ppxscal/embedding_network huggingface dataset:
Image collection taxonomies for photo-book auto-population with intuitive interaction
Authors: Pere Obrador, Nathan Moroney, Ian MacDowell, Eamonn O'Brien-Strain
Venue: Proceeding of the eighth ACM symposium on Document engineering (2008)

We demonstrate a system for automatic image selection for photobook creation, along with an intuitive user interface for fine tuning of the selection results. A versatile image collection representation is introduced, which allows for automatic scalable selection in order to target a specific image count for a predetermined size photobook. The images are selected based on their relevance, while preserving a good coverage of the event (time plus people) in order to maintain the storytelling potential of the selection. The selected images are laid out and presented to the user through an Adobe Flex user interface, which allows them to select images and swap them by semantically related ones, in an intuitive manner. The final result is output to a PDF file.
BAR--Where Distributed Computing Meets Game Theory
Authors: Lorenzo Alvisi
Venue: Proceedings of the 3rd Latin-American symposium on Dependable Computing (2007)

This tutorial describes a general approach for building cooperative services that span multiple administrative domains (MADs). MAD systems are attractive because their diffused control structure may yield services that are potentially less costly and more democratic than their more centralized counterparts. Unfortunately, they are also particularly problematic from a dependability standpoint as they challenge the traditional distinction between correct and faulty nodes.
Multiple Signal Classification Based on Genetic Algorithm for MEG Sources Localization
Authors: Chenwei Jiang, Jieming Ma, Bin Wang, Liming Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

How to locate the neural activation sources effectively and precisely from the magnetoencephalographic (MEG) recording is a critical issue for the clinical neurology and brain functions research. Multiple signal classification (MUSIC) algorithm and recursive MUSIC algorithm are widely used to locate multiple dipolar sources from the MEG data. The drawback of these algorithms is that they run very slowly when scanning a three-dimensional head volume globally. In order to solve this problem, a novel MEG sources localization scheme based on genetic algorithm (GA) is proposed. First, this scheme uses the property of global optimum of GA to estimate the rough source location. Then, combined with grids in small area, the accurate dipolar source localization is performed. Furthermore, we introduce the adaptive crossover and mutation probability, two-point crossover operator, periodical substitution and niche strategies to overcome the disadvantage of GA which falls into local optimum occasionally. Experimental results show that the proposed scheme can improve the speed of source localization greatly and its accuracy is satisfactory.
An adaptive probabilistic trust model and its evaluation
Authors: Chung-Wei Hang, Yonghong Wang, Munindar P. Singh
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

In open settings, the participants are autonomous and there is no central authority to ensure the felicity of their interactions. When agents interact in such settings, each relies upon being able to model the trustworthiness of the agents with whom it interacts. Fundamentally, such models must consider the past behavior of the other parties in order to predict their future behavior. Further, it is sensible for the agents to share information via referrals to trustworthy agents. Much progress has recently been made on probabilistic trust models including those that support the aggregation of information from multiple sources. However, current models do not support trust updates, leaving updates to be handled in an ad hoc manner. This paper proposes a trust representation that combines probabilities and certainty (defined as a function of a probability-certainty density function). Further, it offers a trust update mechanism to estimate the trustworthiness of referrers. This paper describes a testbed that goes beyond existing testbeds to enable the evaluation of a composite probability-certainty model. It then evaluates the proposed trust model showing that the trust model can (a) estimate trustworthiness of damping and capricious agents correctly, (b) update trust values of referrers accurately, and (c) resolve the conflicts in referral networks by certainty discounting.
Recommendation System for Automatic Recovery of Broken Web Links
Authors: Juan Martinez-Romo, Lourdes Araujo
Venue: Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence (2008)

In the web pages accessed when navigating throughout Internet, or even in our own web pages, we sometimes find links which are not valid any more. The search of the right web pages which correspond to those links is often hard. In this work we have analyzed different sources of information to automatically recover broken web links so that the user can be offered a list of possible pages to substitute that link. Specifically, we have used either the anchor text or the web page containing the link, or a combination of both. We report the analysis of a number of issues arising when trying to recover a set of links randomly chosen. This analysis has allowed us to decide the cases in which the system can perform the retrieval of some pages to substitute the broken link. Results have shown that the system is able to do reliable recommendations in many cases, specially under certain conditions on the anchor text and the parent page.
A model of gaze for the purpose of emotional expression in virtual embodied agents
Authors: Brent J. Lance, Stacy C. Marsella
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

