ppxscal/embedding_network huggingface dataset:
A Power Saving Scheme for Heterogeneous Wireless Access Networks
Authors: Sukyoung Lee, Laeyoung Kim, Hojin Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In integrated WLAN and cellular networks, we propose a power saving scheme that completely turns off WLAN interface in the idle state and wakes it up when there is incoming data from long-lived traffic. We also develop a Mobile IPv6 (MIPv6)-based signaling to turn on the WLAN interface only for long-lived traffic. It is shown via simulations that the proposed power saving scheme improves power efficiency over typical WLAN system.
Identifying Maps on the World Wide Web
Authors: Matthew Michelson, Aman Goel, Craig A. Knoblock
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

This paper presents an automatic approach to mining collections of maps from the Web. Our method harvests images from the Web and then classifies them as maps or non-maps by comparing them to previously classified map and non-map images using methods from Content-Based Image Retrieval (CBIR). Our approach outperforms the accuracy of the previous approach by 20% in F1-measure. Further, our method is more scalable and less costly than previous approaches that rely on more traditional machine learning techniques.
Distributed Adaptive Design with Hierarchical Autonomous Graph Transformation Systems
Authors: Leszek Kotulski, Barbara Strug
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

In this paper a graph transformation system with the parallel derivation is used to model the process of distribution and adaptation for computer aided design. It is based on earlier research in formal language theory, especially graph grammars, and distributed models. The motivation for the ideas presented here is given and possible ways of application are described. A connection to the multi-agent model is also presented.
Flexible Grid-Based Clustering
Authors: Marc-Ismaël Akodjènou-Jeannin, Kavé Salamatian, Patrick Gallinari
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Grid-based clustering is particularly appropriate to deal with massive datasets. The principle is to first summarize the dataset with a grid representation, and then to merge grid cells in order to obtain clusters. All previous methods use grids with hyper-rectangular cells. In this paper we propose a flexible grid built from arbitrary shaped polyhedra for the data summary. For the clustering step, a graph is then extracted from this representation. Its edges are weighted by combining density and spatial informations. The clusters are identified as the main connected components of this graph. We present experiments indicating that our grid often leads to better results than an adaptive rectangular grid method.
Graph Parser Combinators
Authors: Steffen Mazanek, Mark Minas
Venue: Implementation and Application of Functional Languages: 19th International Workshop, IFL 2007, Freiburg, Germany, September 27-29, 2007. Revised Selected Papers (2008)

A graph language can be described by a graph grammar in a manner similar to a string grammar known from the theory of formal languages. Unfortunately, graph parsing is known to be computationally expensive in general. There are quite simple graph languages that crush most general-purpose graph parsers.In this paper we present graph parser combinators, a new approach to graph parsing inspired by the well-known string parser combinators. The basic idea is to define primitive graph parsers for elementary graph components and a set of combinators for the construction of more advanced graph parsers. Using graph parser combinators special-purpose graph parsers can be composed conveniently. Thereby, language-specific performance optimizations can be incorporated in a flexible manner.
Programmable Hash Functions and Their Applications
Authors: Dennis Hofheinz, Eike Kiltz
Venue: Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology (2008)

We introduce a new information-theoretic primitive called programmable hash functions (PHFs). PHFs can be used to program the output of a hash function such that it contains solved or unsolved discrete logarithm instances with a certain probability. This is a technique originally used for security proofs in the random oracle model. We give a variety of standard model realizations of PHFs (with different parameters).The programmability of PHFs make them a suitable tool to obtain black-box proofs of cryptographic protocols when considering adaptive attacks. We propose generic digital signature schemes from the strong RSA problem and from some hardness assumption on bilinear maps that can be instantiated with any PHF. Our schemes offer various improvements over known constructions. In particular, for a reasonable choice of parameters, we obtain short standard model digital signatures over bilinear maps.
Optimal Joint Rate and Power Allocation in CDMA Networks
Authors: Richard J. Boucherie, Aloysius Irwan Endrayanto, Adriana Felicia Gabor
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

