ppxscal/embedding_network huggingface dataset:
Propositional independence: formula-variable independence and forgetting
Authors: Jôme Lang, Paolo Liberatore, Pierre Marquis
Venue: Journal of Artificial Intelligence Research (2003)

Independence - the study of what is relevant to a given problem of reasoning - has received an increasing attention from the AI community. In this paper, we consider two basic forms of independence, namely, a syntactic one and a semantic one. We show features and drawbacks of them. In particular, while the syntactic form of independence is computationally easy to check, there are cases in which things that intuitively are not relevant are not recognized as such. We also consider the problem of forgetting, i.e., distilling from a knowledge base only the part that is relevant to the set of queries constructed from a subset of the alphabet. While such process is computationally hard, it allows for a simplification of subsequent reasoning, and can thus be viewed as a form of compilation: once the relevant part of a knowledge base has been extracted, all reasoning tasks to be performed can be simplified.
Application of Improved Differential Evolution Approach on Parameter Identification of Chen and Lü Chaotic Systems
Authors: Wen-Hsien Ho, Shinn-Horng Chen, Jyh-Horng Chou, Tung-Kuan Liu, Ming-Der Jean, Cheng-Ming Li
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01 (2009)

In this paper, a Taguchi-sliding-level differential evolution algorithm (TSLDEA) is proposed to use for finding the actual parameters of Chen and Lü chaotic systems. The TSLDEA, a powerful global numerical optimization method, combines the differential evolution algorithm (DEA) with the Taguchi-sliding-level (TSL) method. The TSL method is fused in the crossover operation of a DEA. Then, the systematic reasoning ability of the TSL method is incorporated in the crossover operation of a DEA to select the better offspring to achieve the crossover, and consequently enhance the DEA. Therefore, the TSLDEA can be more robust, statistically sound, and quickly convergent. Two illustrative examples of parameter identification of Chen and Lü chaotic systems are given to demonstrate the applicability of the proposed TSLDEA.
A probabilistic framework for automatic term recognition
Authors: Wilson Wong, Wei Liu, Mohammed Bennamoun
Venue: Intelligent Data Analysis (2009)

Term recognition identifies domain-relevant terms which are essential for discovering domain concepts and for the construction of terminologies required by a wide range of natural language applications. Many techniques have been developed in an attempt to numerically determine or quantify termhood based on term characteristics. Some of the apparent shortcomings of existing techniques are the ad-hoc combination of termhood evidence, mathematically-unfounded derivation of scores and implicit assumptions concerning term characteristics. We propose a probabilistic framework for formalising and combining qualitative evidence based on explicitly defined term characteristics to produce a new termhood measure. Our qualitative and quantitative evaluations demonstrate consistently better precision, recall and accuracy compared to three other existing ad-hoc measures.
A Method to Detect an Atmosphere of "Involvement, Enjoyment, and/or Excitement" in Multi-user Interaction
Authors: Yoshimasa Ohmoto, Takashi Miyake, Toyoaki Nishida
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

In multi-user interaction, Embodied Conversational Agents (ECAs) have to detect mental states of each user to interact smoothly and naturally. In addition, mental states of a person may be affected by extrinsic factors, such as states of people around. Physiological indices was useful to understand mental states of a person. However, it was impractical that ECAs measure physiological indices of users. The purpose of this study was whether we could detect intrinsic "involvement, enjoyment, and/or excitement" ("IEE") of a person and extrinsic "IEE" of an atmosphere, by using visual information. In other words, the purpose is to develop a method to detect an atmosphere of "IEE" in multi-user interaction.
A lightweight fault tolerance framework for Web services
Authors: Wenbing Zhao, Honglei Zhang Hua Chai
Venue: Web Intelligence and Agent Systems (2009)