Currently, state of the art virtual agents lack the ability to display emotion as seen in actual humans, or even in hand-animated characters. One reason for the emotional inexpressiveness of virtual agents is the lack of emotionally expressive gaze manner. For virtual agents to express emotion that observers can empathize with, they need to generate gaze - including eye, head, and torso movement - to arbitrary targets, while displaying arbitrary emotional states. Our previous work [18] describes the Gaze Warping Transformation, a method of generating emotionally expressive head and torso movement during gaze shifts that is derived from human movement data. Through an evaluation, it was shown that applying different transformations to the same gaze shift could modify the affective state perceived when the transformed gaze shift was viewed by a human observer. In this paper we propose a model of realistic, emotionally expressive gaze that builds upon the Gaze Warping Transformation by improving the transformation implementation, and by adding a model of eye movement drawn from the visual neuroscience literature. We describe how to generate a gaze to an arbitrary target, while displaying an arbitrary emotional behavior. Finally, we propose an evaluation to determine what emotions human observers will attribute to the generated gaze shifts. Once this work is completed, virtual agents will have access to a new channel for emotionally expressive behavior.
Anticipatory Model of Musical Style Imitation Using Collaborative and Competitive Reinforcement Learning
Authors: Arshia Cont, Shlomo Dubnov, Gérard Assayag
Venue: Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (2007)

The role of expectation in listening and composing music has drawn much attention in music cognition since about half a century ago. In this paper, we provide a first attempt to model some aspects of musical expectation specifically pertained to short-time and working memories, in an anticipatory framework. In our proposition anticipation is the mental realization of possible predicted actions and their effect on the perception of the world at an instant in time. We demonstrate the model in applications to automatic improvisation and style imitation. The proposed model, based on cognitive foundations of musical expectation, is an active model using reinforcement learning techniques with multiple agents that learn competitively and in collaboration. We show that compared to similar models, this anticipatory framework needs little training data and demonstrates complex musical behavior such as long-term planning and formal shapes as a result of the anticipatory architecture. We provide sample results and discuss further research.
Progress im High-Level Microprogramming
Authors: S. Davidson
Venue: IEEE Software (1986)

A high-level microprogramming language would speed work on higher level microprogramming and firmware engineering and decouple algorithm design from microcoding. This article examines several approaches. 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
Learning Action Models with Quantified Conditional Effects for Software Requirement Specification
Authors: Hankui Zhuo, Lei Li, Qiang Yang, Rui Bian
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Software requirement specification (SRS) is an important step in software engineering. Extracting requirement specification from an application field is a difficult task. In this paper, we consider software requirement as a problem to be solved by intelligent planning. To do this, one of the difficult things is how to represent the domain, since the software requirement has a feature of changeability. Thus, we divide the work into two tasks: the first one is to describe an incomplete domain of software requirement using PDDL(Planning Domains Definition Language) [7]; the second one is to complete the domain by learning from plan samples which is extracted from business processes. We modify the tool of [9] to learn action models with quantified conditional effects, which is the second task. In this way, people only need to do the first task and extract plan samples, which means the efforts of human beings are saved. At the end of the paper, we give our experiment result to show the efficiency of our method.
Rough Set Combine BP Neural Network in Next Day Load Curve Forcasting
Authors: Chun-Xiang Li, Dong-Xiao Niu, Li-Min Meng
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

Artificial neural network (ANN) is used in load forecasting widely. However, there are still some difficulties in choosing the input variables and selecting one appropriate architecture of the neural networks. According to the characteristics of electric short-term load forecasting, presents on a BPANN basing on rough set. Rough set theory is first used to perform input attributes selection. The initial decision table involves factors of weather and date which are able to affect load curve. Then K-Nearest Neighbor method is taken into selecting of most similar data to the target day as the training set of BPANN. Reduced input data of BPANN can avoid over-training and improved performance of BPANN and decreases times of training. The forecasting practice in Baoding Electric Power Company shows that the proposed model is feasible and has a good forecasting precision.
Towards a unified framework for efficient access methods and query operations in spatio-temporal databases
Authors: Jignesh M. Patel
Venue:  (2008)

