ppxscal/embedding_network huggingface dataset:
A New Algorithm for Image Resizing Based on Bivariate Rational Interpolation
Authors: Shanshan Gao, Caiming Zhang, Yunfeng Zhang
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

A new method for image resizing by bivariate rational interpolation based on function values and partial derivative value is presented. When an original image is resized in an arbitrary ratio, the first step of the method is constructing the rational interpolation fitting the original surface where the given image data points are sampled from. The resized image can be obtained just by re-sampling on the interpolation surface. The algorithm presents how to estimate the partial derivative value of image data point needed for rational interpolation, and at same time considers the adjustment of tangent vector of the edge point to keep edges well defined. Various experiments are presented to show efficiency of the proposed method and that the resized images can preserve clear and sharp borders and hence offer more detail information in real application.
Grammatical inference for robotic self-assembly: basic methodology
Authors: Aboubekeur Hamdi-Cherif, Chafia Kara-Mohammed
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

Two apparently distinct scientific communities might be unified in a common scientific framework, resulting in tangible and mutual benefits. We mean formal languages theory and robot control communities. Blending research from both fields results in the appearance of a richer community, as has been proven by the emergence of many multidisciplinary fields such as bioinformatics and algebraic biology, to cite but two of the contiguous fields. Specifically, the present research work intends to evaluate the effectiveness and usefulness of grammatical inference (GI) in the control of robots for self-assembly purposes. Can we integrate the diversified works dealing with robotic self-assembly while concentrating on graph grammars as an alternative control methodology? Our aim is to answer positively this central question. As far as this paper is concerned, we only give the broad methodological lines of the research while stressing the integration of these different approaches into a single entity.
Time-frequency analysis based state diagnosis of transformers' windings under the short-circuit shock
Authors: Yuying Shao, Zhushi Rao, Zhijian Jin
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

How to effectively identify the looseness or deformation of windings is important to keep the safety of transformers' operation. The short-circuit experiments have been done on the large power transformer of 50000KVA. The vibration intensity change is used to initially detect the windings' state by online monitoring the vibration signal of the shocks. Then the time-frequency analysis further processes these signals in order to recognize the fault information contained. This study finds that the time-frequency analysis can be used to more precisely detect the fault characteristics of windings. Compared with traditional off line short circuit reactance method, the time-frequency analysis is more effective.
Scientific Data Mining: A Practical Perspective
Authors: Chandrika Kamath
Venue:  (2009)

Technological advances are enabling scientists to collect vast amounts of data in fields such as medicine, remote sensing, astronomy, and high-energy physics. These data arise not only from experiments and observations, but also from computer simulations of complex phenomena. They are often complex, with both spatial and temporal components. As a result, it has become impractical to manually explore, analyze, and understand the data. Scientific Data Mining: A Practical Perspective describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, this book identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest;extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists. A majority of the book describes how techniques from disciplines such as image and video processing, statistics, machine learning, pattern recognition, and mathematical optimization can be used for the tasks in each step. It also includes a description of software systems developed for scientific data mining; general guidelines for getting started on the analysis of massive, complex data sets; and an extensive bibliography. Audience: This book is intended for data mining practitioners and scientists interested in applying data mining techniques to their data sets. It is also appropriate for advanced undergraduate and graduate-level courses on data analysis offered in mathematics, computer science, and statistics departments. Contents: Preface; Chapter 1: Introduction; Chapter 2: Data Mining in Science and Engineering; Chapter 3: Common Themes in Mining Scientific Data; Chapter 4: The Scientific Data Mining Process; Chapter 5: Reducing the Size of the Data; Chapter 6: Fusing Different Data Modalities; Chapter 7: Enhancing Image Data; Chapter 8: Finding Objects in the Data; Chapter 9: Extracting Features Describing the Objects; Chapter 10: Reducing the Dimension of the Data; Chapter 11: Finding Patterns in the Data; Chapter 12: Visualizing the Data and Validating the Results; Chapter 13: Scientific Data Mining Systems; Chapter 14: Lessons Learned, Challenges, and Opportunities; Bibliography; Index
Improving the performance and precision of bioinformatic algorithms
Authors: Chau-Wen Tseng
Venue:  (2008)