In this paper, we present the design and implementation of a lightweight fault tolerance framework for Web services. With our framework, a Web service can be rendered fault tolerant by replicating it across several nodes. A consensus-based algorithm is used to ensure total ordering of incoming application requests to the replicated Web service, and to ensure consistent membership view among the replicas. The framework is built by extending an open-source implementation of the WS-ReliableMessaging specification, and all reliable message exchanges in our framework conform to the specification. As such, our framework does not depend on any proprietary messaging and transport protocols, which is consistent with the Web services design principles. Our performance evaluation shows that our implementation is nearly optimal and the framework incurs only moderate runtime overhead.
Measuring Method of Harmonics in Power System Based on Wavelet Neural Network
Authors: Zhen Wang, Yi-hua Li
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

According to the characteristics of harmonics in power system, formation method of training samples is researched. Based on the principle of harmonic measuring equipment, which is the type of parallel filtering, a new measuring method of harmonics based on wavelet neural network is proposed. The wavelet neural network is optimized by improving scale factor and displacement factor of wavelet basis function, adaptive learning rate to increase convergence rate and measuring accuracy of network. The measuring method proposed has been simulated by program, simulation result shows the feasibility and validity.
Reducing labeling effort for structured prediction tasks
Authors: Aron Culotta, Andrew McCallum
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

A common obstacle preventing the rapid deployment of supervised machine learning algorithms is the lack of labeled training data. This is particularly expensive to obtain for structured prediction tasks, where each training instance may have multiple, interacting labels, all of which must be correctly annotated for the instance to be of use to the learner. Traditional active learning addresses this problem by optimizing the order in which the examples are labeled to increase learning efficiency. However, this approach does not consider the difficulty of labeling each example, which can vary widely in structured prediction tasks. For example, the labeling predicted by a partially trained system may be easier to correct for some instances than for others. We propose a new active learning paradigm which reduces not only how many instances the annotator must label, but also how difficult each instance is to annotate. The system also leverages information from partially correct predictions to efficiently solicit annotations from the user. We validate this active learning framework in an interactive information extraction system, reducing the total number of annotation actions by 22%.
Noise brush: interactive high quality image-noise separation
Authors: Jia Chen, Chi-Keung Tang, Jue Wang
Venue: ACM Transactions on Graphics (TOG) (2009)

This paper proposes an interactive approach using joint image-noise filtering for achieving high quality image-noise separation. The core of the system is our novel joint image-noise filter which operates in both image and noise domain, and can effectively separate noise from both high and low frequency image structures. A novel user interface is introduced, which allows the user to interact with both the image and the noise layer, and apply the filter adaptively and locally to achieve optimal results. A comprehensive and quantitative evaluation shows that our interactive system can significantly improve the initial image-noise separation results. Our system can also be deployed in various noise-consistent image editing tasks, where preserving the noise characteristics inherent in the input image is a desired feature.
Multi-period Product Configuration Change Planning: An Application of a Hybrid Algorithm
Authors: Cheng-Jui Chiang, Zheng-Hua Che, Her-Shing Wang
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

This paper focuses on dealing with the supplier selection and the purchase planning in multi-period change problems of product configuration with considering the assembly sequence of components, quantity discount, and customer requirement. To solving the configuration change problem, an optimized mathematical model is established and a decision support system is developed based on a hybrid algorithm. Through applying the proposed hybrid algorithm, the planning of multi-period product configuration change can be completed efficiently and effectively.
Beyond classical planning: procedural control knowledge and preferences in state-of-the-art planners
Authors: Jorge A. Baier, Christian Fritz, Meghyn Bienvenu, Sheila A McIlraith
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Real-world planning problems can require search over thousands of actions and may yield a multitude of plans of differing quality. To solve such real-world planning problems, we need to exploit domain control knowledge that will prune the search space to a manageable size. And to ensure that the plans we generate are of high quality, we need to guide search towards generating plans in accordance with user preferences. Unfortunately, most state-of-the-art planners cannot exploit control knowledge, and most of those that can exploit user preferences require those preferences to only talk about the final state. Here, we report on a body of work that extends classical planning to incorporate procedural control knowledge and rich, temporally extended user preferences into the specification of the planning problem. Then to address the ensuing nonclassical planning problem, we propose a broadly-applicable compilation technique that enables a diversity of state-of-the-art planners to generate such plans without additional machinery. While our work is firmly rooted in AI planning it has broad applicability to a variety of computer science problems relating to dynamical systems.
Applications of Principal Components Methods
Authors: Horia F. Pop, Militon Frentiu
Venue: Proceedings of the 2008 First International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (2008)

