ppxscal/embedding_network huggingface dataset:
Mixed Key Management Using Hamming Distance for Mobile Ad-Hoc Networks
Authors: Seok-Lae Lee, In-Kyung Jeun, Joo-Seok Song
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

Unlike fixed networks, mobile ad-hoc networks have diverse characteristics, such as dynamic topologies, bandwidth-constrained links, energy-constrained operation, limited physical security, etc. Due to these characteristics their security requirements are different from those of fixed networks. This paper presents a method of authenticating the nodes in the ad-hoc network and securely transmitting information in consideration of the characteristics of the mobile ad hoc network. To this end, this paper proposes a method of combining asymmetric and symmetric cryptography to enhance the efficiency of secret key management for authentication and secure channel establishment. In particular, this paper proposes a method that introduces the concept of Hamming Distance to share the secret keys between the nodes. According to this proposal, secure communication between the nodes is possible when one node manages only log 2 N secret keys.
Constructing Phylogenetic Supernetworks from Quartets
Authors: Stefan Grünewald, Andreas Spillner, Kristoffer Forslund, Vincent Moulton
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

In phylogenetics it is common practice to summarize collections of partial phylogenetic trees in the form of supertrees. Recently it has been proposed to construct phylogenetic supernetworks as an alternative to supertrees as these allow the representation of conflicting information in the trees, information that may not be representable in a single tree. Here we introduce SuperQ, a new method for constructing such supernetworks. It works by breaking the input trees into quartet trees, and stitching together the resulting set to form a network. The stitching process is performed using an adaptation of the QNet method for phylogenetic network reconstruction. In addition to presenting the new method, we illustrate the applicability of SuperQ to three data sets and discuss future directions for testing and development.
SDWS: Semantic Search for Deep Web Data
Authors: Wei Fang, Zhiming Cui, Pengyu Hu, Li Huang, Pengpeng Zhao
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

A lot of high quality and wealthy data are hidden in backend database and search engines can not index this page, which is called Deep Web. It is mostly accessible through query interfaces. SDWS, a semantic search engine for Deep Web is presented. We are studying and implementing Semantic Web technology to the each process of Deep Web information integrated, and expertise in Deep Web discovering, annotating query results and integrating information. The novel approach promise better access to Deep Web.
A Multi-Objective Multipopulation Approach for Biclustering
Authors: Guilherme Palermo Coelho, Fabrício Olivetti França, Fernando J. Zuben
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Given that biclustering requires the optimization of two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, will be proposed in this paper. To illustrate the capabilities of this novel algorithm, MOM-aiNet was applied to the extraction of biclusters from two datasets, one taken from a well-known gene expression problem and the other from a collaborative filtering application. A comparative analysis has also been accomplished, with the obtained results being confronted with the ones produced by two popular biclustering algorithms from the literature (FLOC and CC) and also by another immune-inspired approach for biclustering (BIC-aiNet).
Semi-automatic Generation of CHR Solvers for Global Constraints
Authors: Frank Raiser
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Constraint programming often involves global constraints, for which various custom filtering algorithms have been published. This work presents a semi-automatic generation of CHR solvers for the subset of global constraints defineable by specific automata. The generation is based on a constraint logic program modelling an automaton and an improved version of the Prim-Miner algorithm. The solvers only need to be generated once and achieve arc-consistency for over 40 global constraints. 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
Making Remote `Meeting Hopping' Work: Assistance to Initiate, Join and Leave Meetings
Authors: Anita H. Cremers, Maaike Duistermaat, Peter L. Groenewegen, Jacomien G. Jong
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

Recently, it has become more and more common for colleagues and project teams to cooperate at a distance. In order for cooperation at a distance to really boom, it should be made easier to have ad hoc, short, informal meetings. Here it is important to receive cues about the availability of the person you wish to speak to. These cues are usually apparent in a situation of physical proximity, but they are not readily accessible at a distance. Also, attending formal meetings should be made more efficient and attractive, by allowing participants to just attend those parts of the meeting that are relevant to them. This `meeting hopping' should be organized in a way not detrimental to the ongoing meeting. This paper provides an exploration of how a virtual `meeting assistant' that could support remote meeting participants to initiate, join and leave both formal and informal meetings in a natural, non-obtrusive way should be designed, in the form of a scenario and some examples of user interfaces.
Applying the Graph Minor Theorem to the Verification of Graph Transformation Systems
Authors: Salil Joshi, Barbara König
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