Spatio-temporal databases are required to efficiently support queries on large numbers of continuously moving objects. In this thesis we propose three techniques to address this challenge. We develop the STRIPES indexing method, which indexes predicted trajectories in a dual transformed space. Trajectories for objects in d-dimensional space are transformed into points in 2d-dimensional space and are indexed with a hierarchical grid decomposition structure. STRIPES can evaluate a range of queries including time-slice, window, and moving queries. Extensive experimental evaluation shows that STRIPES is significantly faster than leading existing predicted trajectory index (the TPR*-tree) for both updates and queries. The All Nearest Neighbor (ANN) operation is a commonly used primitive for analyzing large multi-dimensional datasets. Traditional R*-tree based methods use a pruning metric called MAXMAXDIST, which allows the algorithms to prune nodes in the index that need not be examined during the ANN computation. We introduce a new pruning metric called NXNDIST, and show that this metric is far more effective than MAXMAXDIST. We also propose the MBRQT index structure, and using extensive experimental evaluation show that MBRQT offers better speedup in ANN computation than the commonly used R*-tree index. In addition, we present the MBA algorithm using depth-first index traversal and bi-directional node expansion. Furthermore, we extend our method to evaluate the more general All-k-Nearest-Neighbor (AkNN) operation. Traditional spatial and traditional temporal joins have been widely studied in the past, but there is very little work on the more complex problem of trajectory joins. We present a general framework called JiST, that introduces a broad class of trajectory join operations and offers a set of algorithms to efficiently evaluate these operations. We then introduce the notion of Trajectory Privacy, and show the application of the JiST framework in the context of privacy preservation. Finally, we present detailed experimental results that demonstrate the efficiency and scalability of the JiST join algorithms. To the best of our knowledge, JiST is the first comprehensive framework for complex trajectory join operations and paves the foundation for building a complex querying platform for emerging trajectory based applications.
The LIMSI Participation in the QAst Track
Authors: Sophie Rosset, Olivier Galibert, Gilles Adda, Eric Bilinski
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)

In this paper, we present two different question-answering systems on speech transcripts which participated to the QAst 2007 evaluation. These two systems are based on a complete and multi-level analysis of both queries and documents. The first system uses handcrafted rules for small text fragments (snippet) selection and answer extraction. The second one replaces the handcrafting with an automatically generated research descriptor. A score based on those descriptors is used to select documents and snippets. The extraction and scoring of candidate answers is based on proximity measurements within the research descriptor elements and a number of secondary factors. The evaluation results are ranged from 17% to 39% as accuracy depending on the tasks.
Fully complex-valued radial basis function networks: Orthogonal least squares regression and classification
Authors: S. Chen, X. Hong, C. J. Harris, L. Hanzo
Venue: Neurocomputing (2008)

We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme.
Letters to the Editor
Venue: IEEE Internet Computing (2007)

A letter about the Peering column "Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies," and the author's reply.
On the tractability of coloring semirandom graphs
Authors: Julia Böttcher, Dan Vilenchik
Venue: Information Processing Letters (2008)

As part of the efforts to understand the intricacies of the k-colorability problem, different distributions over k-colorable graphs have been analyzed. While the problem is notoriously hard (not even reasonably approximable) in the worst case, the average case (with respect to such distributions) often turns out to be ''easy''. Semi-random models mediate between these two extremes and are more suitable to imitate ''real-life'' instances than purely random models. In this work we consider semi-random variants of the planted k-colorability distribution. This continues a line of research pursued by Coja-Oghlan, and by Krivelevich and Vilenchik. Our aim is to study a more general semi-random framework than those suggested so far. On the one hand we show that previous algorithmic techniques extend to our more general semi-random setting; on the other hand we give a hardness result, proving that a closely related semi-random model is intractable. Thus we provide some indication about which properties of the input distribution make the k-colorability problem hard.
Decision Models
Authors: M. Theißen, W. Marquardt
Venue: Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project (2008)

