ppxscal/embedding_network huggingface dataset:
HMNews: an integrated system for searching and browsing hypermedia news content
Authors: Maurizio Montagnuolo, Marco Ferri, Alberto Messina
Venue: Proceedings of the 20th ACM conference on Hypertext and hypermedia (2009)

We describe HMNews (Hyper-Media News), a system designed and implemented for the collection, indexing and retrieval of hypermedia news content coming from Digital Television and the Web. The novelty of the approach relies in the ability of providing hierarchical and multi-resolution multimodal indexes based on the application of a novel generalised hybrid clustering technique. The system supports many functionalities: a) bi-directional news conceptual linking; b) relevant topics detection and tracking; c) integrated hypermedia browsing; d) integrated search and retrieval.
Theoretical results of the dust acoustic waves in a magnetized dusty plasma with different dust particles
Authors: Chang Lin, Mai-mai Lin, Wei-gui Feng
Venue: Journal of Computational and Applied Mathematics (2009)

In this paper, the nonlinear dust acoustic waves (DAW) in a magnetized dusty plasmas with different dust grains are analytically investigated. New analytical solutions of the governing equation for this system have been obtained for the first time. The exact mathematical expressions of the nonlinear dust waves have been canvassed for the general case in magnetized dusty plasma containing different dust particles.
Interactive Management of Human Factors Knowledge for Human Systems Integration Using Systems Modeling Language
Authors: Waldemar Karwowski, Tareq Z. Ahram
Venue: Information Systems Management (2009)

The field of Human Factors in Knowledge Management is often seen as a problem of capturing, organizing, and retrieving information to build knowledge. This process is inextricably bound up with human cognition and, as such, the management of knowledge occurs within an intricately structured behavioral, cultural, and social context. This paper emphasizes the importance of interactive human factors in knowledge management and introduces a model-based human systems integration framework based on systems modeling language (SysML).
Variable Resistance Hand Device using an electro-rheological fluid damper
Authors: B. Weinberg, A. Khanicheh, M. Sivak, O. Unluhisarcikli, G. Morel, J. Shannon, J. Kelliher, M. Sabadosa, G. Bonmassar, B. Patritti, P. Bonato, C. Mavroidis
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This paper presents the design, fabrication, control and testing of the third generation prototype of a novel, one degree of freedom (DOF) Variable Resistance Hand Device (VRHD) that was designed for isotonic, isokinetic, and variable resistance grasp and release exercises. Its principle functionality is derived from an electro-rheological fluid based controllable damper that allows continuously variable modulation of dynamic resistance throughout its stroke. The VRHD system consists of the patient actuated device, the control electronics and software, the practitioner graphical interface and the patient's virtual reality game software. VRHD was designed and experimentally shown to be fully Magnetic Resonance Imaging (MRI) compatible so that it can be used in brain MR imaging during handgrip rehabilitation.
Simulating Individual-Based Models of Epidemics in Hierarchical Networks
Authors: Rick Quax, David A. Bader, Peter M. Sloot
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics operators, which are iteratively applied to the contact network. We reduce the network generator's computational complexity, improve cache efficiency and parallelize the simulator. To evaluate its running time we experiment with an HIV epidemic model that incorporates up to one million homosexual men in a scale-free network, including hierarchical community structure, social dynamics and multi-stage intranode progression. We find that the running times are feasible, on the order of minutes, and argue that SEECN can be used to study realistic epidemics and its properties experimentally, in contrast to defining and solving ever more complicated mathematical models as is the current practice. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A Semantic-Based Navigation Approach for Information Retrieval in the Semantic Web
Authors: Mourad Ouziri
Venue: Proceeding of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies (2007)

Information retrieval in the Web needs more efficient way than traditional approaches. That is, traditional information retrieval approaches are not accurate because they consider only the syntactical level when processing Web resources. So, more conceptual approaches are needed to increase accuracy of information retrieval. In the next generation of the Web, namely the semantic Web, the challenge of information retrieval approaches is to design automatic programs that are able to understand and process semantics of Web resources. Therefore, semantics needs to be clearly formalized and processed by programs. In this chapter, we use a knowledge representation formalism to represent the semantics of Web resources and then to perform semantic navigation. The knowledge representation formalism is used to design a semantic index of the Web resources. Generally, it references a huge amount of Web resources. So we present an efficient navigation strategy of this index to make more accurate information retrieval.
Denoising by using multineural networks for medical X-ray imaging applications
Authors: Yeqiu Li, Jianming Lu, Ling Wang, Yahagi Takashi
Venue: Neurocomputing (2009)