We show how to view certain subclasses of (single-pushout) graph transformation systems as well-structured transition systems, which leads to decidability of the covering problem via a backward analysis. As the well-quasi order required for a well-structured transition system we use the graph minor ordering. We give an explicit construction of the backward step and apply our theory in order to show the correctness of a leader election protocol.
Weakly Supervised Cell Nuclei Detection and Segmentation on Tissue Microarrays of Renal Clear Cell Carcinoma
Authors: Thomas J. Fuchs, Tilman Lange, Peter J. Wild, Holger Moch, Joachim M. Buhmann
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

Renal cell carcinoma (RCC) is one of the ten most frequent malignancies in Western societies and can be diagnosed by histological tissue analysis. Current diagnostic rules rely on exact counts of cancerous cell nuclei which are manually counted by pathologists.We propose a complete imaging pipeline for the automated analysis of tissue microarrays of renal cell cancer. At its core, the analysis system consists of a novel weakly supervised classification method, which is based on an iterative morphological algorithm and a soft-margin support vector machine. The lack of objective ground truth labels to validate the system requires the combination of expert knowledge of pathologists. Human expert annotations of more than 2000 cell nuclei from 9 different RCC patients are used to demonstrate the superior performance of the proposed algorithm over existing cell nuclei detection approaches.
Feist and Facts: If Data Is Protected, Will It Be More or Less Available?
Authors: Michael Lesk
Venue: IEEE Security and Privacy (2003)

The US Congress is now considering the Database and Collections of Information Misappropriations Act (HR 3261), which is intended to provide federal protection for businesses that collect, publish, and sell databases. Until 1991, it was generally possible in the US to copyright databases, even of relatively routine information arranged in boring and obvious ways. Then the Supreme Court, ruling in Feist v. Rural Telephone Service, decided that white pages phone listings couldn't be copyrighted. The court held that copyright is intended to protect "creative acts and that alphabetizing names wasn't such an act. The Feist decision rejected the traditional view that "sweat of the brow labor was enough to justify copyright.
Toward a Robust System to Monitor Head Motions during PET Based on Facial Landmark Detection: A New Approach
Authors: Sergey Anishchenko, Vladislav Osinov, Dmitry Shaposhnikov, Lubov Podlachikova, Richard Comley, Xiaohong W. Gao
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

A new approach for the detection of head motions during PET scanning is presented. The proposed system includes 4 modules, which are: input module, face segmentation, facial landmark detection, and head movement estimation. The developed system is tested on pictures monitoring a subject's head while simulating PET scanning (n=12) and face images of subjects with different skin colours (n=31). Experimental results show that the centres of chosen facial landmarks (eye corners and middle point of nose basement) can be detected with high precision (1±0.64 pixels). Processing of 2D images with known moving parameters demonstrates that the parameter movement in terms of rotation and translation along X, Y, and Z directions can be obtained very accurately via the developed methods.
Formal Definition of Relevant Logical Symbol Occurrence
Authors: Noriaki Yoshiura
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

The formalization of human reasoning is a main issue in artificial intelligence. Although relevant logic is one of most useful ways of such formalization, inference of logic does not correspond to that of human reasoning because the aim of logical inference is to deal with the truth values of propositions while that of human reasoning is to discover useful information. We introduce the concept of logical symbol occurrence relevance into relevant logic for formalization of human reasoning. This paper discusses relevance of logical symbol occurrence and gives formal definition of relevance from the discussion. Moreover, we propose new logic where all logical symbol occurrences in theorems are relevant with respect to the relevance definition of this paper.
An Analysis of Interest-Community Facilitated Peer-to-Peer Search
Authors: Elth Ogston
Venue: Agents and Peer-to-Peer Computing: 5th International Workshop, AP2PC 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers (2006)

We study the effect of semantic overlay structure on the performance of decentralized search. Semantic overlays create communities of nodes that share particular interests. In peer-to-peer systems these communities can be designed to improve the recall of search algorithms. Such communities also play a role in balancing load between agents. An examination of these two performance metrics on some basic semantic overlay topologies shows that the choice of the best decentralized search algorithm can be influenced by differing design goals. We present an extensive experimental evaluation using data sets from eDonkey and Movielens. We find that, in general, these data sets do not exhibit obvious semantic clusters of nodes. For this reason, using a best-neighbors overlay, in which nodes individually choose their neighbors, to implement search produces better recall values than using an overlay that specifically clusters nodes into groups. Using best-neighbors overlays, on the other hand, can lead to highly unbalanced load distributions, a problem avoided in clustered overlays. We also find that forwarding search queries to "friends" in best-neighbors overlays does little to improve recall while further unbalancing load distributions.
Designing technology to aid cognition
Authors: Ron Baecker
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

