ppxscal/embedding_network huggingface dataset:
Social networks as an approach to the enhancement of collaboration among scientists
Authors: Richard Lackes, Markus Siepermann, Erik Frank
Venue: International Journal of Web Based Communities (2009)

The private use of online social networks is exceedingly popular. In business environments, web services of such kind as well as proprietary networking solutions are likewise deployed. Online social networks help people socialise and map business relations. In addition, employees use social web services to coordinate tasks and collaboratively manage projects. In the recent past, social networking emerged as a key communication channel in addition to traditional e-mail. However, our study shows that online social networks in research are not used to the same extent. The problem is that the existing solutions do not meet the requirements of the research community. In this article, we propose a conceptual design for the enhancement of online collaboration in the academic environment.
Conformant planning via symbolic model checking
Authors: Alessandro Cimatti, Marco Roveri
Venue: Journal of Artificial Intelligence Research (2000)

We tackle the problem of planning in nondeterministic domains, by presenting a new approach to conformant planning. Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal despite the nondeterminism of the domain. Our approach is based on the representation of the planning domain as a finite state automaton. We use Symbolic Model Checking techniques, in particular Binary Decision Diagrams, to compactly represent and efficiently search the automaton. In this paper we make the following contributions. First, we present a general planning algorithm for conformant planning, which applies to fully nondeterministic domains, with uncertainty in the initial condition and in action effects. The algorithm is based on a breadth-first, backward search, and returns conformant plans of minimal length, if a solution to the planning problem exists, otherwise it terminates concluding that the problem admits no conformant solution. Second, we provide a symbolic representation of the search space based on Binary Decision Diagrams (BDDs), which is the basis for search techniques derived from symbolic model checking. The symbolic representation makes it possible to analyze potentially large sets of states and transitions in a single computation step, thus providing for an efficient implementation. Third, we present CMBP (Conformant Model Based Planner), an efficient implementation of the data structures and algorithm described above, directly based on BDD manipulations, which allows for a compact representation of the search layers and an efficient implementation of the search steps. Finally, we present an experimental comparison of our approach with the state-of-the-art conformant planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems from the distribution packages of these systems, plus other problems defined to stress a number of specific factors. Our approach appears to be the most effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all the problems where a comparison is possible, CMBP outperforms its competitors, sometimes by orders of magnitude.
A Discussion System for Knowledge Sharing and Collaborative Analysis of Incidents in Nuclear Power Plants
Authors: Saizo Aoyagi, Hidenori Fujino, Hirotake Ishii, Hiroshi Shimoda, Hiroshi Sakuda, Hidekazu Yoshikawa, Toshio Sugiman
Venue: Proceedings of the 3d International Conference on Online Communities and Social Computing: Held as Part of HCI International 2009 (2009)

Incident analysis is an important activity to maintain the safety of nuclear power plants. Much discussion is required to utilize the collected incidents effectively in the incident analysis activity. On-line Computer Mediated Communication(CMC) activity is an appropriate circumstance for geographically dispersed workers in nuclear power plants to discuss about the incident analysis. Some studies, however, indicate that the discussion activity in CMC tends to stagnate after a short period of time. For this study, the authors developed a discussion system for knowledge sharing and collaborative analysis of incidents, and proposed a method to promote discussion among users through introduction of "Active Participant". The Active Participant always behaves actively, and such behaviors are expected to promote the other members' incident analysis in the discussion group. To evaluate the effectiveness of the introduction of the Active Participant and obtain concrete guidance of the Active Participant, an experiment was conducted with nuclear power plant workers who were asked to evaluate the discussion system. The results of the experiment show that Active Participant can promote discussion among group members if enough number of incidents are submitted to the discussion system.
A Tableau Calculus for Regular Grammar Logics with Converse
Authors: Linh Anh Nguyen, Andrzej Szałas
Venue: Proceedings of the 22nd International Conference on Automated Deduction (2009)

We give a sound and complete tableau calculus for deciding the general satisfiability problem of regular grammar logics with converse (REG c logics). Tableaux of our calculus are defined as "and-or" graphs with global caching. Our calculus extends the tableau calculus for regular grammar logics given by Goré and Nguyen [11] by using a cut rule and existential automaton-modal operators to deal with converse. We use it to develop an ExpTime (optimal) tableau decision procedure for the general satisfiability problem of REG c logics. We also briefly discuss optimizations for the procedure.
A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network
Authors: Mohd Zakree Nazri, Siti Mariyam Shamsuddin, Azuraliza Abu Bakar, Salwani Abdullah
Venue: Proceedings of the 8th International Conference on Artificial Immune Systems (2009)

