ppxscal/embedding_network huggingface dataset:
Coordinating Competitive Agents in Dynamic Airport Resource Scheduling
Authors: Xiaoyu Mao, Adriaan Mors, Nico Roos, Cees Witteveen
Venue: Proceedings of the 5th German conference on Multiagent System Technologies (2007)

In real-life multi-agent planning problems, long-term plans will often be invalidated by changes in the environment during or after the planning process. When this happens, short-term operational planning and scheduling methods have to be applied in order to deal with these changed situations. In addition to the dynamic environment, in such planning systems we also have to be aware of sometimes conflicting interests of different parties, which render a centralized approach undesirable. In this paper we investigate two agent-based scheduling architectures where stakeholders are modelled as autonomous agents. We discuss this approach in the context of an interesting airport planning problem: the planning and scheduling of deicing and anti-icing activities. To coordinate the competition between agents over scarce resources, we have developed two mechanisms: one mechanism based on decommitment penalties, and one based on a more traditional (Vickrey) auction. Experiments show that the auction-based mechanism best respects the preferences of the individual agents, whereas the decommitment mechanism ensures a fairer distribution of delay over the agents.
A Scaling Algorithm for the Maximum Node-Capacitated Multiflow Problem
Authors: Maxim A. Babenko, Alexander V. Karzanov
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

We study the problem of finding a fractional node-capacitated multiflow of maximum value in an undirected network. Previously known methods for this problem are based on linear programming and the ellipsoid method. In this paper we apply a capacity scaling approach and develop a purely combinatorial weakly polynomial algorithm of time complexity O(¿(n,m,U) n 2 log2 n logU), where n, m, U are the number of nodes, the number of edges, and the maximum node capacity, respectively, and ¿(n,m,U) denotes the complexity of finding a maximum integer flow in a digraph with n nodes, m edges, and integer arc capacities not exceeding U ¿ ¿ + .
Generating Routes with Bio-inspired Algorithms under Uncertainty
Authors: Maria Belén Vaquerizo García
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The planning of an optimal design of routes is a complex problem of optimization and belongs to the type of NP-Hard problems. In this case, to find an exact solution is nonviable, and, therefore, it needs methods that assure the optimal management of the real resources to the design of the new routes under the best criteria about times and costs.This paper proposes the use of heuristic algorithms bio-inspired for the optimization in the design of the routes under diverse restrictions in the collective urban public transport in a town. This is because there are many applications in the transport field where this type of heuristic have proved to be very efficient. Moreover, among the variables that have greatest impact in developing this work, is the passenger demand, seen as uncertain data. For his treatment, it is suggested the use of the Fuzzy Sets Theory.Therefore, the purpose of this study is to establish a model for solving a complex and uncertain problem.
iReMedI - Intelligent Retrieval from Medical Information
Authors: Saurav Sahay, Bharat Ravisekar, Sundaresan Venkatasubramanian, Anushree Venkatesh, Priyanka Prabhu, Ashwin Ram
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.
Bayesian Network Decomposition for Modeling Breast Cancer Detection
Authors: Marina Velikova, Nivea Carvalho Ferreira, Peter Lucas
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The automated differentiation between benign and malignant abnormalities is a difficult problem in the breast cancer domain. While previous studies consider a single Bayesian network approach, in this paper we propose a novel perspective based on Bayesian network decomposition. We consider three methods that allow for different (levels of) network topological or structural decomposition. Through examples, we demonstrate some advantages of Bayesian network decomposition for the problem at hand: (i) natural and more intuitive representation of breast abnormalities and their features (ii) compact representation and efficient manipulation of large conditional probability tables, and (iii) a possible improvement in the knowledge acquisition and representation processes.
The Modified PNN Prediction Interval for Spacecraft Data
Authors: Jiahui Luan, Chen Lu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

In this paper, a new method is proposed for predicting the future evolution of a time series of spacecraft telemetry data. Because such a time series has usually a non-stationary trend, nonlinear functional relationship between inputs and outputs and other uncertainties, an appropriate prediction model for spacecraft data is needed to set up. To this end, we analyze the characteristics of the Probability Neural Network (PNN) and modify its architecture for fitting the requirements of prediction. We propose a new model: the Modified Probability Neural Network (MPNN) which incorporates the characteristics of statistics into the ANN prediction model. We also analyze the statistical characteritics of the error of MPNN prediction for spacecraft data to compute approximate prediction interval. Finally we construct the interval prediction with the MPNN model and apply it to predicting the spacecraft data.
The Importance of Non-theorems and Counterexamples in Program Verification
Authors: Graham Steel
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

