ppxscal/embedding_network huggingface dataset:
Accuracy and stability in incompressible SPH (ISPH) based on the projection method and a new approach
Authors: Rui Xu, Peter Stansby, Dominique Laurence
Venue: Journal of Computational Physics (2009)

The stability and accuracy of three methods which enforce either a divergence-free velocity field, density invariance, or their combination are tested here through the standard Taylor-Green and spin-down vortex problems. While various approaches to incompressible SPH (ISPH) have been proposed in the past decade, the present paper is restricted to the projection method for the pressure and velocity coupling. It is shown that the divergence-free ISPH method cannot maintain stability in certain situations although it is accurate before instability sets in. The density-invariant ISPH method is stable but inaccurate with random-noise like disturbances. The combined ISPH, combining advantages in divergence-free ISPH and density-invariant ISPH, can maintain accuracy and stability although at a higher computational cost. Redistribution of particles on a fixed uniform mesh is also shown to be effective but the attraction of a mesh-free method is lost. A new divergence-free ISPH approach is proposed here which maintains accuracy and stability while remaining mesh free without increasing computational cost by slightly shifting particles away from streamlines, although the necessary interpolation of hydrodynamic characteristics means the formulation ceases to be strictly conservative. This avoids the highly anisotropic particle spacing which eventually triggers instability. Importantly pressure fields are free from spurious oscillations, up to the highest Reynolds numbers tested.
Feature Selection Based on SVM for Credit Scoring
Authors: Ping Yao
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

As the credit industry has been growing rapidly, huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model, so, effective feature selection methods are necessary for credit dataset with huge number of features. This paper aims at comparing seven well-known feature selection methods for credit scoring. Which are t-test, principle component analysis (PCA), factor analysis (FA), stepwise regression, Rough Set (RS), Classification and regression tree (CART) and Multivariate adaptive regression splines (MARS). Support vector machine (SVM) is used as the classification model. Two credit scoring databases are used in order to provide a reliable conclusion. Regarding the experimental results, the CART and MARS methods outperform the other methods by the overall accuracy and type I error and type II error.
Fiber Optic Lighting Development Trends in Asia
Authors: Shih-Yuan Chiang, Chiu-Chi Wei, Te-Hsuan Chiang, Chiao-Ling Peng
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Fiber optic lighting products, which originated from defense applications but have gradually migrated to civilian uses, possess the advantages of energy conservation, safety, and environmental protection, and allow the control of light path. As a consequence, fiber optic lighting products are expected to play key roles in the lighting industry during the 21st century. In view of these circumstances, this study explores fiber optic lighting industry development trends in Asia, including the developed countries of Taiwan and Japan and the developing countries of China and Vietnam, and examines issues that may affect the fiber optic lighting industry in the future. This study employs the Delphi method to interview experts and researchers in Taiwan, Japan, China, and Vietnam, determine a hierarchy influencing the fiber optic lighting industry's future development trends, and establish assessment criteria. The Analytic Hierarchy Process (AHP) is then used to investigate the relative importance of different aspects and rules. Results suggest that the development of fiber optic lighting in Taiwan must emphasize safety awareness, strengthening environmental protection laws, and integration of technologies from different industries, in order to respond to overall industry needs and conform to international development trends. The main point of emphasis in Japan is on the drafting and revision of laws governing environmental energy conservation mechanism so as to reduce pollution on a worldwide scale. In addition, the strong guidance ability of relevant units of the Japanese government, and people's high level of acceptance of green products, constitute the key to an industry clustering effect in Japan. For their part, China and Vietnam must depend on transfer of foreign technology and integration with existing technologies if they are to develop technologies and products. The two countries are still at the development stage, and must obtain technology from foreign sources. Because of this, since these countries' governments are aware of the difficulty of obtaining initial development funds, they must rely on their foreign investment policies and incentives to stimulate the development of their fiber optic lighting industries.
Enhancing the recall of presented material
Authors: Ronald B. Larson
Venue: Computers Education (2009)

Many educators distribute either complete or incomplete handouts so students can follow along with their lectures. This research examines a teaching system that combines computer-generated graphics presentations and detailed outline handouts with blanks added. An experiment found that this system produced significantly higher short-term recall of a presentation when compared with note-taking on lined paper and with no note-taking. Visual design variables, specifically slide typefaces and type, were also manipulated to examine whether they had independent effects on short-term recall or influenced the benefits from note-taking. No significant main effects or interactions were found for the visual aid variables.
Designing Usable Bio-information Architectures
Authors: Davide Bolchini, Anthony Finkestein, Paolo Paolini
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains (2009)

