricles (0.81) and good for the caudate nuclei (0.67).
The invariant set theory of multiple valued iterative dynamical systems
Authors: Byungik Kahng
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

Invariant set theory is an important tool in the control theory. It has rich history that goes back over a century, yet it is still an active research topic both in pure mathematics and theoretical engineering. It is easy to reduce a traditional discrete-time control dynamical system to an iterative dynamics of one endomorphism in the phase space. It is not easy to do the same in the presence of the disturbance. The purpose of this paper is to show that we can overcome this difficulty using the dynamics of multiple valued maps. As an application, we study the reachability problem of the maximal positively invariant set, using the multiple valued dynamics.
Optimizing user QoE through overlay routing, bandwidth management and dynamic transcoding
Authors: Maarten Wijnants, Wim Lamotte, Bart De Vleeschauwer, Filip De Turck, Bart Dhoedt, Piet Demeester, Peter Lambert, Dieter Van de Walle, Jan De Cock, Stijn Notebaert, Rik Van de Walle
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

More and more, multimedia services are being accessed via fixed and mobile networks. These services are typically much more sensitive to packet loss, delay and/or congestion than traditional services. In particular, multimedia data is often time critical and, as a result, network issues are not well tolerated and significantly deteriorate the user’s Quality of Experience (QoE). We therefore propose a QoE optimization platform that is able to mitigate problems that might occur at any location in the delivery path from service provider to customer. More specifically, the distributed architecture supports overlay routing to circumvent erratic parts of the network core. In addition, it comprises proxy components that realize last mile optimization through automatic bandwidth management and the application of processing on multimedia flows. In this paper we introduce a transcoding service for this proxy component which enables the transformation of H.264/AVC video flows to an arbitrary bit rate. Through representative experimental results, we illustrate how this addition enhances the QoE optimization capabilities of the proposed platform by allowing the proxy component to compute more flexible and effective bandwidth distributions.
Robust sequence memory in sparsely-connected networks with controllable steady-state period
Authors: Min Xia, Jian'an Fang, Feng Pan, En'jian Bai
Venue: Neurocomputing (2009)

A novel sparsely-connected neural network for sequence memory with controllable steady-state period is proposed in this study. By introducing a new exponential kernel sampling function and the sampling interval parameter, the steady-state period can be controlled, and the steady-state time steps is equal to the sampling interval parameter. Ascribing to the exponential kernel sampling function, the sequence storage capacity is enlarged compared with the existing sequence memory models. Owning to the sparsely-connected of Gaussian distribution, the model produces the efficient use of synapse resources, but the sequence storage capacity is decreased compared with the fully-connected networks. The study also gives a significant result that the networks of different dimensions have the same synapse connection efficiency if they are with the same connection mean degree.
Operating system enhancements for data-intensive server systems
Authors: Kai Shen
Venue:  (2008)

Recent studies on operating system support for concurrent server systems mostly target CPU-intensive workloads with light disk I/O activities. However, an important class of server systems that access a large amount of disk-resident data, such as the index searching server of large-scale Web search engines, has received limited attention. In this thesis work, we examine operating system techniques to improve the performance of data-intensive server systems under concurrent execution. We propose OS enhancements in three aspects of the operating system: file system prefetching, memory management, and disk I/O system. First, we propose a competitive prefetching strategy that can balance the overhead of disk I/O switching and the wasted I/O bandwidth on prefetching unnecessary data. Second, we explore a new memory management scheme for prefetched data, in order to reduce prefetching-induced page thrashing at high execution concurrency. Third, we present five performance anomalies and solutions we identified in the Linux I/O system, and provide general discussions on operating system performance debugging. We have implemented the proposed techniques in the Linux 2.6 kernel. Performance evaluation on microbenchmarks and real applications shows promising results.
Letters: Ensemble of online sequential extreme learning machine
Authors: Yuan Lan, Yeng Chai Soh, Guang-Bin Huang
Venue: Neurocomputing (2009)

Liang et al. [A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006), 1411-1423] has proposed an online sequential learning algorithm called online sequential extreme learning machine (OS-ELM), which can learn the data one-by-one or chunk-by-chunk with fixed or varying chunk size. It has been shown [Liang et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks 17 (6) (2006) 1411-1423] that OS-ELM runs much faster and provides better generalization performance than other popular sequential learning algorithms. However, we find that the stability of OS-ELM can be further improved. In this paper, we propose an ensemble of online sequential extreme learning machine (EOS-ELM) based on OS-ELM. The results show that EOS-ELM is more stable and accurate than the original OS-ELM.
Easy Mac OS X Leopard
Authors: Kate Binder
Venue:  (2008)