In this paper, a new type of multineural network filter (MNNF) is presented that is trained for restoration and enhancement of the digital radiological images. In medical radiographices, noise has been categorized as quantum mottle, which is related to the incident X-ray exposure and artificial noise, which is caused by the grid, etc. MNNF consists of several neural network filters (NNFs). A novel analysis method is proposed to make the characteristics of the trained MNNF clearly. In the proposed method, a characteristics judgement system is presented to decide which NNF will be executed through the standard deviation value of pixels in the input region. The new approach was tested on nine clinical medical X-ray images and five synthesized noisy X-ray images. In all cases, the proposed MNNF produced better results in terms of peak signal-to-noise ratio (PSNR), mean-to-standard-deviation ratio (MSR) and contrast to noise ratio (CNR) measures than the original NNF, linear inverse filter and nonlinear median filter.
Constructive formal conversion of Moore machine to deterministic finite automata
Authors: Shagufta Riaz, Nazir Ahmad Zafar
Venue: Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems (2008)

The complexity of a system in terms of its state space and functional aspects enforces to choose most prolific and rigorous approaches to sustain the development of the software systems. Finite automaton is a formal and natural medium to describe the static and dynamic behavior of a complex system. Z notation is an ideal specification language having inherent capability to describe state space of a system and then defining operations over it. This paper deals with the integration of finite automata and Z which can enhance the modeling power for a complex system. Although Moore machine cannot be used for language recognition but its conversion into deterministic finite automata makes it possible to pretend as a language recognizer. Here a formal conversion of Moore machine to deterministic finite automata based on a string as well as a language is proposed using Z. Formal specification of the linkage between Z and automata is analyzed and validated using Z/EVES tool.
iSAX: disk-aware mining and indexing of massive time series datasets
Authors: Jin Shieh, Eamonn Keogh
Venue: Data Mining and Knowledge Discovery (2009)

Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we introduce a novel multi-resolution symbolic representation which can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. To demonstrate the utility of this representation, we constructed a simple tree-based index structure which facilitates fast exact search and orders of magnitude faster, approximate search. For example, with a database of one-hundred million time series, the approximate search can retrieve high quality nearest neighbors in slightly over a second, whereas a sequential scan would take tens of minutes. Our experimental evaluation demonstrates that our representation allows index performance to scale well with increasing dataset sizes. Additionally, we provide analysis concerning parameter sensitivity, approximate search effectiveness, and lower bound comparisons between time series representations in a bit constrained environment. We further show how to exploit the combination of both exact and approximate search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing tens of millions of time series.
Instruction-Level Fault Tolerance Configurability
Authors: Demid Borodin, B. H. Juurlink, Said Hamdioui, Stamatis Vassiliadis
Venue: Journal of Signal Processing Systems (2009)

Due to modern technology trends such as decreasing feature sizes and lower voltage levels, fault tolerance (FT) is becoming increasingly important in computing systems. Several schemes have been proposed to enable a user to configure the FT at the application level, thereby enabling the user to trade stronger FT for performance or vice versa. In this paper, we propose supporting instruction-level rather than application-level configurability of FT, since different parts of some applications (e.g., multimedia) can have different reliability requirements. Weak or no FT will be applied to less critical parts, resulting in time and/or resource gains. These gains can be used to apply stronger FT techniques to the more critical parts; hence increasing the overall reliability. The paper shows how some existing FT techniques can be adapted to support instruction-level FT configurability, how a programmer can specify the desired FT level of the instructions, and how the compiler can manage it automatically. A comparison between the existing FT scheme EDDI (which duplicates all instructions) and the proposed approach is performed both at the kernel and at full application levels. The simulation results show that both the performance and the energy consumption are significantly improved (up to 50% at the kernel and up to 16% at full application level), while the fault coverage depends on the application. For the full application (JPEG encoder), our approach is only applied to one kernel in order to avoid increasing the programming effort significantly.
A multi-level biomedical ontology-enabled broker: dynamic service-based data source integration
Authors: Ramez Elmasri
Venue:  (2008)