Bioinformatics websites offer to the life science large community repositories of information ranging from genes, genomes, proteins, experimental data and their integration, with the aim of supporting the elucidation of biological processes. As the bioinformatics community increasingly relies on the design, sharing and use of web-based resources, it is important to systematically address the usability of these applications and to deliver a more rewarding user experience to researchers. The bioinformatics community is also acknowledging the role that Human-Computer Interaction can play to improve the usability of these systems. In the context of a project aiming at improving the usability of large bioinformatics websites, we carried out an in-depth usability analysis and conceptual redesign of a well-known protein repository, with the aim of characterizing information architecture usability problems and providing corresponding design solutions to improve the user experience. This design has been validated and refined using interactive prototypes with users, usability experts and domain experts, and opens a new set of navigation opportunities which has the potential to improve the research work of bioinformaticians. Although being a preliminary study, the research reveals generic information architecture and navigation issues which have design implications for browsing-intensive bioinformatics repositories at large.
A New Hybrid DCT and Contourlet Transform Based JPEG Image Steganalysis Technique
Authors: Zohaib Khan, Atif Bin Mansoor
Venue: Proceedings of the 16th Scandinavian Conference on Image Analysis (2009)

In this paper, a universal steganalysis scheme for JPEG images based upon hybrid transform features is presented. We first analyzed two different transform domains (Discrete Cosine Transform and Discrete Contourlet Transform) separately, to extract features for steganalysis. Then a combination of these two feature sets is constructed and employed for steganalysis. A Fisher Linear Discriminant classifier is trained on features from both clean and steganographic images using all three feature sets and subsequently used for classification. Experiments performed on images embedded with two variants of F5 and Model based steganographic techniques reveal the effectiveness of proposed steganalysis approach by demonstrating improved detection for hybrid features.
Enlisting ISPs to Improve Online Privacy: IP Address Mixing by Default
Authors: Barath Raghavan, Tadayoshi Kohno, Alex C. Snoeren, David Wetherall
Venue: Proceedings of the 9th International Symposium on Privacy Enhancing Technologies (2009)

Today's Internet architecture makes no deliberate attempt to provide identity privacy--IP addresses are, for example, often static and the consistent use of a single IP address can leak private information to a remote party. Existing approaches for rectifying this situation and improving identity privacy fall into one of two broad classes: (1) building a privacy-enhancing overlay layer (like Tor) that can run on top of the existing Internet or (2) research into principled but often fundamentally different new architectures. We suggest a middle-ground: enlisting ISPs to assist in improving the identity privacy of users in a manner compatible with the existing Internet architecture, ISP best practices, and potential legal requirements.
Clustering data stream: A survey of algorithms
Authors: Alireza Rezaei Mahdiraji
Venue: International Journal of Knowledge-based and Intelligent Engineering Systems (2009)

A data stream is a massive, continuous and rapid sequence of data elements. The data stream model requires algorithms to make a single pass over the data, with bounded memory and limited processing time, whereas the stream may be highly dynamic and evolving over time. Mining data streams is a real time process of extracting interesting patterns from high-speed data streams. Mining data streams raises new problems for the data mining community in terms of how to mine continuous high-speed data items that you can only have one look at. Clustering, useful tool in data mining, is the process of finding groups of similar data elements which are defined by a given similarity measure. Over the past few years, a number of clustering algorithms for data stream have been put forth. In this paper, we survey five different algorithms for clustering data stream. These algorithms consist divide and conquer, doubling, statistical grid-based, STREAM and CluStream. We compare these algorithms based on several different characters.
An Overview of FORCES: An INRIA Project on Declarative Formalisms for Emergent Systems
Authors: Jesús Aranda, Gerard Assayag, Carlos Olarte, Jorge A. Pérez, Camilo Rueda, Mauricio Toro, Frank D. Valencia
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

The FORCES project aims at providing robust and declarative formalisms for analyzing systems in the emerging areas of Security Protocols, Biological Systems and Multimedia Semantic Interaction. This short paper describes FORCES's motivations, results and future research directions.
Characterization of the convergence of stationary Fokker-Planck learning
Authors: Arturo Berrones
Venue: Neurocomputing (2009)