Recent advances in biotechnology have enabled scientists to generate and collect huge amounts of biological experimental data. Software tools for analyzing both genomic (DNA) and proteomic (protein) data with high speed and accuracy have thus become very important in modern biological research. This thesis presents several techniques for improving the performance and precision of bioinformatics algorithms used by biologists.Improvements in both the speed and cost of automated DNA sequencers have allowed scientists to sequence the DNA of an increasing number of organisms. One way biologists can take advantage of this genomic DNA data is to use it in conjunction with expressed sequence tag (EST) and cDNA sequences to find genes and their splice sites. This thesis describes ESTmapper, a tool designed to use an eager write-only top-down (WOTD) suffix tree to efficiently align DNA sequences against known genomes. Experimental results show that ESTmapper can be much faster than previous techniques for aligning and clustering DNA sequences, and produces alignments of comparable or better quality.Peptide identification by tandem mass spectrometry (MS/MS) is becoming the dominant high-throughput proteomics workflow for protein characterization in complex samples. Biologists currently rely on protein database search engines to identify peptides producing experimentally observed mass spectra. This thesis describes two approaches for improving peptide identification precision using statistical machine learning.HMMatch (HMM MS/MS Match) is a hidden Markov model approach to spectral matching, in which many examples of a peptide fragmentation spectrum are summarized in a generative probabilistic model that captures the consensus and variation of each peak&rsquo;s intensity. Experimental results show that HMMatch can identify many peptides missed by traditional spectral matching and search engines.PepArML (Peptide Identification Arbiter by Machine Learning) is a machine learning based framework for improving the precision of peptide identification. It uses classification algorithms to effectively utilize spectra features and scores from multiple search engines in a single model-free framework that can be trained in an unsupervised manner. Experimental results show that PepArML can improve the sensitivity of peptide identification for several synthetic protein mixtures compared with individual search engines.
Deterministic Computation of Pseudorandomness in Sequences of Cryptographic Application
Authors: A. Fúster-Sabater, P. Caballero-Gil, O. Delgado-Mohatar
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

An easy method of checking balancedness degree as well as run quantification in sequences obtained from LFSR-based keystream generators has been developed. The procedure is a deterministic alternative to the traditional application of statistical tests. The computation method allows one to check deviation of balancedness and run distribution goodness from standard values. The method here developed can be considered as a first selective criterium for acceptance/rejection of this type of generators of cryptographic application.
Information intelligence in cloud computing: how can Vijjana, a collaborative, self-organizing, domain centric knowledge network model help
Authors: Asesh Das, Ramana Y. V. Reddy, Luyi Wang, Sumitra Reddy
Venue: Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies (2009)

This research reports agent activities in a collaborative, self-organizing, domain centric knowledge network, called Vijjana. Vijjana means classified knowledge in Sanskrit language. This model has been designed and implemented to assist users engaged in web activities, limiting the data retrieval to desired material only. The model Vijjana sits on the top layer of the cloud computing architecture, and with its agent activities develops a body of intelligent reasoning system which can semantically connect different dispersed users with their own knowledge nets. Vijjana model makes the cloud computing intelligibly usable for connecting different people working with the same domain specific interest. This provides a high order of accountability with assurance on confidentiality, integrity and availability.
A multi-level pressure-sensing two-handed interface with finger-mounted pressure sensors
Authors: Masaki Omata, Manabu Kajino, Atsumi Imamiya
Venue: Proceedings of Graphics Interface 2009 (2009)

This paper proposes separating a pressure sensor off from an input device and attaching it directly onto a user's finger to allow the user to input pressure values into a computer with various devices and various places. This proposal solves the problem of requiring an individual pressure sensor for each pressure-sensing input device because we've attached a sensor to not a device which is pushed but rather a finger which pushes it. As an instance, we developed a multi-level pressure-sensing two-handed user interface by measuring the positions and pressure values of both the user's hands. The user can manipulate a screen object with the dominant hand and assist it by adjusting the position and the intensity of pressure of the dominant hand and non-dominant hand. We developed some GUI functions: cursor aura for expanding the sphere of its influence, non-dominant hand cursor for picking up a hidden window, and pressure-sensing keyboard input to add arousal to text. The advantages of our system are; (1) a user can use a favorite device and add pressure value, and (2) a user can enter a multi-level value by pressing heavily or lightly without looking at user's hands.
Aggregation in hub location problems
Authors: Elena O. Gavriliouk
Venue: Computers and Operations Research (2009)

