ppxscal/embedding_network huggingface dataset:
A Multiscale, Cell-Based Framework for Modeling Cancer Development
Authors: Yi Jiang
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

We use a systems approach to develop a predictive model that medical researchers can use to study and treat cancerous tumors. Our multiscale, cell-based model includes intracellular regulations, cellular level dynamics and intercellular interactions, and extracellular chemical dynamics. The intracellular level protein regulations and signaling pathways are described by Boolean networks. The cellular level growth and division dynamics, cellular adhesion, and interaction with the extracellular matrix are described by a lattice Monte Carlo model. The extracellular dynamics of the chemicals follow a system of reaction-diffusion equations. All three levels of the model are integrated into a high-performance simulation tool. Our simulation results reproduce experimental data in both avascular tumors and tumor angiogenesis. This model will enable medical researchers to gain a deeper understanding of the cellular and molecular interactions associated with cancer progression and treatment.
Automatic Relative Radiometric Normalization Algorithm Based on Pseudo-Invariant Neighborhood
Authors: Xiaolian Deng, Changyao Wang, Bangjun Lei
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

An automatic relative radiometric normalization algorithm of multi-temporal remote sensing images based on pseudo-invariant neighborhood was introduced in this paper. The main purpose of this research was to develop a robust algorithm of relative radiometric normalization to minimize imaging differences of multi-temporal satellite images. The main idea was to construct statistical regression model of relative radiometric normalization by pseudo-invariant neighborhoods, which were obtained by extracting steady ground point correspondences. The algorithm's detailed processes were as follows: First, a method of image matching was applied to recognize ground point correspondences of multi-temporal remote sensing images. Second, steady ground point correspondences were determined by matching results. Third, sample dataset of steady ground point correspondences was obtained by pseudo-invariant neighborhoods. Finally, a statistical regression model of relative radiometric normalization was constructed to calibrate imaging differences of multi-temporal remote sensing images. By the experiments, we could see that the total RSME of reference image and calibrated image was reduced apparently in comparison with the total RSME of reference image and un-calibrated image. We could conclude that this modified algorithm was more accurate and efficient than traditional algorithm, and the result of calibration was more objective and dependable.
Implicit adaptive mesh refinement for 2D reduced resistive magnetohydrodynamics
Authors: Bobby Philip, Luis Chacón, Michael Pernice
Venue: Journal of Computational Physics (2008)

An implicit structured adaptive mesh refinement (SAMR) solver for 2D reduced magnetohydrodynamics (MHD) is described. The time-implicit discretization is able to step over fast normal modes, while the spatial adaptivity resolves thin, dynamically evolving features. A Jacobian-free Newton-Krylov method is used for the nonlinear solver engine. For preconditioning, we have extended the optimal ''physics-based'' approach developed in [L. Chacon, D.A. Knoll, J.M. Finn, An implicit, nonlinear reduced resistive MHD solver, J. Comput. Phys. 178 (2002) 15-36] (which employed multigrid solver technology in the preconditioner for scalability) to SAMR grids using the well-known Fast Adaptive Composite grid (FAC) method [S. McCormick, Multilevel Adaptive Methods for Partial Differential Equations, SIAM, Philadelphia, PA, 1989]. A grid convergence study demonstrates that the solver performance is independent of the number of grid levels and only depends on the finest resolution considered, and that it scales well with grid refinement. The study of error generation and propagation in our SAMR implementation demonstrates that high-order (cubic) interpolation during regridding, combined with a robustly damping second-order temporal scheme such as BDF2, is required to minimize impact of grid errors at coarse-fine interfaces on the overall error of the computation for this MHD application. We also demonstrate that our implementation features the desired property that the overall numerical error is dependent only on the finest resolution level considered, and not on the base-grid resolution or on the number of refinement levels present during the simulation. We demonstrate the effectiveness of the tool on several challenging problems.
Autonomous Querying for Knowledge Networks
Authors: Kieran Greer, Matthias Baumgarten, Chris Nugent, Maurice Mulvenna, Kevin Curran
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