The convergence properties of the stationary Fokker-Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empirical arguments for the characterization of convergence of the estimation in the case of separable and nonseparable nonlinear optimization problems are given. Some implications of the convergence of stationary Fokker-Planck learning for the inference of parameters in artificial neural network models are outlined.
A Security Log Based Trust Negotiation Model
Authors: Cao Yuan, Yaqin Li, Jun Yan, Zhengsong Liao
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

Trust Negotiation (TN) is an approach to establish trust relationship between strangers by disclosing iteratively credentials and access control policies. In open and distributed environment TN brings convenience in resource share, while it still has some shortcomings, such as easy to fail in negotiation, difficult to track users’ deed and so on.... Security log provides a method to analyze the past and present status of TN for system administrator. According the current state of TN, a security log based trust negotiation model is proposed in the paper by combining security log with TN, which helps efficiently find out why the negotiation fails and track users’ deed by recording all kinds of negotiation states.
An Investigation of Models for GPS Kinematical Data Processing
Authors: Liu Lilong, Wen Hongyan, Liu Bin
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 02 (2009)

The characteristics of three GPS kinematical data processing models, Least Square, Kalman filtering, are discussed and their advantages and disadvantages are compared. With observational data and pertinent data processing software, the applicable condition, context and effect of the three models are experimented. Results show that when the mobile platform is in uniform motion, the accuracy of the three models are almost equal; when the mobile platform is in stochastic acceleration, the accuracy of new filtering model is superior to that of LS, and that of Kalman filtering is the worst.
A computational model of the cerebral cortex
Authors: Thomas Dean
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

Our current understanding of the primate cerebral cortex (neocortex) and in particular the posterior, sensory association cortex has matured to a point where it is possible to develop a family of graphical models that capture the structure, scale and power of the neocortex for purposes of associative recall, sequence prediction and pattern completion among other functions. Implementing such models using readily available computing clusters is now within the grasp of many labs and would provide scientists with the opportunity to experiment with both hard-wired connection schemes and structure-learning algorithms inspired by animal learning and developmental studies. While neural circuits involving structures external to the neocortex such as the thalamic nuclei are less well understood, the availability of a computational model on which to test hypotheses would likely accelerate our understanding of these circuits. Furthermore, the existence of an agreed-upon cortical substrate would not only facilitate our understanding of the brain but enable researchers to combine lessons learned from biology with state-of-the-art graphical-model and machine-learning techniques to design hybrid systems that combine the best of biological and traditional computing approaches.
Sparse kernel SVMs via cutting-plane training
Authors: Thorsten Joachims, Chun-Nam John Yu
Venue: Machine Learning (2009)

We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. This results in two benefits. First, the added flexibility makes it possible to find sparser solutions of good quality, substantially speeding-up prediction. Second, the improved sparsity can also make training of Kernel SVMs more efficient, especially for high-dimensional and sparse data (e.g. text classification). This has the potential to make training of Kernel SVMs tractable for large training sets, where conventional methods scale quadratically due to the linear growth of the number of SVs. In addition to a theoretical analysis of the algorithm, we also present an empirical evaluation.
Optimal Multicore Scheduling: An Application of ASP Techniques
Authors: Viren Kumar, James Delgrande
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

Optimal scheduling policies for multicore platforms have been the holy grail of systems programming for almost a decade now. In this paper, we take two instances of the optimal multicore scheduling problem, the dual-core and the quad-core instances, and attempt to solve them using Answer Set Programming. We represent the problem using a graph theoretic formulation, whereby finding the minimum edge cover of a weighted graph aids us in finding an optimal multicore schedule. We then use an answer set solver to obtain answer sets of varying sizes for our formulation. While a polynomial time algorithm for finding the minimum edge cover of a weighted graph exists in imperative languages and is suitable for dual-core processors, our approach uses ASP techniques to solve both the dual-core and quad-core versions of this problem. We discuss some optimizations to reduce execution runtime, and conclude by discussing potential uses of our application.
An Optimization Load Balancing Algorithm Design in Massive Storage System
Authors: Deng Zhenjie, Cui Yan
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