We argue that the detection and refutation of non-theorems, and the discovery of appropriate counterexamples, is of vital importance to the Grand Challenge of a Program Verifier.
Experimenting with Language Support for Proximity in Ambient-Oriented Programming
Authors: Victor Ramiro, Jessie Dedecker, Éric Tanter, Peter Barron
Venue: Engineering Environment-Mediated Multi-Agent Systems: International Workshop, EEMMAS 2007, Dresden, Germany, October 5, 2007. Selected Revised and Invited Papers (2008)

Proximity is a key to scalable and meaningful interactions in distributed systems, both natural and artificial, and in particular in pervasive computing environments. However, proximity as such is a vague notion that can be considered both in a very factual manner (spatial distance) and in a very abstract and subjective manner (user affinity). We claim that an adequate system or programming language for ambient intelligence applications ought to support an open notion of proximity, making it possible to rely on different, possibly subjective, understandings of proximity, as well as their combinations. We explore how to extend the Ambient-Oriented Programming language AmbientTalk with language constructs that give programmers flexible control over subjective proximity definitions in both service advertising and discovery.
Measuring Inconsistencies in Ontologies
Authors: Xi Deng, Volker Haarslev, Nematollaah Shiri
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

In this paper, we propose a novel approach to measure inconsistencies in ontologies based on Shapley values, which are originally proposed for game theory. This measure can be used to identify which axioms in an input ontology or which parts of these axioms need to be removed or modified in order to make the input consistent. We also propose optimization techniques to improve the efficiency of computing Shapley values. The proposed approach is independent of a particular ontology language or a particular reasoning system used. Application of this approach can improve the quality of ontology diagnosis and repair in general.
Semantically Enhanced Entity Ranking
Authors: Gianluca Demartini, Claudiu S. Firan, Tereza Iofciu, Wolfgang Nejdl
Venue: Proceedings of the 9th international conference on Web Information Systems Engineering (2008)

Users often want to find entities instead of just documents, i.e., finding documents entirely about specific real-world entities rather than general documents where the entities are merely mentioned. Searching for entities on Web scale repositories is still an open challenge as the effectiveness of ranking is usually not satisfactory. Semantics can be used in this context to improve the results leveraging on entity-driven ontologies. In this paper we propose three categories of algorithms for query adaptation, using (1) semantic information, (2) NLP techniques, and (3) link structure, to rank entities in Wikipedia. Our approaches focus on constructing queries using not only keywords but also additional syntactic information, while semantically relaxing the query relying on a highly accurate ontology. The results show that our approaches perform effectively, and that the combination of simple NLP, Link Analysis and semantic techniques improves the retrieval performance of entity search.
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity
Authors: Jürgen Schmidhuber
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

I postulate that human or other intelligent agents function or should function as follows. They store all sensory observations as they come--the data is `holy.' At any time, given some agent's current coding capabilities, part of the data is compressible by a short and hopefully fast program / description / explanation / world model. In the agent's subjective eyes, such data is more regular and more beautiful than other data [2,3]. It is well-known that knowledge of regularity and repeatability may improve the agent's ability to plan actions leading to external rewards. In absence of such rewards, however, known beauty is boring. Then interestingness becomes the first derivative of subjective beauty: as the learning agent improves its compression algorithm, formerly apparently random data parts become subjectively more regular and beautiful. Such progress in data compression is measured and maximized by the curiosity drive [1,4,5]: create action sequences that extend the observation history and yield previously unknown / unpredictable but quickly learnable algorithmic regularity. We discuss how all of the above can be naturally implemented on computers, through an extension of passive unsupervised learning to the case of active data selection: we reward a general reinforcement learner (with access to the adaptive compressor) for actions that improve the subjective compressibility of the growing data. An unusually large data compression breakthrough deserves the name discovery. The creativity of artists, dancers, musicians, pure mathematicians can be viewed as a by-product of this principle. Good observer-dependent art deepens the observer's insights about this world or possible worlds, unveiling previously unknown regularities in compressible data, connecting previously disconnected patterns in an initially surprising way that makes the combination of these patterns subjectively more compressible, and eventually becomes known and less interesting. Several qualitative examples support this hypothesis.
Triple Graph Grammars and OCL for Validating System Behavior
Authors: Duc-Hanh Dang
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