The human immune system provides inspiration in the attempt of solving the knowledge acquisition bottleneck in developing ontology for semantic web application. In this paper, we proposed an extension to the Guided Agglomerative Hierarchical Clustering (GAHC) method that uses an Artificial Immune Network (AIN) algorithm to improve the process of automatically building and expanding the concept hierarchy. A small collection of Malay text is used from three different domains which are IT, Biochemistry and Fiqh to test the effectiveness of the proposed approach and also by comparing it with GAHC. The proposed approach consists of three stages: pre-processing, concept hierarchy induction using GAHC and concept hierarchy learning using AIN. To validate our approach, the automatically learned concept hierarchy is compared to a reference ontology developed by human experts. Thus it can be concluded that the proposed approach has greater ability to be used in learning concept hierarchy.
A Game Theory Approach to Detect Malicious Nodes in Wireless Sensor Networks
Authors: Yenumula B. Reddy
Venue: Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications (2009)

Wireless sensor networks has grown a lot in recent years and offers excellent opportunities in defense related applications such as monitoring environment and collecting data related to antisocial activities. Currently, the needs of wireless sensors have become inevitable in daily life. With the continuous growth of Wireless sensor networks in daily life, business, and defense applications, the security of transferring data from sensors to their destination has become an important research area. Due to the limitations of power, storage, and processing capabilities, current security mechanisms of wireless networks or wired networks can not apply directly to wireless sensor networks. So there is a need to develop new techniques or modify the current security mechanisms to transfer data from source (from the field) to base station (destination). In this paper, we discuss currently available intrusion detection techniques, attack models using game theory, and then propose a new framework to detect malicious nodes using zero sum game approach for nodes in the forward data path. The first part of research provides the game model with probability of energy required for transferring the data packets. The second part derives the model to detect the malicious nodes using probability of acknowledgement at source.
A parallel computer implementation of the Asymptotic Numerical Method to study thermal convection instabilities
Authors: Marc Medale, Bruno Cochelin
Venue: Journal of Computational Physics (2009)

We have developed a numerical model to efficiently compute steady-state combined buoyancy and thermocapillary convection solutions. It features a parallel computer implementation of an Asymptotic Numerical Method to perform steady-state path-following and locate bifurcation points in problems involving large size algebraic systems, up to few million degrees of freedom. The model has been first validated on a problem for which a reference solution exists and then is used to analyse the influence of the container size and shape on Rayleigh-Benard-Marangoni convection and its related cellular pattern.
Checking Thorough Refinement on Modal Transition Systems Is EXPTIME-Complete
Authors: Nikola Beneš, Jan Křetínský, Kim G. Larsen, Jiří Srba
Venue: Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing (2009)

Modal transition systems (MTS), a specification formalism introduced more than 20 years ago, has recently received a considerable attention in several different areas. Many of the fundamental questions related to MTSs have already been answered. However, the problem of the exact computational complexity of thorough refinement checking between two finite MTSs remained unsolved.We settle down this question by showing EXPTIME-completeness of thorough refinement checking on finite MTSs. The upper-bound result relies on a novel algorithm running in single exponential time providing a direct goal-oriented way to decide thorough refinement. If the right-hand side MTS is moreover deterministic, or has a fixed size, the running time of the algorithm becomes polynomial. The lower-bound proof is achieved by reduction from the acceptance problem of alternating linear bounded automata and the problem remains EXPTIME-hard even if the left-hand side MTS is fixed.
Intranet satisfaction questionnaire: Development and validationof a questionnaire to measure user satisfaction with the Intranet
Authors: Javier A. Bargas-Avila, Jonas Lötscher, Sébastien Orsini, Klaus Opwis
Venue: Computers in Human Behavior (2009)