A knowledge network is a construct that will organise knowledge in a way that allows it to be efficiently retrieved and used. While an Internet-based network is the obvious application area, the system would also be suitable for pervasive sensorised environments. Key elements thereof are its lightweight, reference-based structure and its autonomous nature. This paper is concerned with describing the querying process that will be used to retrieve information from the network. For this process, the network metadata will act as a lightweight and distributed ontology, where the hierarchical structures of the network will describe the main relationships and guide the search. Then, using autonomous querying, the ontology can be updated with personal references between sources, allowing for semantically unrelated concepts to also be linked together. A novel linking mechanism is described that is shown to be effective, dynamic and adaptive, and could be particularly useful in tomorrow's Semantic Web environment.
Improving Temporal Language Models for Determining Time of Non-timestamped Documents
Authors: Nattiya Kanhabua, Kjetil Nørvåg
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

Taking the temporal dimension into account in searching, i.e., using time of content creation as part of the search condition, is now gaining increasingly interest. However, in the case of web search and web warehousing, the timestamps (time of creation or creation of contents) of web pages and documents found on the web are in general not known or can not be trusted, and must be determined otherwise. In this paper, we describe approaches that enhance and increase the quality of existing techniques for determining timestamps based on a temporal language model. Through a number of experiments on temporal document collections we show how our new methods improve the accuracy of timestamping compared to the previous models.
A Similarity Retrieval Method in Brain Image Sequence Database
Authors: Haiwei Pan, Qilong Han, Xiaoqin Xie, Zhang Wei, Jianzhong Li
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Similarity Retrieval in medical image sequence database is an important part in domain-specific application because there are several technical aspects which make this problem challenging. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.
Design and implementation of enhanced IEEE 802.15.4 for supporting multimedia service in Wireless Sensor Networks
Authors: Changsu Suh, Zeeshan Hameed Mir, Young-Bae Ko
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

Recently, there has been a growing demand to incorporate multimedia content delivery over the Wireless Sensor Networks (WSNs). This feature could not only enhance several existing applications in the commercial, industrial, and medical domains, but could also spur an array of new applications. However, the efficient gathering of still images, audio, and video information in WSNs imposes stringent requirements on the throughput and energy consumption. Most wireless communication standards with high or moderate data throughputs do not focus primarily on energy efficiency. The IEEE 802.15.4 WPAN standard provides a widely accepted solution for low-cost and low-power wireless communication, with a potential to cater to many types of application scenarios. IEEE 802.15.4 MAC includes features such as its dual operational modes (Non-Beacon-enabled mode/Beacon-enabled mode), which make this standard more attractive for providing multimedia services over the networked sensors. Its Beacon-enabled mode can conserve energy by using the RF sleep mechanism, but it is limited by the lower data throughput. On the other hand, the Non-Beacon-enabled mode can offer higher data throughput but at the expense of significant energy consumption, mainly due to the idle listening problem. In order to overcome these issues, we propose an enhancement to IEEE 802.15.4, named TEA-15.4, which adaptively adjusts the active period based on traffic information. To detect data traffic in the network, the proposed scheme utilizes two techniques: Arbitrary Traffic Signal (ATS) and Traffic Time-Out (TTO). By utilizing these two techniques, the proposed TEA-15.4 can not only support enough data throughput to carry out multimedia communications, but also offer lower energy consumption for the sensing device in WSNs. For performance evaluations, we implement our proposed scheme and the IEEE 802.15.4 full-standard on the TinyOS. Based on the results gathered from testbed experiments and the TOSSIM simulator, TEA-15.4 is shown to be a suitable mechanism for Wireless Multimedia Sensor Networks (WMSNs).
Self Optimizing Neural Networks SONN-3 for Classification Tasks
Authors: Adrian Horzyk
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The paper introduces new valuable improvements of a performance, a generalization ability and a topology optimization of the Self-Optimizing Neural Networks (SONNs). The described SONN-3 integrates the very effective solutions used in the SONN-2 together with the very effective ADFA algorithms for an automatic conversion of real input features into binary vectors. The integration provides not only a simple sum of valuable features of the both methods but it makes able to substantially improve a performance and generalization properties of these networks reducing SONN-3 topology sizes in comparison to SONN-2 topology sizes. The paper describes the construction of the SONN-3 and compares its performance with the SONN-2 and other AI computational methods applied to an exemplar classification task.
Personalized Recommendations for the Web 3D
Authors: Bartek Ochab, Nicolas Neubauer, Klaus Obermayer
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