See it done. Do it yourself. Its that Easy! Easy Mac OS X Leopard teaches you the fundamentals of working with the Mac operating system. Fully illustrated steps with simple instructions guide you through each task, building the skills you need to master Mac OS X Leopard with ease. No need to feel intimidated, well hold your hand every step of the way. Learn how to: Save, open, and print your documents Use Spotlight to instantly locate any file Network multiple Macs and share your Internet connection Back up your important data automatically Buy music online Use iPhoto to take control of your photo collection Share calendars with coworkers, family, and friends Keep your Mac running smoothly Easy steps guide you through each task. Items you select or click are shown in bold. Each step is fully illustrated. Introduction xii Chapter 1: Getting Started 2 Chapter 2: Working with Disks, Folders, and Files 20 Chapter 3: Installing and Using Applications 56 Chapter 4: Setting System Preferences 76 Chapter 5: Customizing Your Mac 90 Chapter 6: Organizing Your Life 108 Chapter 7: Printing, Faxing, and Scanning 132 Chapter 8: Getting Online 146 Chapter 9: Living Online 166 Chapter 10: Getting an iLife 202 Chapter 11: Sharing Your Mac with Multiple Users 226 Chapter 12: Creating a Home Network 238 Chapter 13: Maintaining Your Mac 250 Glossary 264 Index 274 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
Knowledge and object phylogenetic production cascades --the TELOS case
Authors: Ioan Rosca
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

In the unitary design of systems involving people, objects, processes and concepts, the engineering acquires a hybrid character, requiring the coagulation between the contributions based on diverse expertises and posing coordination and communication problems inside mixed design teams. The LORNET project federates research institutions from Canada for the construction of a “middleware” supporting technical and semantic inter-operation between campuses and repositories distributed on the Internet. It is a double challenge: an effort of concurrent research for instrumenting the instructional concurrent engineering. Observing the circle between “doing by learning” and “learning by doing”, activity coordination systems can be enriched with a “semantic layer” (facilitating the procedures execution by alleviating their comprehension and learning). Reciprocally, the production and management of distributed pedagogical activities require collaborative procedure modelling and orchestration. This paper is addressed to those seeking different perspectives in dealing with complexity. I summarize it as follows: mixing the management of persons, objects, processes and knowledge; using the procedures' models for their orchestration; approaching a “4d” vision for extending the observation of short process (“ontogenetic” and “physiological”) to those of long-term evolutions; unitary management of phylogenetic cascades-reproducing structures and processes-based on metafunctions.
A Time-Series Representation for Temporal Web Mining Using a Data Band Approach
Authors: Mireille Samia, Stefan Conrad
Venue: Proceeding of the 2007 conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS'2006 (2007)

The analysis of events ordered over time and the discovery of significant hidden relationships from this temporal data is becoming the concern of the information society. Using temporal data as temporal sequences without any preprocessing fails to find key features of these data. Therefore, before applying mining techniques, an appropriate representation of temporal sequences is needed. Our representation of time series can be used in different fields, such as aviation science and earth science, and can also be applied to, for instance, Temporal Web Mining (TWM) [1], [2], [3], [4]. Our representation of time series aims at improving the possibility of specifying and finding an important occurrence. In our new concept, we use data band ranges and areas in order to determine the importance or the weight of a segment. According to the closeness of a segment to a data band range, this representation of time series can help to find a significant event. This paper focuses on our representation of time series.
On the efficiency of cell-like and tissue-like recognizing membrane systems
Authors: Miguel A. Gutiérrez-Naranjo, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Francisco J. Romero-Campero
Venue: International Journal of Intelligent Systems (2009)

Cell-like recognizing membrane systems are computational devices in the framework of membrane computing inspired from the structure of living cells, where biological membranes are arranged hierarchically. In this paper tissue-like recognizing membrane systems are presented. The idea is to consider that membranes are placed in the nodes of a graph, mimicking the cell intercommunication in tissues. In this context, polynomial complexity classes associated with recognizing membrane systems can be defined. We recall the definition for cell-like systems, and we introduce the corresponding complexity classes for the tissue-like case. Moreover, in this paper two efficient solutions to the satisfiability problem are analyzed and compared from a complexity point of view. &copy; 2009 Wiley Periodicals, Inc.
Formalising the 'No Information without Data-representation' Principle
Authors: Patrick Allo
Venue: Proceeding of the 2008 conference on Current Issues in Computing and Philosophy (2008)