In recent years, Intranets have become increasingly important to their companies. Substantial investments have been made to provide crucial information and workflows to employees. In this context the question of quality assurance arises: how can user satisfaction with the Intranet be measured? This article presents the development of a questionnaire to measure user satisfaction with the Intranet. After a first validation of the instrument (18 items) in an international insurance company (N"1=881), a final set of 13 items remained. These were tested with the Intranet of a national retail company (N"2=1350). The final version showed a high internal consistency (Cronbach @a) of .89, good item difficulties (.36-.73) and discriminatory power coefficients (.48-.73), as well as a moderate average homogeneity of .44. An exploratory factor analysis revealed two factors, ''Content Quality'' and ''Intranet Usability'', explaining 56.54% of the variance. Meanwhile, the survey was translated into 10 languages: Chinese, English, French, German, Italian, Japanese, Portuguese, Russian, Slovenian, and Spanish.
Initialization of the Neighborhood EM Algorithm for Spatial Clustering
Authors: Tianming Hu, Ji Ouyang, Chao Qu, Chuanren Liu
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Like other iterative refinement clustering algorithms, the Neighborhood Expectation-Maximization (NEM) algorithm is sensitive to the initial state of cluster separation. Therefore, the study of the initialization methods is of great importance for the success of finding a better sub-optimal solution in practice. However, existing initialization methods for mixture model based clustering using EM-style algorithms do not account for the unique properties of spatial data, such as spatial autocorrelation. To that end, this paper incorporates spatial information into the initialization and compares three representative initialization methods. Our experimental results on both synthetic and real-world datasets show that the NEM algorithm usually leads to a better clustering solution if it starts with initial states returned by the spatial augmented initialization method based on K-Means.
Bayesian Registration via Local Image Regions: Information, Selection and Marginalization
Authors: Matthew Toews, William M. Wells,  Iii
Venue: Proceedings of the 21st International Conference on Information Processing in Medical Imaging (2009)

We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in an image-guided neurosurgical application.
MHC: Peptide Analysis: Implications on the Immunogenicity of Hantaviruses' N protein
Authors: Maurício Menegatti Rigo, Dinler Amaral Antunes, Gustavo Fioravanti Vieira, José Artur Chies
Venue: Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology (2009)

Hantaviruses, members of the Bunyaviridae family, are enveloped negative-stranded RNA viruses with tripartite genomes --- S, M and L. The S genome codes for a nucleocapsid (N) protein, which is quite conserved among different species of the hantavirus genus and possess a recognized immunogenicity. In this work we analyzed the sequence of two regions in this protein (N94 ¿ 101 and N180 ¿ 188), which presents T cell epitopes for two species of hantaviruses --- Sin Nombre and Puumala. Interestingly, the same region has no described epitopes for Hantaan virus, despite its similarity. A study using a bioinformatic approach for the construction of MHC:peptide complexes was performed to detect any variation on the cleft region that could explain such degrees of immunogenicity. Our results shown topological and charges differences among the constructed complexes.
Efficient metadeliberation auctions
Authors: Ruggiero Cavallo, David C. Parkes
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Imagine a resource allocation scenario in which the interested parties can, at a cost, individually research ways of using the resource to be allocated, potentially increasing the value they would achieve from obtaining it. Each agent has a private model of its research process and obtains a private realization of its improvement in value, if any. From a social perspective it is optimal to coordinate research in a way that strikes the right tradeoff between value and cost, ultimately allocating the resource to one party- thus this is a problem of multi-agent metadeliberation. We provide a reduction of computing the optimal deliberation-allocation policy to computing Gittins indices in multi-anned bandit worlds, and apply a modification of the dynamic-VCG mechanism to yield truthful participation in an ex post equilibrium. Our mechanism achieves equilibrium implementation ofthe optimal policy even when agents have the capacity to deliberate about other agents' valuations, and thus addresses the problem of strategic deliberation.
Using Temporal Constraints to Integrate Signal Analysis and Domain Knowledge in Medical Event Detection
Authors: Feng Gao, Yaji Sripada, Jim Hunter, François Portet
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

The events which occur in an Intensive Care Unit (ICU) are many and varied. Very often, events which are important to an understanding of what has happened to the patient are not recorded in the electronic patient record. This paper describes an approach to the automatic detection of such unrecorded 'target' events which brings together signal analysis to generate temporal patterns, and temporal constraint networks to integrate these patterns with other associated events which are manually or automatically recorded. This approach has been tested on real data recorded in a Neonatal ICU with positive results.
Medical Terminology Online for Mastering Healthcare Terminology - Spiral Bound: User Guide, Access Code, Spiral Bound Textbook, 3rd edition
Authors: Betsy J. Shiland
Venue:  (2009)

This money saving package includes Shiland: Mastering Healthcare Terminology,3e + Shiland: iTerms Audio for Mastering Healthcare Terminology - Retail Pack,3e + Shiland: Medical Terminology Online for Mastering Healthcare Terminology (User Guide and Access Code)
Improving simultaneous mapping and localization in 3D using global constraints
Authors: Rudolph Triebel, Wolfram Burgard
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