Modeling on the average storage response time to a single file and the RAID diak array is accomplished by using M/G/1 queuing theory, and the optimal load balancing algorithm based on this model is addressed, and its implementation method is given. The optimal stripe algorithm is effective by simulation experimental verification; it can use the corresponding SU (Stripe Unit) and SW (Stripe Width) according to the different storage access characteristics and different access flow, so it has the shortest storage access response time.
Evaluation of Web User Interfaces for the Online Retail of Apparel
Authors: Dominik Rupprecht, Rainer Blum, Karim Khakzar
Venue: Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009 (2009)

In this paper we present intermediate findings of the ongoing research project SiMaKon. It presents results of user tests, on how to design a web-based user interface for the online retail of apparel. The aim was to increase both the usability and the shopping experience. The study focuses on the manner of the product catalogue navigation and presentation in relation to different "modalities of needs". The results indicate that not only one approach for an interface is to be chosen, but that a combination of several concepts should be combined.
Multi-class Blind Steganalysis Based on Image Run-Length Analysis
Authors: Jing Dong, Wei Wang, Tieniu Tan
Venue: Proceedings of the 8th International Workshop on Digital Watermarking (2009)

In this paper, we investigate our previously developed run-length based features for multi-class blind image steganalysis. We construct a Support Vector Machine classifier for multi-class recognition for both spatial and frequency domain based steganographic algorithms. We also study hierarchical and non-hierarchical multi-class schemes and compare their performance for steganalysis. Experimental results demonstrate that our approach is able to classify different stego images according to their embedding techniques based on appropriate supervised learning. It is also shown that the hierarchical scheme performs better in our experiments.
Visual Culture and Electronic Government: Exploring a New Generation of E-Government
Authors: Victor Bekkers, Rebecca Moody
Venue: Proceedings of the 8th International Conference on Electronic Government (2009)

E-government is becoming more picture-oriented. What meaning do stakeholders attach to visual events and visualization? Comparative case study research show the functional meaning primarily refers to registration, integration, transparency and communication. The political meaning refers to new ways of framing in order to secure specific interests and claims. To what the institutional meaning relates is ambiguous: either it improves the position of citizens, or it reinforces the existing bias presented by governments. Hence, we expect that the emergence of a visualized public space, through omnipresent penetration of (mobile) multimedia technologies, will influence government-citizen interactions.
A Digest and Pattern Matching-Based Intrusion Detection Engine
Authors: Zhongqiang Chen, Yuan Zhang, Zhongrong Chen, Alex Delis
Venue: The Computer Journal (2009)

Intrusion detection/prevention systems (IDSs/IPSs) heavily rely on signature databases and pattern matching (PM) techniques to identify network attacks. The engines of such systems often employ traditional PM algorithms to search for telltale patterns in network flows. The observations that real-world network traffic is largely legitimate and that telltales manifested by exploits rarely appear in network streams lead us to the proposal of Fingerprinter. This framework integrates fingerprinting and PM methods to rapidly distinguish well-behaved from malicious traffic. Fingerprinter produces concise digests or fingerprints for attack signatures during its programming phase. In its querying phase, the framework quickly identifies attack-free connections by transforming input traffic into its fingerprint space and matching its digest against those of attack signatures. If the legitimacy of a stream cannot be determined by fingerprints alone, our framework uses the Boyer–Moore algorithm to ascertain whether attack signatures appear in the stream. To reduce false matches, we resort to multiple fingerprinting techniques including Bloom–Filter and Rabin–Fingerprint. Experimentation with a prototype and a variety of traces has helped us establish that Fingerprinter significantly accelerates the attack detection process.
OAR: a formal framework for multi-agent negotiation
Authors: Jiaying Shen, Ingo Weber, Victor Lesser
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

In Multi-Agent systems, agents often need to make decisions about how to interact with each other when negotiating over task allocation. In this paper, we present OAR, a formal framework to address the question of how the agents should interact in an evolving environment in order to achieve their different goals. The traditional categorization of self-interested and cooperative agents is unified by adopting a utility view. We illustrate mathematically that the degree of cooperativeness of an agent and the degree of its selfdirectness are not directly related. We also show how OAR can be used to evaluate different negotiation strategies and to develop distributed mechanisms that optimize the performance dynamically. This research demonstrates that sophisticated probabilistic modeling can be used to understand the behaviors of a system with complex agent interactions.