We propose an approach based on the integration of Triple Graph Grammars (TGGs) and the Object Constraint Language (OCL) for checking the conformance between use case and design models.
Model-Based Run-Time Error Detection
Authors: Jozef Hooman, Teun Hendriks
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

The reliability of high-volume products, such as consumer electronic devices, is threatened by the combination of increasing complexity, decreasing time-to-market, and strong cost constraints. As an approach to maintain a high level of reliability and to avoid customer complaints, we present a run-time awareness concept. Part of this concept is the use of models for run-time error detection. We have implemented a general awareness framework in which an application and a model of its desired behaviour can be inserted. It allows both time-based and event-based error detection at run time. This method, coupled to local recovery techniques, aims to minimize any user exposure to product-internal technical errors, thereby improving user-perceived reliability.
Municipal Creditworthiness Modelling by Kohonen's Self-Organizing Feature Maps and Fuzzy Logic Neural Networks
Authors: Petr Hajek, Vladimir Olej
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

The paper presents the design of municipal creditworthiness parameters. Further, the design of model for municipal creditworthiness classification is presented. The model is composed of Kohonen's self-organizing feature maps and fuzzy logic neural networks, where the output of Kohonen's self-organizing feature maps represents the input of fuzzy logic neural networks. It is a feed-forward fuzzy logic neural network with three layers. Standard neurons are replaced by fuzzy neurons in the fuzzy logic neural network.
Immune Systems and Computation: An Interdisciplinary Adventure
Authors: Jon Timmis, Paul Andrews, Nick Owens, Ed Clark
Venue: Proceedings of the 7th international conference on Unconventional Computing (2008)

Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and are developed through the application of techniques such as mathematical and computational modeling of immunology, abstraction from those models into algorithm (and system) design and implementation in the context of engineering. Whilst AIS has become known as an area of computer science and engineering that uses immune system metaphors for the creation of novel solutions to problems, we argue that the area of AIS is much wider and is not confined to the simple development of new algorithms. In this paper we would like to broaden the understanding of what AIS are all about, thus driving the area into a true interdisciplinary one of genuine interaction between immunology, mathematics and engineering.
Re-envisioning community college transfer
Authors: Deborah G. Boisvert, Irene Bruno, Reza Kamali, Michael Puopolo, Maria Rynn, Lisa Feuerbach
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

In this paper, we describe an innovative project that is gathering information for a re-envisioning of transfer procedures between two and four-year higher education institutions in three states. Convening relevant administrators, faculty, and local business partners within each of the three regions, this project has been researching current transfer models (successful and unsuccessful), identifying exemplary frameworks and identifying the variables that lead to enhanced upward progression. The authors will discuss the current status of transfer in the regions and will identify processes that are being used to identify more innovative and effective methods to increase student transferability between levels.
A study of west virginia p--12 teachers' use of the internet as a professional and instructional tool
Authors: Lisa A. Heaton
Venue:  (2006)