Multivariate statistical methods for the analysis of large quantities of data have been applied to problem solving in different domains during the last decades. This paper summarizes the main points of the Principal Components Analysis (PCA) method and its robust fuzzy alternatives, and describes a few applications highlighting the practical usefulness of this approach.
IPv4/IPv6 Intercommunication Technology Research
Authors: Chen Yan-ge, Tan Shui-mu, Guo Jun-ying
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

For IPv6 and IPv4 network seamless interconnection question, it is very essential to study IPv4/IPv6 intercommunication technology to ensure smooth transition of IPv4 to IPv6. To solve the seamless interconnection question, this article mainly researches IPv4 to IPv6 development's intercommunication question of three stages, including IPv6 based-IPv4 network interconnection, IPv4/IPv6 transparent interconnection and IPv4 IPv6-based network interconnection, and simultaneously analyzes the various interconnection stages of working ways and the suitable scopes in detail. Through choosing the technology logically,it can solve the seamless communication question.
Compressive data gathering for large-scale wireless sensor networks
Authors: Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen
Venue: Proceedings of the 15th annual international conference on Mobile computing and networking (2009)

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.
Introduction to the 3rd ROMAND Workshop on Robust Methods in Analysis of Natural Language Data
Authors: Vincenzo Pallotta, Amalia Todirascu
Venue: Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data (2004)

Robustness in Computational Linguistics has been recently recognized as a central issue for the design of interactive applications based on natural language communication. If a failure of the system can be acceptable in batch applications requiring a human intervention, an on-line system should be capable of dealing with unforeseen situations in a more flexible way. When we talk about system failure we do not think at inherent program failures like infinite loops or system exception, we consider, rather, failures related to the processing of the input and its assimilation in the system's knowledge base. A failure of this kind means simply that the system does not "understand" the input. The automated analysis of natural language data has become a central issue in the design of Intelligent Information Systems. Processing unconstrained natural language data is still considered an AI-hard task. However, various analysis techniques have been proposed in order to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.
Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models
Authors: Jun-Bao Li, Jeng-Shyang Pan, Zhe-Ming Lu
Venue: Neural Computing and Applications (2009)

This paper presents a novel face recognition method by integrating the Gabor wavelet representation of face images and the enhanced powerful discriminator, complete Kernel Fisher Discriminant (CKFD) with fractional power polynomial (FPP) models. The novelty of this paper comes from (1) Gabor wavelet, is employed to extract desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations in illumination and facial expressions, which improves the recognition performance; (2) a recently proposed powerful discriminator, namely CKFD, which enhances its discriminating ability using two kinds of discriminant information (i.e., regular and irregular information), is employed to classify the Gabor features; (3) the FPP models, are employed to CKFD analysis to enhance the discriminating ability. Comparing with existing principal component analysis, linear discriminant analysis, kernel principal component analysis, KFD and CKFD methods, the proposed method gives the superior results with the ORL, Yale and UMIST face databases.
Remote Consultation System Using Hierarchically Structured Agents
Authors: Hiroshi Yajima, Jun Sawamoto, Kazuo Matsuda
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 fields of technological innovation the speed of advance is fast, and while it is difficult for some people to keep up, there are few experts in new technologies. Since consultation is focused on a small number of experts, phenomena such as being unable to obtain sufficient information in a timely manner occur, and are one of the major reasons for the increasing social-technological divide. This paper proposes a 2-level hierarchical remote consultation system using two types of agent. The system possesses the features that through the responses to consultation made in advance by multiple agents, experts can focus on only complex questions, and in addition, consultees' waiting times are reduced. Its effectiveness is demonstrated experimentally.
Aggregation of Trust for Iterated Belief Revision in Probabilistic Logics
Authors: Pere Pardo
Venue: Proceedings of the 3rd International Conference on Scalable Uncertainty Management (2009)