We introduce the Second Life Location Recommender System (SLLoRS). This system lets users rate and tag locations within the 3D environment Second Life in order to provide personalized recommendations on a collaborative basis. We demonstrate the system as an in-world application and explore some of the general challenges of applying recommendation systems to 3D online environments, like the implementation of data-intensive applications facing restricted computational resources and the segmentation of recommendations in a continuous input space.
Segment Based Caching Replacement Algorithm in Streaming Media Transcoding Proxy
Authors: Yoohyun Park, Yongju Lee, Hagyoung Kim, Kyongsok Kim
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

To support various devices for multimedia services, it needs to some adaptation technologies. A transcoding is one of major fields in adaptive multimedia delivery. A transcoding proxy is usually necessary to provide adapting multimedia object to various mobile devices by not only transcoding multimedia objects to meet different needs on demand, but also caching them for later use. In this paper, we propose a caching algorithm in transcoding proxy that can reduce the startup time, improve caching ratio with segment-based proxy management. Our simulation results demonstrate that the proposed algorithms outperform the competing algorithms.
Incorporating Privacy Outcomes: Teaching an Old Dog New Tricks
Authors: Ed Brown, Tracy Ann Kosa
Venue: Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust (2008)

Canadian government bodies are subject to a number of requirements, including legislation, regulations,directives and policies, that speaks to informational privacy. These have come to be considered synonymous with the completion of a Privacy Impact Assessment.Some go so far as to specifically require an assessment,but few speak to specific technical content. Nor are there process requirements for sustaining privacy standards once the assessment document is submitted. At best,recommendations are identified to enhance the privacy posture of a program area's information management practices, but there is no mechanism to ensure that they are implemented.We propose the PIA process be adapted to mandate privacy outcomes in terms of specific actions that must betaken once the assessment is complete. Starting with the established PIA document, the program area can identify how to best marry the privacy requirements with the established business processes supporting the service delivery line. The result would incorporate privacy outcomes as ongoing activities and include not only consideration of agency requirements for personal information management, but also the impact to an individual's informational privacy.
Some Criteria of Decidability for Axiomatic Systems in Three-Valued Logic
Authors: I. D. Zaslavsky
Venue: Logic, Language, and Computation: 6th International Tbilisi Symposium on Logic, Language, and Computation,TbiLLC 2005 Batumi, Georgia, September 12-16, 2005. Revised Selected Papers (2007)

Two criteria of decidability for axiomatic systems based on J.Lukasiewicz's three-valued logic are established.
A Novel Rule Ordering Approach in Classification Association Rule Mining
Authors: Yanbo J. Wang, Qin Xin, Frans Coenen
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

A Classification Association Rule (CAR), a common type of mined knowledge in Data Mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an "antecedent $\Rightarrow$ (consequent-class" rule. Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that builds an Association Rule Mining (ARM) based classifier using CARs. Regardless of which particular methodology is used to build it, a classifier is usually presented as an ordered CAR list, based on an applied rule ordering strategy. Five existing rule ordering mechanisms can be identified: (1) Confi-dence-Support-size_of_Antecedent (CSA), (2) size_of_Antecedent-Confidence-Support (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) (¿ 2 Testing. In this paper, we divide the above mechanisms into two groups: (i) pure "support-confidence" framework like, and (ii) additive score assigning like. We consequently propose a hybrid rule ordering approach by combining one approach taken from (i) and another approach taken from (ii). The experimental results show that the proposed rule ordering approach performs well with respect to the accuracy of classification.
A Multi-Source Streaming Model for Mobile Peer-to-Peer (P2P) Overlay Networks
Authors: Alireza Goudarzi Nemati, Tomoya Enokido, Makoto Takizawa
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