This study examines West Virginia P-12 teachers' use of the Internet as a professional and instructional tool. Quantitative and qualitative methods were used to gather descriptive data. Based on an extensive review of the literature, the researcher developed survey included 30 questions on the use of the Internet by teachers as a professional tool and by their students as an instructional tool. Two hundred forty-two West Virginia P-12 public school teachers participated in the study with fourteen participating in interviews. Interviews provided anecdotal information of teachers' experiences in finding, creating, and using the Internet for professional and instructional activities. Findings based on survey results are presented using the support of qualitative feedback from interviews. This study found respondents' most common use of the Internet as a professional tool was to find supplemental information for lessons and as an instructional tool was to have their students find information and/or resources. When comparing the 30 professional and instructional use questions to the demographic information, three areas that showed significant differences were participation in West Virginia Internet initiatives, the participant's identification of the greatest barrier to using the Internet, and the school's percentage of students receiving free or reduced lunch. No significant differences between the professional and instructional questions and age of respondents, years of teaching experience, and how the participants obtained their Internet knowledge were found.
Integrating MeSH Ontology to Improve Medical Information Retrieval
Authors: M. C. Díaz-Galiano, M. Á. García-Cumbreras, M. T. Martín-Valdivia, A. Montejo-Ráez, L. A. Ureña-López
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retrieval subtask. The experiments accomplished are based on the integration of specific knowledge in the topics.We have used the MeSH ontology to expand the queries. The expansion consists in searching terms from the topic query in the MeSH ontology in order to add similar terms. We have processed the set of collections using Information Gain (IG) in the same way as in ImageCLEFmed 2006.In our experiments mixing visual and textual information we obtain better results than using only textual information. The weigth of the textual information is very strong in this mixed strategy. In the experiments with a low textual weight, the use of IG improves the results obtained.
On the Relation between Anisotropic Diffusion and Iterated Adaptive Filtering
Authors: Michael Felsberg
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

In this paper we present a novel numerical approximation scheme for anisotropic diffusion which is at the same time a special case of iterated adaptive filtering. By assuming a sufficiently smooth diffusion tensor field, we simplify the divergence term and obtain an evolution equation that is computed from a scalar product of diffusion tensor and the Hessian. We propose further a set of filters to approximate the Hessian on a minimized spatial support. On standard benchmarks, the resulting method performs in average nearly as good as the best known denoising methods from the literature, although it is significantly faster and easier to implement. In a GPU implementation video real-time performance is achieved for moderate noise levels.
Computational Study on Dominating Set Problem of Planar Graphs
Authors: Marjan Marzban, Qian-Ping Gu, Xiaohua Jia
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

Recently, there has been significant theoretical progress towards fixed-parameter algorithms for the DOMINATING SET problem of planar graphs. It is known that the problem on a planar graph with n vertices and dominating number k can be solved in $O(c^{\sqrt{k}}n)$ time using tree/branch-decomposition based algorithms, where c is some constant. However there has been no computational study report on the practical performances of the $O(c^{\sqrt{k}}n)$ time algorithms. In this paper, we report computational results of Fomin and Thilikos algorithm which uses the branch-decomposition based approach. The computational results show that the algorithm can solve the DOMINATING SET problem of large planar graphs in a practical time for the class of graphs with small branchwidth. For the class of graphs with large branchwidth, the size of instances that can be solved by the algorithm in a practical time is limited to a few hundreds edges. The practical performances of the algorithm coincide with the theoretical analysis of the algorithm. The results of this paper suggest that the branch-decomposition based algorithms can be practical for some applications on planar graphs.
On the Stable Set Polytope of Claw-Free Graphs
Authors: Anna Galluccio, Claudio Gentile, Paolo Ventura
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

We define the class of geared (fuzzy) line graphs as the class of graphs obtained by repeated applications of the extended gear composition to a (fuzzy) line graph H. Using the decomposition theorem for claw-free graphs of Chudnovsky and Seymour [2], we show that this class represents a large subclass of claw-free graphs having stability number at least 4.We provide a complete linear description of the stable set polytope of geared (fuzzy) line graphs. This result gives a first substantial answer to the longstanding open question of finding a defining linear system for the stable set polytope of claw-free graphs [10].
Multinomial mixture model with feature selection for text clustering
Authors: Minqiang Li, Liang Zhang
Venue: Knowledge-Based Systems (2008)

The task of selecting relevant features is a hard problem in the field of unsupervised text clustering due to the absence of class labels that would guide the search. This paper proposes a new mixture model method for unsupervised text clustering, named multinomial mixture model with feature selection (M3FS). In M3FS, we introduce the concept of component-dependent ''feature saliency'' to the mixture model. We say a feature is relevant to a certain mixture component if the feature saliency value is higher than a predefined threshold. Thus the feature selection process is treated as a parameter estimation problem. The Expectation-Maximization (EM) algorithm is then used for estimating the model. The experiment results on commonly used text datasets show that the M3FS method has good clustering performance and feature selection capability.