Recently, the problem of learning volumetric maps from three-dimenisional range data has become quite popular in the context of mobile robotics. One of the key challenges in this context is to reduce the overall amount of data. The smaller the namber of data points, however, the fewer information is available to register the scans and to conputer a consistent map. In this paper we present a novel approach that estimates global constaints from the data and utilizes these contraints to improve the registration process. In our current system we simultaneously minimize the distance between scans and the distance of edges from planes extracted from the edges to obtain highly accurate three-dimensional modele of the environment. Several experiments carried out in simulation as well as with three-dimensional data obtained with a mobile robot in an outdoor environment we show that our approach yields seriously more accurate models compared to a standard apporach that does not utilize the global constraints.
Name-it: naming and detecting faces in video by the integration of image and natural language processing
Authors: Shin'ichi Satoh, Yuichi Nakamura, Takeo Kanade
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

We have been developing Name-It, a system that associates faces and names in news videos. First, as the only knowledge source, the system is given news videos which include image sequences and transcripts obtained from audio tracks or closed caption texts. The system can then either infer the name of a given face and output the name candidates, or can locate the faces in news videos by a name. To accomplish this task, the system extracts faces from image sequences and names from transcripts, both of which might correspond to key persons in news topics. The proposed system takes full advantage of advanced image and natural language processing. The image processing contributes to the extraction of face sequences which provide rich information for face-name association. The processing also helps to select the best frontal view of a face in a face sequence to enhance the face identification which is required for the processing. On the other hand, the natural language processing effectively extracts names by using lexical/grammatical analysis and knowledge of the news video topics structure. The success of our experiments demonstrates the benefits of the advanced image and natural language processing methods and their incorporation.
Free-form Sketching with Ball B-Splines
Authors: Rongqing Song, Zhongke Wu, Mingquan Zhou, Xuefeng Ao
Venue: Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments (2009)

Quickly and conveniently generating a 3D freeform model is a challenging problem in computer graphics field. This paper proposes a new approach for rapid building 3D freeform shapes through sketching based on ball B-splines.
Incremental Learning Algorithm for Online Action Game System
Authors: Junichi Hoshino, Hiroshi Mori
Venue: Proceedings of the 8th International Conference on Entertainment Computing (2009)

One of the limitations of computer opponents in action games is that the character AI is constructed in advance, and players may become bored quickly. We have built an online action game system in which a non-player character (NPC) can incrementally learn sequences of action and combinations. NPCs can adopt different fighting strategies for fighting with different players.
A secure and improved self-embedding algorithm to combat digital document forgery
Authors: Abbas Cheddad, Joan Condell, Kevin Curran, Paul Mc Kevitt
Venue: Signal Processing (2009)

The recent digital revolution has facilitated communication, data portability and on-the-fly manipulation. Unfortunately, this has brought along some critical security vulnerabilities that put digital documents at risk. The problem is in the security mechanism adopted to secure these documents by means of encrypted passwords; however, this security shield does not actually protect the documents which are stored intact. We propose here a solution to this real world problem through a 1D hash algorithm coupled with 2D iFFT (irreversible Fast Fourier Transform) to encrypt digital documents in the 2D spatial domain. Further by applying an imperceptible information hiding technique we can add another security layer which is resistant to noise and to a certain extent JPEG compression. We support this assertion by showing a practical example which is drawn from our set of experiments. This work exploits Jarvis' kernel to generate the error diffusion signal and the Wavelet-based Inverse Halftoning via De-convolution (WInHD) to recover the approximation of the original signal. Our method not only points out forgery but also allows legal or forensics expert gain access to the original document despite being manipulated. This would undoubtedly be very useful in cases of disputes or claims.
Analog IC Fault Diagnosis based on Wavelet Neural Network Ensemble
Authors: Zuo Lei, Wang Jinhui, Hou Ligang, Geng Shuqin, Wu Wuchen
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

A fault diagnosis method for analog IC diagnosis based on wavelet neural network ensemble (WNNE) and Adaboost algorithm, is proposed in this paper. This makes the way of the directory be of use in fault, and enhances the validity of the fault diagnosis. Using wavelet decomposition as a tool for extracting feature, Then, after training the WNNE by faulty feature vectors, the model of the circuit with fault diagnosis system is built. Simulation results have shown that this claim is more effective than wavelet neural network (WNN).