In this contribution, the last of the four application domain models is presented. The model, implemented as an ontology, covers the rationale underlying the decisions in design projects. The ontology supports the representation of concrete decisions for documentation purposes as well as generalized decision templates, which can serve as guidelines for designers and help to reduce the effort for documenting concrete decisions.
CASC-J4 The 4th IJCAR ATP System Competition
Authors: Geoff Sutcliffe
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

The CADE ATP System Competition (CASC) is an annual evaluation of fully automatic, first-order Automated Theorem Proving (ATP) systems - the world championship for such systems. In addition to the primary aim of evaluating the relative capabilities of ATP systems, CASC aims to stimulate ATP research in general, to stimulate ATP research towards autonomous systems, to motivate implementation and fixing of systems, to provide an inspiring environment for personal interaction between ATP researchers, and to expose ATP systems within and beyond the ATP community. Fulfillment of these objectives provides stimulus and insight for the development of more powerful ATP systems, leading to increased and more effective usage. CASC-J4 was held on 13th August 2008, as part of the 4th International Joint Conference on Automated Reasoning, in Sydney, Australia. It was the thirteenth competition in the CASC series (lucky for some). The CASC-J4 web site provides access to details of the competition design, competition resources, and the systems that were entered: http://www.tptp.org/CASC/J4/
Fuzzy Functional Dependency and the Resolution Principle
Authors: Nedzad Dukić, Zikrija Avdagić
Venue: Informatica (2005)

In this paper we establish equivalence between a theory of fuzzy functional dependences and a fragment of fuzzy logic. We give a way to interpret fuzzy functional dependences as formulas in fuzzy logic. This goal is realized in four steps. Truth assignment of attributes is defined in terms of closeness between two tuples in a fuzzy relation. A corresponding fuzzy formula is associated to a fuzzy functional dependence. It is proved that if a relation satisfies a fuzzy functional dependence, then the corresponding fuzzy formula is satisfied and vice verse. Finally, equivalence of a fuzzy formulas and a set fuzzy functional dependence is demonstrated. Thus we are in position to apply the rule of resolution from fuzzy logic, while calculating fuzzy functional dependences.
Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations
Authors: Himanshu Jain, Edmund Clarke, Orna Grumberg
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

The use of Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms for obtaining interpolants for conjunctions of linear diophantine equations, linear modular equations (linear congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates in a counterexample guided abstraction refinement (CEGAR) framework. This has enabled verification of simple programs that cannot be checked using existing CEGAR based model checkers.
A quality of experience model for haptic user interfaces
Authors: Abdelwahab Hamam, Mohamad Eid, Abdulmotaleb El Saddik, Nicolas D. Georganas
Venue: Proceedings of the 2008 Ambi-Sys workshop on Haptic user interfaces in ambient media systems (2008)

Multimedia systems and applications have recently started to integrate the sense of touch and force feedback in the human-computer interaction. Surprisingly, measuring the quality of experience when haptic modality is incorporated in a graphical user interface has received limited attention from the research community. In this paper, we propose a taxonomy for measuring the quality of experience of a haptic user interface (HUI) applications. Furthermore, the taxonomy is modeled using a mathematical model. Finally, the proposed model is evaluated using two HUI-based applications: the haptic learning system and the haptic enabled UML CASE tool. The performance evaluation demonstrated that the proposed model is capable of reflecting the user estimation of the applications.
Specializations of Ferrers ideals
Authors: Alberto Corso, Uwe Nagel
Venue: Journal of Algebraic Combinatorics: An International Journal (2008)

We introduce a specialization technique in order to study monomial ideals that are generated in degree two by using our earlier results about Ferrers ideals. It allows us to describe explicitly a cellular minimal free resolution of various ideals including any strongly stable and any squarefree strongly stable ideal whose minimal generators have degree two. In particular, this shows that threshold graphs can be obtained as specializations of Ferrers graphs, which explains their similar properties.