Web services have recently become a new trend for gathering biomedical information. However, it is not easy to integrate and obtain a concise/complete query result among thousands of service operations. In this dissertation, we propose a multi-level ontology-enabled service broker system for dynamically integrating web services in the biomedical domain (BioServiceBroker)[1, 2]. By introducing multi-level modeling concepts and intra/inter level relationships [3], our approach facilitates more accurate modeling of biomedical ontologies, which leads to a better understanding of the data stored in various biological data sources as well as the services provided by the data sources. In addition, we incorporate temporal concepts with new enhanced QoS measures [2], which allow service requesters to control more querying factors in order to precisely invoke corresponding service operations. We also define a Unified Biomedical Service Interface (UBSI) as a proposed service deployment standard. The multi-level ontology-enabled service broker system can be combined with other mediator systems [4] for cross referencing the diverse bioinformatics sources and can also be utilized in other application domains. Our ultimate goal is to construct a public, scalable, and interoperable biomedical service platform based on UBSI to benefit scientists in data searching and publishing.
More on Presumptions and Burdens of Proof
Authors: Henry Prakken, Giovanni Sartor
Venue: Proceeding of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference (2008)

This paper extends our previous logical analysis of presumptions and burden of proof by studying the force of a presumption once counterevidence has been offered. In the jurisprudential literature different accounts of this issue have been given: some have argued that a presumption is nullified by counterarguments while others have maintained that this gives presumptions a force that is too slight. We argue that these differences largely are not a matter of logic but of legal policy, and we show how the various accounts can be logically formalised.
A fast and automatic stereotactic registration with a single CT-slice
Authors: B. Maurin, C. Doignon, M. de Mathelin, A. Gangi
Venue: Computer Vision and Image Understanding (2009)

The paper presents an algorithm for the automatic registration of a stereotactic frame from a single slice captured by a computed tomography (CT) scanner. Such registration is needed in interventional radiology for CT-image guidance of a robotic assistant. Stereotactic registration is based on a set of line fiducials that produce a set of image points. Our objective is to achieve the automatic registration of a stereotactic frame in the presence of noisy data and outliers. To this end, a new formulation of the stereotactic registration problem with a single image is proposed, for any configuration of the fiducials. With very few fiducials, closed-form and numerical solutions are presented. This comes in useful for building a robust automatic line to point matching algorithm based on the RANSAC statistical method. Simulation and experimental results are presented and highlight the robustness properties of the algorithm. They show that the registration is accurate with moderate computing requirements even for a large amount of outliers.
Nonlinear dynamic system identification using pipelined functional link artificial recurrent neural network
Authors: Haiquan Zhao, Jiashu Zhang
Venue: Neurocomputing (2009)

A computationally efficient pipelined functional link artificial recurrent neural network (PFLARNN) is proposed for nonlinear dynamic system identification using a modification real-time recurrent learning (RTRL) algorithm in this paper. In contrast to a feedforward artificial neural network (such as a functional link artificial neural network (FLANN)), the proposed PFLARNN consists of a number of simple small-scale functional link artificial recurrent neural network (FLARNN) modules. Since those modules of PFLARNN can be performed simultaneously in a pipelined parallelism fashion, this would result in a significant improvement in its total computational efficiency. Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion. Therefore, the performance of the proposed filter can be further improved. Computer simulations demonstrate that with proper choice of functional expansion in the PFLARNN, this filter performs better than the FLANN and multilayer perceptron (MLP) for nonlinear dynamic system identification.
Hybrid Adaptive Web Service Selection with SAWSDL-MX and WSDL-Analyzer
Authors: Matthias Klusch, Patrick Kapahnke, Ingo Zinnikus
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