In this paper it is shown how communication about trust in a multi-agent system may be used to endow agents with belief change capabilities, in a probabilistic logical framework. Belief change operators are obtained in an intuitive, principled way using aggregation operators for trust-values. Under additional conditions, such change operators may be proved to be maxichoice. The present approach constitutes a sound method for autonomous uncertainty management in multi-agent systems. 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
Reflective Learning through Playing Digital Game The Sims 2
Authors: Hui-Chun Hsiao
Venue: Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development (2009)

Containing too many actions and only a little time for reflection, earlier games were reflexive rather than reflective. Most of the research literature on game play emphasized the learning that occurs through game actions but relatively few concern the potential for emotional experiences and reflection that occur within game play. In combining artificial intelligence (AI) techniques and advanced simulation systems, the digital game The Sims 2 (TS2), provides a life-like narrative structured environment for player exploration, making it one of the most powerful and dynamic interactive narrative environments ever created. The TS2 player online community provides artistic creations, fictional narratives, ongoing in-depth discussions of play experiences, and descriptions of real-life experiences that have intermingled to become a firm network that supports social interactions and learning. In this study, I investigated the reflection that occurred via playing the digital game TS2 to see if such experience and reflection could lead players to generate new understanding and learning.
Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Venue: ACL Workshops (1998)

Natural Language Processing is increasingly moving away from the idea of handcoding grammars and systems, and the NeMLaP series of conferences has encouraged the exploration of new approaches to all aspects of the field. The techniques NeMLaP portrays tend to move away from the symbolic toward the connectionist and the statistical, and often have a learning flavour about them. The ACL Special Interest Group on Natural Language Learning last year initiated the CoNLL series which focus specifically on Learning, so it is highly appropriate that the two conferences, originally scheduled for two different cities in Australia, should be held jointly to maximize the interaction between the two groups.
Automatically Detecting Points of Interest and Social Networks from Tracking Positions of Avatars in a Virtual World
Authors: Frank Kappe, Bilal Zaka, Michael Steurer
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

With hundreds of millions of users already today,virtual worlds will become an important factor in tomorrow's media landscape. In a virtual world, users are represented by so-called avatars. These avatars move around the virtual world, communicate with each other,and interact with the virtual world. The movements of these avatars can be tracked precisely, and useful information can be inferred from analyzing these movements. In this paper, we analyze a large data set (>200 million records) of position data describing the movements of avatars in the virtual world Second Life.The dataset was derived from in-world sensors that had been deployed beforehand, but also so-called bots can be used to gather such information. From this data, we can track usage patterns of avatars (and therefore users) overtime. We can also identify regions of high interest where a large number of users gather frequently (which would be important for planning advertising in the virtual world), and visualize this statistical analysis using heat maps. By combining the position data with information about the language spoken by the avatars, we can label these regions according to the language predominantly spoken there. Analyzing incidents of co-location of avatars over a period of time, we can automatically infer friends, and eventually social networks. Using additional metadata such as language we can label clusters in this automatically generated social network.
Syntactico-semantic learning of categorial grammars
Authors: Isabelle Tellier
Venue: Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning (1998)

Natural language learning seems, from a formal point of view, an enigma. As a matter of fact, every human being, given nearly exclusively positive examples (as psycholinguists have noticed) is able at the age of about five to master his/her mother tongue. Though, no linguistically interesting class of formal languages is learnable with positive data in usual models (Gold's (67) and Valiant's (84)).
Bounded Model Checking of ACTL Formulae
Authors: Wei Chen, Wenhui Zhang
Venue: Proceedings of the 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering (2009)

In this paper, we give a new and improved Bounded Model Checking encoding method forthe universal fragment of CTL (ACTL). More specifically, the new encoding method works for verification of ACTL properties, instead of error-hunting. Combine our verification encoding and bug-hunting encoding proposed before, we get a Bounded Model Checking procedure that works for both valid and invalid ACTL properties. The underlying idea and intuition are summarized in this paper and we implement our tool BMV (Bounded Model Verification) on top of the well-knownmodel checker NuSMV 2, and conduct experiments that show the strength and weakness of ACTL Bounded Model Checking compared to traditional BDD-based model checking procedure.