We present a framework for technological aids for cognition intended primarily for individuals with cognitive impairments and seniors experiencing cognitive decline. We illustrate the framework with concrete research projects and near-term challenges.
Estimation, prediction and the Stein phenomenon under divergence loss
Authors: Malay Ghosh, Victor Mergel, Gauri Sankar Datta
Venue: Journal of Multivariate Analysis (2008)

We consider two problems: (1) estimate a normal mean under a general divergence loss introduced in [S. Amari, Differential geometry of curved exponential families - curvatures and information loss, Ann. Statist. 10 (1982) 357-387] and [N. Cressie, T.R.C. Read, Multinomial goodness-of-fit tests, J. Roy. Statist. Soc. Ser. B. 46 (1984) 440-464] and (2) find a predictive density of a new observation drawn independently of observations sampled from a normal distribution with the same mean but possibly with a different variance under the same loss. The general divergence loss includes as special cases both the Kullback-Leibler and Bhattacharyya-Hellinger losses. The sample mean, which is a Bayes estimator of the population mean under this loss and the improper uniform prior, is shown to be minimax in any arbitrary dimension. A counterpart of this result for predictive density is also proved in any arbitrary dimension. The admissibility of these rules holds in one dimension, and we conjecture that the result is true in two dimensions as well. However, the general Baranchick [A.J. Baranchick, a family of minimax estimators of the mean of a multivariate normal distribution, Ann. Math. Statist. 41 (1970) 642-645] class of estimators, which includes the James-Stein estimator and the Strawderman [W.E. Strawderman, Proper Bayes minimax estimators of the multivariate normal mean, Ann. Math. Statist. 42 (1971) 385-388] class of estimators, dominates the sample mean in three or higher dimensions for the estimation problem. An analogous class of predictive densities is defined and any member of this class is shown to dominate the predictive density corresponding to a uniform prior in three or higher dimensions. For the prediction problem, in the special case of Kullback-Leibler loss, our results complement to a certain extent some of the recent important work of Komaki [F. Komaki, A shrinkage predictive distribution for multivariate normal observations, Biometrika 88 (2001) 859-864] and George, Liang and Xu [E.I. George, F. Liang, X. Xu, Improved minimax predictive densities under Kullbak-Leibler loss, Ann. Statist. 34 (2006) 78-92]. While our proposed approach produces a general class of predictive densities (not necessarily Bayes, but not excluding Bayes predictors) dominating the predictive density under a uniform prior. We show also that various modifications of the James-Stein estimator continue to dominate the sample mean, and by the duality of estimation and predictive density results which we will show, similar results continue to hold for the prediction problem as well.
Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams
Authors: Yuan Lin, Hongyan Liu
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

In this paper, we present a new algorithm, Separator, for accurate and efficient Hierarchical Heavy Hitter (HHH) detection, an emerging research area of data stream mining. Existing algorithms exploit either bottom-up or top-down processing strategy to solve this problem, whereas we propose a novel combination of these two strategies. Based on this strategy and a devised compact data structure, we implement our algorithm. It is theoretically proved to have tight error bound and small space usage. Comprehensive experiments conducted also verify its accuracy and efficiency.
RAQNet: A Topology-Aware Overlay Network
Authors: Seyed Iman Mirrezaei, Javad Shahparian, Mohammad Ghodsi
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

Peer-to-peer overlay networks provide a useful infrastructure for building distributed applications. These networks provide efficient and fault-tolerant routing and object locating within a self-organizing overlay network. This paper presents a multi-dimensional overlay network called RAQNet which is based on RAQ. RAQ supports exact match queries and range queries over multi-dimensional data efficiently. Moreover, its routing cost does not depend on the dimension of the search space. In RAQNet, we have improved its original routing algorithms and extended it to have topology awareness property. In RAQNet, nodes are connected to each other if their labels are "close" to each other with respect to the topology of its underlying network. A topology match between the overlay and underlying network results in reduced routing delay and network link traffic. In comparison with RAQ, we will describe different node-join algorithms and routing table maintenance in order to provide the topology awareness. We present the experimental results through a prototype implementation of two emulated networks.
Facets of Adaptivity
Authors: Claudia Raibulet
Venue: Proceedings of the 2nd European conference on Software Architecture (2008)