In this paper we propose a polynomial time algorithm for the optimal rate and power allocation problem in a two cell CDMA network. We assume continuous rates and limited powers for the base stations.
Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints
Authors: Yi Guo, Paul W. Kwan, Junbin Gao
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Biometric data like fingerprints are often highly structured and of high dimension. The "curse of dimensionality" poses great challenge to subsequent pattern recognition algorithms including neural networks due to high computational complexity. A common approach is to apply dimensionality reduction (DR) to project the original data onto a lower dimensional space that preserves most of the useful information. Recently, we proposed Twin Kernel Embedding (TKE) that processes structured or non-vectorial data directly without vectorization. Here, we apply this method to clustering and visualizing fingerprints in a 2-dimensional space. It works by learning an optimal kernel in the latent space from a distance metric defined on the input fingerprints instead of a kernel. The outputs are the embeddings of the fingerprints and a kernel Gram matrix in the latent space that can be used in subsequent learning procedures like Support Vector Machine (SVM) for classification or recognition. Experimental results confirmed the usefulness of the proposed method.
Towards a Dependency Constraint Language to Manage Software Architectures
Authors: Ricardo Terra, Marco Tulio Oliveira Valente
Venue: Proceedings of the 2nd European conference on Software Architecture (2008)

This paper presents a dependency constraint language that allows software architects to restrict the spectrum of dependencies that can be presented in a given software system. The ultimate goal is to provide designers with means to define acceptable and unacceptable dependencies according to the planned system architecture. Once defined, such restrictions will be automatically enforced by a tool, thus avoiding silent erosions in the architecture. The paper also presents first results of applying the language in a Web-based system.
Maximum Resolution Topology for Multi-Attribute Dichotomies
Authors: James K. Ho, Sydney C. K. Chu
Venue: Informatica (2005)

The Star Plot approach to high-dimensional data visualization is applied to multi-attribute dichotomies. It is observed that the areas of the plot for the two parts of a dichotomy may be used as an aggregate measure of their relative dominance. An optimization model is developed to determine a topology (or weighted configuration of the attributes) that maximizes the resolution of this measure with respect to a given set of reference dichotomies.
Comparing Learners' Affect While Using an Intelligent Tutoring System and a Simulation Problem Solving Game
Authors: Ma. Mercedes Rodrigo, Ryan S. Baker, Sidney D'Mello, Ma. Celeste Gonzalez, Maria C. Lagud, Sheryl A. Lim, Alexis F. Macapanpan, Sheila A. Pascua, Jerry Q. Santillano, Jessica O. Sugay, Sinath Tep, Norma J. Viehland
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

We compare the affect associated with an intelligent tutoring environment, Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplusix than it was in The Incredible Machine. Students experienced significantly less boredom and frustration and more flow while using Aplusix. This implies that, while aspects unique to games (e.g. fantasy and competition) may make games more fun, the interactivity and challenge common to both games and ITSs may play a larger role in making both types of systems affectively positive learning environments.
Towards Data Mining Without Information on Knowledge Structure
Authors: Alexandre Vautier, Marie-Odile Cordier, René Quiniou
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Most knowledge discovery processes are biased since some part of the knowledge structure must be given before extraction. We propose a framework that avoids this bias by supporting all major model structures e.g. clustering, sequences, etc., as well as specifications of data and DM (Data Mining) algorithms, in the same language. A unification operation is provided to match automatically the data to the relevant DM algorithms in order to extract models and their related structure. The MDL principle is used to evaluate and rank models. This evaluation is based on the covering relation that links the data to the models. The notion of schema, related to the category theory, is the key concept of our approach. Intuitively, a schema is an algebraic specification enhanced by the union of types, and the concepts of list and relation. An example based on network alarm mining illustrates the process.
An Improved Algorithm for Eleman Neural Network by Adding a Modified Error Function
Authors: Zhiqiang Zhang, Zheng Tang, Guofeng Tang, Vairappan Catherine, Xugang Wang, Runqun Xiong
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

The Eleman Neural Network has been widely used in various fields ranging from temporal version of the Exclusive-OR function to the discovery of syntactic categories in natural language date. However, one of the problems often associated with this type of network is the local minima problem which usually occurs in the process of the learning. To solve this problem, we have proposed an error function which can harmonize the update weights connected to the hidden layer and those connected to the output layer by adding one term to the conventional error function. It can avoid the local minima problem caused by this disharmony. We applied this method to the Boolean Series Prediction Questions problems to demonstrate its validity. The result shows that the proposed method can avoid the local minima problem and largely accelerate the speed of the convergence and get good results for the prediction tasks.
Dimensionality Reduction and Linear Discriminant Analysis for Hyperspectral Image Classification
Authors: Qian Du, Nicolas H. Younan
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In this paper, we investigate the application of Fisher's linear discriminant analysis (FLDA) to hyperspectral remote sensing image classification. The basic idea of FLDA is to design an optimal transform so that the classes can be well separated in the low-dimensional space. The practical difficulty of applying FLDA to hyperspectral images includes the unavailability of enough training samples and unknown information for all the classes present. So the original FLDA is modified to avoid the requirements of complete class knowledge, such as the number of actual classes present. We also investigate the performance of the class of principal component analysis (PCA) techniques prior to FLDA and find that the interference and noise adjusted PCA (INAPCA) can provide the improvement in the final classification.
Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge
Authors: Ranga Chandra Gudivada, Xiaoyan A. Qu, Jing Chen, Anil G. Jegga, Eric K. Neumann, Bruce J. Aronow
Venue: Journal of Biomedical Informatics (2008)