In peer-to-peer (P2P) overlay networks, multimedia contents are in nature distributed to peers by downloading and caching. Here, a peer which transmits a multimedia content and a peer which receives the content are referred to as source and receiver peers, respectively. A peer on a mobile computer is moving in the network. Furthermore, a peer moves around computers as a mobile agent. Thus, not only receiver peers but also source peers are moving. In this paper, we would like to discuss how source peers deliver multimedia contents to receiver peers in a streaming model so that enough quality of service (QoS) required is supported in change of network and peer QoS, possibly according to the movements of the peers. In this paper, we discuss a multi-source streaming protocol where a receiver peer can receive packets of a multimedia content from multiple source peers which can support enough QoS. If a current source peer is expected to support less QoS than required, another source peer takes over the source peer and starts sending packets of the multimedia content. The receiver peer is required to receive packets of the content with enough QoS, e.g. no packet loss even if the source peer is being changed. We discuss how to change source peers so as to support enough QoS to the receiver peer.
Data Clustering: User's Dilemma
Authors: Anil K. Jain
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Data clustering is a long standing research problem in pattern recognition, computer vision, machine learning, and data mining with applications in a number of diverse disciplines. The goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge about cluster shapes. While a large number of clustering algorithms exist, there is no optimal algorithm. Each clustering algorithm imposes a specific structure on the data and has its own approach for estimating the number of clusters. No single algorithm can adequately handle various cluster shapes and structures that are encountered in practice. Instead of spending our effort in devising yet another clustering algorithm, there is a need to build upon the existing published techniques. In this talk we will address the following problems: (i) clustering via evidence accumulation, (ii) simultaneous clustering and dimensionality reduction, (iii) clustering under pair-wise constraints, and (iv) clustering with relevance feedback. Experimental results show that these approaches are promising in identifying arbitrary shaped clusters in multidimensional data.
Heterogeneous Reasoning
Authors: John Etchemendy
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

During the 1990s, we developed the theory of formal heterogeneous deduction: logically valid inference that involves information expressed using multiple different representations. The Hyperproof program was developed as an implementation of that theory, and permitted deductions using sentences of first-order logic and blocks world diagrams.We have since been generalizing both the theory and the implementation to allow applications in a wide variety of domains.
Second Life Gift Registry: Bringing Retail Web Applications into the Metaverse
Authors: David Chodos, Eleni Stroulia
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

The combination of realistic virtual environments and product information offers a compelling new medium for consumers to shop for products. Specifically, by combining the experience of exploring a virtual house with retail information on items in that house, a consumer can virtually populate a house with desired items. This is particularly appealing for people, such as a couple getting married, who are looking to furnish an entire house with newly-purchased items. The system proposed in this paper provides this combination of realistic, 3D visualization and detailed product information. It uses Second Life to provide the realistic user interface, and a REST-style web-based application to create connections between this platform and publicly-available retailer APIs. In cases where a retailer API is unavailable, HTML wrapping may be used to obtain equivalent information. The economic viability of the concept is examined through a business case study.
Design features of the ERA 1101 computer
Authors: F. C. Mullaney
Venue: Papers and discussions presented at the Dec. 10-12, 1951, joint AIEE-IRE computer conference: Review of electronic digital computers (1951)

The ERA 1101 computer is a single-address binary-system parallel computer using magnetic drum memory. The word length is 24 binary digits, equivalent to seven decimal digits plus sign. The logic is quite conventional. 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
Web User Profiling Using Fuzzy Clustering
Authors: Giovanna Castellano, Fabrizio Mesto, Michele Minunno, Maria Alessandra Torsello
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Web personalization is the process of customizing a Web site to the preferences of users, according to the knowledge gained from usage data in the form of user profiles. In this work, we experimentally evaluate a fuzzy clustering approach for the discovery of usage profiles that can be effective in Web personalization. The approach derives profiles in the form of clusters extracted from preprocessed Web usage data. The use of a fuzzy clustering algorithm enable the generation of overlapping clusters that can capture the uncertainty among Web users navigation behavior based on their interest. Preliminary experimental results are presented to show the clusters generated by mining the access log data of a Web site.
Short-Term Load Forecasting Using BiLinear Recurrent Neural Network
Authors: Sung Hwan Shin, Dong-Chul Park
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A prediction scheme of short-term electric load forecasting using a BiLinear Recurrent Neural Network (BLRNN) is proposed in this paper. Since the BLRNN is based on the bilinear polynomial, it has been successfully used in modeling highly nonlinear systems with time-series characteristics and the BLRNN can be a natural choice in predicting electric load. The performance of the proposed BLRNN-based predictor is evaluated and compared with the conventional MultiLayer Perceptron Type Neural Network (MLPNN)-based predictor. Experiments are conducted on load data from the North-American Electric Utility (NAEU). The results show that the proposed BLRNN-based predictor outperforms the MLPNN-based one in terms of the Mean Absolute Percentage Error (MAPE).