Adaptivity is one of the key requirements of today's information systems. It is used in various areas which may range from control and operating systems to networks, from robotics to intelligent systems, from multimedia to information retrieval and Web Services. Essentially, it is related to changes performed in the systems at run-time. These changes may regard various aspects: architectural, structural, behavioral or content. Considering the diversity of its application areas and of ways of achievement, this paper aims to discuss the facets of adaptivity by raising the following questions: Why is it needed? Which are its objectives? Which are its main open research issues?
Decision Making System for Regulation of a Bimodal Urban Transportation Network, Associating a Classical and a Multi-Agent Approaches
Authors: Karim Bouamrane, Christian Tahon, Marc Sevaux, Bouziane Beldjilali
Venue: Informatica (2005)

To offer high quality services, when users are increasingly demanding and competition more and more hard, is now a major problem that transportation companies are faced with. So, ensuring a regular traffic needs to identify the randomly occurring disturbances that affect the transportation system and to eliminate or reduce their impacts on the traffic. This paper presents a decision support system TRSS (Traffic Regulation Support System). TRSS is a supervision environment for the regulation of urban transportation system. TRSS (tram and bus) is based on the regulation operator decision-making process. It provides the operator with the information he needs to identify disturbances and evaluate potential corrective actions to be carried out, according to the regulation strategy he has selected. The first part of the paper presents the decision model we work with. The second part deals with the functional model used in the decision support system. Decision support system for transportation and characteristics of a DSS for a transportation system are described in the third part. In the fourth part, we present the components of the decision-making TRSS supervision tool. In the fifth part, we present the criteria of evaluation and the sixth part is devoted to the presentation of the results.
Human-Centric Design of Percipient Knowledge Distribution Service
Authors: Peter Géczy, Noriaki Izumi, Shotaro Akaho, Kôiti Hasida
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

We present a functional design of behaviorally centered percipient knowledge distribution service for organizational systems. The service transparently embeds explicit knowledge into the web content; enabling easy access by users while they perform their browsing tasks. The service functionality emphasizes proper nonintrusive presentation of knowledge objects, and suitable alignment with the web content as well as organizational policies. The design utilizes analytic findings of human browsing behavior on a large scale organizational intranet portal. The findings imply that knowledge objects should contain at least two information components. One should be rapidly processable within seconds, and the other within few minutes. The knowledge objects should be presented at the target pages of users' browsing tasks. Functionality of the service should be personalized to account for user diversity.
Sparse Key Points Controlled Animation for Individual Face Model
Authors: Jian Yao, Yangsheng Wang, Bin Ding
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

We use RBF deformation and normal projection to regulate the scanned facial model, which makes their topology equivalent to a regular grid mesh and can generate principle components. Then the synthesized individual face model can be directly flatten to a regular plan, so the motion vectors of vertexes can be interpolated with barycentric coordinate. The regulation and animation remapping needs less than 40 key points and can work in real-time
An Improved SVM Classifier for Medical Image Classification
Authors: Yun Jiang, Zhanhuai Li, Longbo Zhang, Peng Sun
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

Support Vector Machine (SVM) has high classifying accuracy and good capabilities of fault-tolerance and generalization. The Rough Set Theory (RST) approach has the advantages on dealing with a large amount of data and eliminating redundant information. In this paper, we join SVM classifier with RST which we call the Improved Support Vector Machine (ISVM) to classify digital mammography. The experimental results show that this ISVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are close to 100% averagely.
What Should a Generic Emotion Markup Language Be Able to Represent?
Authors: Marc Schröder, Laurence Devillers, Kostas Karpouzis, Jean-Claude Martin, Catherine Pelachaud, Christian Peter, Hannes Pirker, Björn Schuller, Jianhua Tao, Ian Wilson
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Working with emotion-related states in technological contexts requires a standard representation format. Based on that premise, the W3C Emotion Incubator group was created to lay the foundations for such a standard. The paper reports on two results of the group's work: a collection of use cases, and the resulting requirements. We compiled a rich collection of use cases, and grouped them into three types: data annotation, emotion recognition, and generation of emotion-related behaviour. Out of these, a structured set of requirements was distilled. It comprises the representation of the emotion-related state itself, some meta-information about that representation, various kinds of links to the "rest of the world", and several kinds of global metadata. We summarise the work, and provide pointers to the working documents containing full details.