Most common chronic diseases are caused by the interactions of multiple factors including the influences and responses of susceptibility and modifier genes that are themselves subject to etiologic events, interactions, and environmental factors. These entities, interactions, mechanisms, and phenotypic consequences can be richly represented using graph networks with semantically definable nodes and edges. To use this form of knowledge representation for inferring causal relationships, it is critical to leverage pertinent prior knowledge so as to facilitate ranking and probabilistic treatment of candidate etiologic factors. For example, genomic studies using linkage analyses detect quantitative trait loci that encompass a large number of disease candidate genes. Similarly, transcriptomic studies using differential gene expression profiling generate hundreds of potential disease candidate genes that themselves may not include genetically variant genes that are responsible for the expression pattern signature. Hypothesizing that the majority of disease-causal genes are linked to biochemical properties that are shared by other genes known to play functionally important roles and whose mutations produce clinical features similar to the disease under study, we reasoned that an integrative genomics-phenomics approach could expedite disease candidate gene identification and prioritization. To approach the problem of inferring likely causality roles, we generated Semantic Web methods-based network data structures and performed centrality analyses to rank genes according to model-driven semantic relationships. Our results indicate that Semantic Web approaches enable systematic leveraging of implicit relations hitherto embedded among large knowledge bases and can greatly facilitate identification of centrality elements that can lead to specific hypotheses and new insights.
Geolocation-Based Trust for Vanet's Privacy
Authors: Jetzabel Serna, Jesus Luna, Manel Medina
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

Research in Vehicular Ad Hoc NETworks (VANETs) has evolved considerably over the last years. Security and privacy in VANETs have recently appealed special interest in the research community. In this paper we overview the main privacy concepts and explain why this concept is fundamental for wide adoption of VANETs. A set of privacy requirements for VANETs are established and studied, towards proposing a novel mechanism beyond the use of pseudonyms. In particular, this research demonstrates that there are still several challenges concerning privacy which solution is feasible to be extrapolated from highly demanding environments like e-Health. This paper reports our work in progress mainly describing the basis of a privacy mechanism that uses an authorization paradigm based on a Mandatory Access Control model and a novel mechanism that propagates trust information based on a vehicle's geolocation
Changing Legal Systems: Abrogation and Annulment Part I: Revision of Defeasible Theories
Authors: Guido Governatori, Antonino Rotolo
Venue: Proceedings of the 9th international conference on Deontic Logic in Computer Science (2008)

In this paper we investigate how to model legal abrogation and annulment in Defeasible Logic. We examine some options that embed in this setting, and similar rule-based systems, ideas from belief and base revision. In both cases, our conclusion is negative, which suggests to adopt a different logical model.
Detecting Anomalous Traffic Using Statistical Discriminator and Neural Decisional Motor
Authors: Paola Baldassarri, Anna Montesanto, Paolo Puliti
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

One of the main challenges in the information security concerns the introduction of systems able to identify intrusions. In this ambit this work takes place describing a new Intrusion Detection System based on anomaly approach. We realized a system with a hybrid solution between host-based and network-based approaches, and it consisted of two subsystems: a statistical system and a neural one. The features extracted from the network traffic belong only to the IP Header and their trend allows us detecting through a simple visual inspection if an attack occurred. Really the two-tier neural system has to indicate the status of the system. It classifies the traffic of the monitored host, distinguishing the background traffic from the anomalous one. Besides, a very important aspect is that the system is able to classify different instances of the same attack in the same class, establishing which attack occurs.
The Early Years of Programming in Switzerland
Authors: H. R. Schwarz
Venue: IEEE Annals of the History of Computing (1981)