In this paper, we present an adaptive, hybrid semantic matchmaker for SAWSDL services, called SAWSDL-MX2. It determines three kinds of semantic service similarity with a given service request, that are logic-based, text-based and structural similarity. In particular, the degree of structural service similarity is computed by the WSDL-Analyzer tool [12] by means of XMLS tree edit distance measurement, string-based and lexical comparison of the respective XML-based WSDL services. SAWSDL-MX2 then learns the optimal aggregation of these different matching degrees over a subset of a test collection SAWSDL-TC1 based on a binary support vector machine-based classifier. Finally, we compare the retrieval performance of SAWSDL-MX2 with a non-adaptive matchmaker variant SAWSDL-MX1 [1] and the straight forward combination of its logic-based only variant SAWSDL-M0 with WSDL-Analyzer.
Visual exploration of categorical and mixed data sets
Authors: Sara Johansson
Venue: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (2009)

For categorical data there does not exist any similarity measure which is as straight forward and general as the numerical distance between numerical items. Due to this it is often difficult to analyse data sets including categorical variables or a combination of categorical and numerical variables (mixed data sets). Quantification of categorical variables enables analysis using commonly used visual representations and analysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data, which uses a quantification process introduced in [16]. The application enables analysis of mixed data sets by providing an environment for exploratory analysis using common visual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The effectiveness of the quantification process and of the features of the application is demonstrated through a case scenario.
High quality factor silicon cantilever driven by piezoelectric thin film actuator for resonant based mass detection
Authors: Jian Lu, Tsuyoshi Ikehara, Yi Zhang, Takashi Mihara, Toshihiro Itoh, Ryutaro Maeda
Venue: Microsystem Technologies (2009)

A high quality factor (Q-factor) piezoelectric lead zirconate titanate (PZT) thin film actuated single crystal silicon cantilever was proposed in this paper for resonant based ultra sensitive mass detection. Intrinsic energy dissipation and other negative effects from PZT-electrode stack were successfully compressed by separating the PZT actuator from the resonant structure. Excellent Q-factor, which is comparable to silicon cantilever (without actuator) and several times larger than that of latest reported other integrated cantilevers, was successfully obtained under both atmospheric pressure and reduced pressures. For a 30&#x00a0;&#x03bc;m-wide 100&#x00a0;&#x03bc;m-long cantilever, the Q-factor was measured as 1,113 and 7,279 under the pressure of 101.2&#x00a0;KPa and 35&#x00a0;Pa, respectively. Besides, it was found that greater Q-factor can be achieved at high vibration mode by the proposed structure. However, support loss became significant because of the increased actuator&#x2019;s vibration amplitude which in turn leads to unexpected energy dissipation. Therefore, an optimized structure using node-point actuation was suggested and discussed in last section of this paper.
Compact MR-brake with serpentine flux path for haptics applications
Authors: Doruk Senkal, Hakan Gurocak
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This research explores a new approach to shape the magnetic flux path in a MR brake. Magnetically conductive and non-conductive elements were stacked to weave the magnetic flux through the rotor and the outer shell of the brake. This approach enabled design of a more compact and powerful MR brake. In addition, a ferro-fluidic sealing technique was developed to prevent the fluid from leaking and to reduce off-state friction. Experimental results showed that, when compared to a commercial MR brake, our 33% smaller prototype MR-brake could generate 2.7 times more torque (10.9 Nm). A 1-DOF haptic interface employing the brake enabled crisp virtual wall collision simulations. Significant reduction in the off-state torque was obtained by applying a reverse current pulse to collapse a residual magnetic field in the brake.
The Big Brother Database: Evaluating Face Recognition in Smart Home Environments
Authors: Annalisa Franco, Dario Maio, Davide Maltoni
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

In this paper a preliminary study on template updating techniques for face recognition in home environments is presented. In particular a new database has been created specifically for this application, where the face images acquired are characterized by a great variability in terms of pose and illumination but the number of subjects is quite limited and a large amount of images can be exploited for intensive incremental learning. The steps of database creation and the characteristics of the data collected are described in detail. We believe such a database could be very useful to develop and optimize face recognition approaches for smart home environments. Moreover some preliminary results on incremental learning are provided and analyzed to evaluate the effects of incremental template updating on the recognition performance.