One of the basic principles of the general definition of information is its rejection of dataless information. In general, it is implied that “there can be no information without physical implementation” [1]. Though this is usually considered a commonsensical assumption, many questions arise with regard to its general application. In this paper, a combined logic for data and information is elaborated, and specifically used to investigate the consequences of restricted and unrestricted data-implementation principles.
A Fast Track To Structured Finance Modeling, Monitoring and Valuation: Jump Start VBA
Authors: William Preinitz
Venue:  (2009)

This book is designed to start with simple examples that progressively develop the reader's confidence to take on more complex tasks. There is very little theoretical discussion about computer science, operations research algorithms, mathematics, or finance. The thrust of the book is to teach the reader to break complex tasks down into simple tasks. It then looks to implement those simple tasks into VBA code using a critical subset of the features of the language. The tentative contents is: (1) Why? What? Who? Where? and How? (2) Common Sense (3) Securitizing A Loan Portfolio (4) Understanding the Excel Waterfall (5) Designing the VBA Model (6) Laying the Model Groundwork (7) Recorded Macros: A First Look at the VBA Language (8) Writing Menus: An Introduction to Data, Ranges, Arrays, and Objects (9) Controlling the Flow of the Model (10) Building Messaging Capabilities (11) Designing the Models Reports (12) Main Program and Menus (13) Writing the Collateral Selection Code (14) Calculating the Cash Flows (15) Running the Waterfall: Producing Initial Results (16) Debugging the Model (17) Validating the Model (18) Running the Model (19) Building Additional Capabilities (20) Documentation of the Model (21) Managing the Growth of the Model (22) Building Portfolio Monitoring Model (23) Valuation Techniques: How do we Determine Price? (24) Challenging Times For the Deal (25) Parting Admonitions
On Parallel Thinning Algorithms: Minimal Non-simple Sets, P-simple Points and Critical Kernels
Authors: Gilles Bertrand, Michel Couprie
Venue: Journal of Mathematical Imaging and Vision (2009)

Critical kernels constitute a general framework in the category of abstract complexes for the study of parallel homotopic thinning in any dimension. In this article, we present new results linking critical kernels to minimal non-simple sets (MNS) and P-simple points, which are notions conceived to study parallel thinning in discrete grids. We show that these two previously introduced notions can be retrieved, better understood and enriched in the framework of critical kernels. In particular, we propose new characterizations which hold in dimensions 2, 3 and 4, and which lead to efficient algorithms for detecting P-simple points and minimal non-simple sets.
Optimal preventive maintenance policy for leased equipment using failure rate reduction
Authors: Ruey Huei Yeh, Kow-Chin Kao, Wen Liang Chang
Venue: Computers and Industrial Engineering (2009)

This study proposes a maintenance scheme for leased equipment using failure rate reduction method and derives an optimal preventive maintenance (PM) policy that minimizes expected total cost. Under the proposed maintenance scheme, the lessor (equipment owner) rectifies failures with minimal repairs within the lease period, and the lessor may incur a penalty when repair time exceeds a time limit as specified in the lease contract. To reduce the expected total cost, the lessor may employ PM actions to decrease the number of possible failures. In this study, an efficient algorithm is developed to derive the optimal PM policy and a closed-form solution is obtained for the case where the lifetime distribution of the equipment is Weibull. The expected total cost using the optimal PM policy under the proposed maintenance scheme is then compared with the performance of other policies under various maintenance schemes through numerical examples.
Characterization and evaluation of similarity measures for pairs of clusterings
Authors: Darius Pfitzner, Richard Leibbrandt, David Powers
Venue: Knowledge and Information Systems (2009)

In evaluating the results of cluster analysis, it is common practice to make use of a number of fixed heuristics rather than to compare a data clustering directly against an empirically derived standard, such as a clustering empirically obtained from human informants. Given the dearth of research into techniques to express the similarity between clusterings, there is broad scope for fundamental research in this area. In defining the comparative problem, we identify two types of worst-case matches between pairs of clusterings, characterised as independently codistributed clustering pairs and conjugate partition pairs. Desirable behaviour for a similarity measure in either of the two worst cases is discussed, giving rise to five test scenarios in which characteristics of one of a pair of clusterings was manipulated in order to compare and contrast the behaviour of different clustering similarity measures. This comparison is carried out for previously-proposed clustering similarity measures, as well as a number of established similarity measures that have not previously been applied to clustering comparison. We introduce a paradigm apparatus for the evaluation of clustering comparison techniques and distinguish between the goodness of clusterings and the similarity of clusterings by clarifying the degree to which different measures confuse the two. Accompanying this is the proposal of a novel clustering similarity measure, the Measure of Concordance (MoC). We show that only MoC, Powers&#x2019;s measure, Lopez and Rajski&#x2019;s measure and various forms of Normalised Mutual Information exhibit the desired behaviour under each of the test scenarios.