The computer-aided era in Switzerland started in 1950 with the relay machine Z4. Its main properties are described. and the numerical work carried out on it is presented. The ideas pursued and the basic discoveries realized are discussed. During the operation of the Z4, the staff of the Institute for Applied Mathematics at ETH designed the electronic computer ERMETH. Its properties as well as the most important numerical investigations of the years from 1956 to 1962 are outlined, including the work on algorithmic languages and compilers. 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
Performance measurement and analysis of large-scale parallel applications on leadership computing systems
Authors: Brian J. N. Wylie, Markus Geimer, Felix Wolf
Venue: Scientific Programming (2008)

Developers of applications with large-scale computing requirements are currently presented with a variety of high-performance systems optimised for message-passing, however, effectively exploiting the available computing resources remains a major challenge. In addition to fundamental application scalability characteristics, application and system peculiarities often only manifest at extreme scales, requiring highly scalable performance measurement and analysis tools that are convenient to incorporate in application development and tuning activities. We present our experiences with a multigrid solver benchmark and state-of-the-art real-world applications for numerical weather prediction and computational fluid dynamics, on three quite different multi-thousand-processor supercomputer systems - Cray XT3/4, MareNostrum & Blue Gene/L - using the newly-developed SCALASCA toolset to quantify and isolate a range of significant performance issues.
Precise Interval Analysis vs. Parity Games
Authors: Thomas Gawlitza, Helmut Seidl
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

In [8], a practical algorithm for precise interval analysis is provided for which, however, no non-trivial upper complexity bound is known. Here, we present a lower bound by showing that precise interval analysis is at least as hard as computing the sets of winning positions in parity games. Our lower-bound proof relies on an encoding of parity games into systems of particular integer equations. Moreover, we present a simplification of the algorithm for integer systems from [8]. For the given encoding of parity games, the new algorithm provides another algorithm for parity games which is almost as efficient as the discrete strategy improvement algorithm by Vöge and Jurdziński [17].
Quantum Wireless Sensor Networks
Authors: Naya Nagy, Marius Nagy, Selim G. Akl
Venue: Proceedings of the 7th international conference on Unconventional Computing (2008)

Security in sensor networks, though an important issue for widely available wireless networks, has been studied less extensively than other properties of these networks, such as, for example, their reliability. The few security schemes proposed so far are based on classical cryptography. In contrast, the present paper develops a totally new security solution, based on quantum cryptography. The scheme developed here comes with the advantages quantum cryptography has over classical cryptography, namely, effectively unbreakable keys and therefore unbreakable messages. Our security system ensures privacy of the measured data field in the presence of an intruder listening to messages broadcasted in the field.
Towards Diagnostic Simulation in Sensor Networks
Authors: Mohammad Maifi Khan, Tarek Abdelzaher, Kamal Kant Gupta
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

While deployment and practical on-site testing remains the ultimate touchstone for sensor network code, good simulation tools can help curtail in-field troubleshooting time. Unfortunately, current simulators are successful only at evaluating system performance and exposing manifestations of errors. They are not designed to diagnose the root cause of the exposed anomalous behavior. This paper presents a diagnostic simulator, implemented as an extension to TOSSIM [6]. It (i) allows the user to ask questions such as "why is (some specific) bad behavior occurring?", and (ii) conjectures on possible causes of the user-specified behavior when it is encountered during simulation. The simulator works by logging event sequences and states produced in a regular simulation run. It then uses sequence extraction, and frequent pattern analysis techniques to recognize sequences and states that are possible root causes of the user-defined undesirable behavior. To evaluate the effectiveness of the tool, we have implemented the directed diffusion protocol and used our tool during the development process. During this process the tool was able to uncover two design bugs that were not addressed in the original protocol. The manifestation of these two bugs were same but the causes of failure were completely different - one was triggered by node reboot and the other was triggered by an overflow of timestamps generated by the local clock. The case study demonstrates a success scenario for diagnostic simulation.
An Experimental Use of Learning Environment for Problem-Posing as Sentence-Integration in Arithmetical Word Problems
Authors: Tsukasa Hirashima, Takuro Yokoyama, Masahiko Okamoto, Akira Takeuchi
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

We have developed a computer-based environment for learning by problem-posing as sentence-integration. The system was evaluated through two months usage in classroom by the ninety-nine students belonging to three classes of an elementary school second grade. As the results, we found that (1) the second grade students of elementary school had posed problems continuously with the system, and (2) both students and teachers answered questionnaires that the problem-posing activity using this system was useful for learning. Moreover, we confirmed that (3) our system improved the problem solving ability of low performance students.