Because of their widespread use in real-world transportation situations, hub location models have been extensively studied in the last two decades. Many types of hub location problems are NP-hard and remain unmanageable when the number of nodes exceeds 200. We present a way to tackle large-sized problems using aggregation, explore the resulting error, and show how to reduce it. Furthermore, we develop a heuristic based on aggregation for k-hub center problems and present computational results.
Knowledge-Rich Data Mining in Financial Risk Detection
Authors: Yi Peng, Gang Kou, Yong Shi
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Financial risks refer to risks associated with financing, such as credit risk, business risk, debt risk and insurance risk, and these risks may put firms in distress. Early detection of financial risks can help credit grantors to reduce risk and losses, establish appropriate policies for different credit products and increase revenue. As the size of financial databases increases, large-scale data mining techniques that can process and analyze massive amounts of electronic data in a timely manner become a key component of many financial risk detection strategies and continue to be a subject of active research. However, the knowledge gap between the results data mining methods can provide and actions can be taken based on them remains large in financial risk detection. The goal of this research is to bring the concept of chance discovery into financial risk detection to build the knowledge-rich data mining process and therefore increase the usefulness of data mining results in financial risk detection. Using six financial risk related datasets, this research illustrates that the combination of data mining techniques and chance discovery can provide knowledge-rich data mining results to decision makers; promote the awareness of previously unnoticed chances; and increase the actionability of data mining results.
Development of a miniature pin-array tactile module using elastic and electromagnetic force for mobile devices
Authors: Tae-Heon Yang, Sang-Youn Kim, Chong Hui Kim, Dong-Soo Kwon, Wayne J. Book
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

In these days, tactile sensation using a vibration motor is recently receiving attention for immersive interaction with mobile devices. However, the vibration motor is not enough to provide detailed texture or small-scale shape. In order to generate various and exiting tactile sensation, this paper presents a new tactile actuator with a solenoid, a permanent magnet and an elastic spring. This paper also proposes a miniature tactile module with the proposed actuators. On constructing a miniature tactile module, we separate the elastic springs in the actuators into several layers to minimize the contactor's gap without decreasing the performance of the tactile module. We conduct experiments to investigate each contactor's output force and the frequency response of the proposed tactile module. Each contactor can generate enough output force to stimulate human's mechanoreceptors. Moreover, since the contactors are actuated in a wide range of frequency, the proposed tactile module can generate various tactile sensations.
Robust Airborne 3D Visual Simultaneous Localization and Mapping with Observability and Consistency Analysis
Authors: Abdelkrim Nemra, Nabil Aouf
Venue: Journal of Intelligent and Robotic Systems (2009)

This paper aims to present a robust airborne 3D Visual Simultaneous Localization and Mapping (VSLAM) solution based on a stereovision system. We propose three innovative contributions to the Airborne VSLAM. The first one is the development of an alternative data fusion nonlinear H∞ filtering scheme. This scheme is based on 3D vision observation model and avoids issues linked with the classical Extended Kalman Filtering (EKF) techniques such as the linearization errors, the initialization problem and noise statistics assumptions. The second contribution consists of a consistency and observability analysis for the Airborne VSLAM. The third contribution is a new approach to map management, based on the k-nearest landmark concept, and allowing efficient loop closure detection and map building. This approach reduces considerably the complexity of our Airborne VSLAM algorithm, which becomes independent of the map landmark number. Simulation results show the efficiency of the proposed Airborne VSLAM solution for which comparisons with other techniques are favourable.
Plenary lecture 1: stability analyses of a system of nonlinear partial differential equations and applications
Authors: Daniel N. Riahi
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

In this lecture we first review the stability analyses that have been carried out in the past for systems of nonlinear partial differential equations (PDEs) with important applications in fluid mechanics. Next, we consider a relevant nonlinear stability system of three-dimensional and time dependent nonlinear PDEs and their associated boundary conditions with applications for convective flows. Under certain assumptions, scaling and conditions, we will derive a mathematical model for the nonlinear stability system. We then determine some class of nonlinear steady and unsteady solutions of the resulting system. We explain the mathematical and nonlinear properties of these solutions. We also analyze the stability of these solutions and discuss the corresponding results for the stable solutions and their applications.
Representing publication and distribution practices for scholarly materials: a cross-disciplinary comparison
Authors: Phillip M. Edwards
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

This poster presents a pluralistic approach for representing discipline-specific, cross-disciplinary, and discipline-independent work practices related to scholarly communication. This approach has been applied to qualitative analysis from an investigation of publication and distribution practices of scholars within the biological sciences and the field of communication. The resulting representations illustrate shared work practices and areas where diverse practices exist, both of which can guide the development of digital collections of scholarly materials. This poster also considers challenges related to aligning data collection methods with the application of these representational techniques.
Multi-Agent Least-Squares Policy Iteration
Authors: Victor Palmer
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Least-Squares Policy Iteration [3] is an approximate reinforcement learning technique capable of training policies over large, continuous state spaces. Unfortunately, the computational requirements of LSPI scale poorly with the number of system agents. Work has been done to address this problem, such as the Coordinated Reinforcement Learning (CRL) approach of Guestrin, et al [1], but this requires that one have prior information about the learning system such as knowing interagent dependencies and the form of the Q-function. We demonstrate a hybrid gradient-ascent/LSPI approach which is capable of using LSPI to efficiently train multi-agent policies. Our approach has computational requirements which scale as O(N), where N is the number of system agents, and does not have the prior knowledge requirements of CRL. Finally, we demonstrate our algorithm on a standard multi-agent network control problem [1].
SoC design approach using convertibility verification
Authors: Roopak Sinha, Partha S. Roop, Samik Basu
Venue: EURASIP Journal on Embedded Systems (2008)

Compositional design of systems on chip from preverified components helps to achieve shorter design cycles and time to market. However, the design process is affected by the issue of protocol mismatches, where two components fail to communicate with each other due to protocol differences. Convertibility verification, which involves the automatic generation of a converter to facilitate communication between two mismatched components, is a collection of techniques to address protocol mismatches. We present an approach to convertibility verification using module checking. We use Kripke structures to represent protocols and the temporal logic ACTL to describe desired system behavior. A tableau-based converter generation algorithm is presented which is shown to be sound and complete. We have developed a prototype implementation of the proposed algorithm and have used it to verify that it can handle many classical protocol mismatch problems along with SoC problems. The initial idea for ACTL-based convertibility verification was presented at SLA++P '07 as presented in the work by Roopak Sinha et al. 2008.
ICT intervention in the `Chandanbari' Village of Bangladesh: Results from a field study
Authors: Mahfuz Ashraf, Jo Hanisch, Paul Swatman
Venue: Information Systems Frontiers (2009)

The United Nations, the World Bank and the International Telecommunication Union purport that information and communication technology (ICT) interventions improve development in third world countries. Hence, developing countries are rushing to implement various ICT interventions in urban-rural areas aiming to engender local or national development. Through an interpretive case study of an ICT intervention in the Chandanbari village area of Bangladesh, this research describes some challenges surrounding ICT interventions. From background literature concerning bridging the digital-divide we emphasise users' capability, local context and local content during the planning and implementation of ICT interventions. We apply Heeks' (eDevelopment Briefing, 3(1), 1---2, 2005) information chain model to improve understanding of ICT-led development at the community/individual level. Our findings uncover considerable interest in ICT interventions from the community/individuals; however, there are challenges to the long-term acceptance of ICT interventions, such as the socio-cultural issues of gender discrimination and cultural barriers.
Evolutionary approach for buffer management in asynchronous transfer mode networks
Authors: M. Sundarambal, R. Devaraj, P. Anbalagan
Venue: International Journal of Information Systems and Change Management (2008)

Fuzzy systems are becoming more widespread in many applications and it is possible to implement using very large scale integration technology. This article deals with buffer management scheme based on evolutionary computing for shared memory asynchronous transfer mode switches. Output queuing is done in the proposed system to increase the throughput. The objective of this research is to model a fuzzy buffer management system that sets the threshold for the output ports in the shared memory. Genetic algorithm technique is used to optimise the fuzzy system parameters derived for each switch size and offers a high degree of scalability. The performance of designed 'fuzzy-genetic algorithm' buffer management system is compared with the push-out mechanism (ideal mechanism) and dynamic threshold mechanism. Fuzzy system is simulated using the MODELSIM software and genetic algorithm is simulated using the JAVA language.