ppxscal/embedding_network huggingface dataset:
Semantic role labeling: an introduction to the special issue
Authors: Lluís Màrquez, Xavier Carreras, Kenneth C. Litkowski, Suzanne Stevenson
Venue: Computational Linguistics (2008)

Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.
Improving Search on Gnutella-Like P2P Systems
Authors: Qi Zhao, Jiaoyao Liu, Jingdong Xu
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Gnutella has many weaknesses. Queries are handled identically no matter how popular the querying objects are. Blind flooding causes a great amount of redundant messages. In this paper, we explore how to retain the simplicity of Gnutella, while addressing its inherent weaknesses. We propose GPP, a content location solution in which multiple proxy peers are adopted to help a source peer locate desired objects. The underlying philosophy is to route queries to the areas where more results can be found. Towards this end, peers in GPP collaborate to achieve better search performance. Simulation results show that GPP outperforms Modified BFS and s-APS.
Fast recursive filters for simulating nonlinear dynamic systems
Authors: J. H. van Hateren
Venue: Neural Computation (2008)

A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities may depend on system variables, and the topology of the system may be complex, including feedback. Several examples taken from neuroscience are given: phototransduction, photopigment bleaching, and spike generation according to the Hodgkin-Huxley equations. The scheme uses two slightly different forms of autoregressive filters, with an implicit delay of zero for feedforward control and an implicit delay of half a sample distance for feedback control. On a fairly complex model of the macaque retinal horizontal cell, it computes, for a given level of accuracy, one to two orders of magnitude faster than the fourth-order Runge-Kutta. The computational scheme has minimal memory requirements and is also suited for computation on a stream processor, such as a graphical processing unit.
Don't Get Emotional
Authors: Leysia Palen, Susanne Bødker
Venue: Affect and Emotion in Human-Computer Interaction: From Theory to Applications (2008)

The topic of emotion in computing is enjoying recent and growing attention. Such attention is problematic because, we argue, foregrounding emotion marks it as a subcomponent of interaction, which has the surprising consequence of reducing the importance of emotion's function. Emotion has come into vogue in reaction to the emphasis put on work, on efficiency and productivity in human computer interaction for so long. We discuss how this dichotomy is not in itself very fruitful in changing human-computer interaction. We offer this article as a voice in an ongoing discussion about where we are heading, and which directions this discussion could take.
The Verification Grand Challenge and Abstract Interpretation
Authors: Patrick Cousot
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)

Abstract Interpretation is a theory of approximation of mathematical structures, in particular those involved in the semantic models of computer systems [4,10,11]. Abstract interpretation can be applied to the systematic construction of methods and effective algorithms to approximate undecidable or very complex problems in computer science. The scope of application is rather large e.g. from type inference [5], model-checking [13], program transformation [14], watermarking [15] to context-free grammar parser generation [16].
Context-Aware Cache Management in Grid Middleware
Authors: Fabrice Jouanot, Laurent D'Orazio, Claudia Roncancio
Venue: Proceedings of the 1st international conference on Data Management in Grid and Peer-to-Peer Systems (2008)

This paper focuses on context-aware data management services in grids with the aim of constructing self-adaptive middleware. The contribution is twofold. First it proposes a framework to facilitate the development of context management services. The reason is that, even a context management service, is context specific. The creation of ad-hoc context managers is crucial. Second, this papers introduces context awareness in cooperative semantic caches. Preliminary results of our experiments on the Grid5000 platform are reported.
A Middle-Ware for the Automated Composition and Invocation of Semantic Web Services Based on Temporal HTN Planning Techniques
Authors: Juan Fernández-Olivares, Tomás Garzón, Luis Castillo, Óscar García-Pérez, Francisco Palao
Venue: Current Topics in Artificial Intelligence: 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2007, Salamanca, Spain, November 12-16, 2007. Selected Papers (2007)

This work presents a middle-ware able to translate OWL-S web services descriptions into a temporal HTN domain in order to automatically compose and execute sequences of web service invocations, including parallel branches and complex synchronizations, based on the combination of HTN planning and temporal reasoning techniques.
On k-optimal distributed constraint optimization algorithms: new bounds and algorithms
Authors: Emma Bowring, Jonathan P Pearce, Christopher Portway, Manish Jain, Milind Tambe
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In large-scale or low-bandwidth networks, finding the global optimum is often impractical. K-optimality is a promising new approach: for the first time it provides us a set of locally optimal algorithms with quality guarantees as a fraction of global optimum. Unfortunately, previous work in k-optimality did not address domains where we may have prior knowledge of reward structure; and it failed to provide quality guarantees or algorithms for domains with hard constraints (such as agents' local resource constraints). This paper addresses these shortcomings with three key contributions. It provides: (i) improved lower-bounds on k-optima quality incorporating available prior knowledge of reward structure; (ii) lower bounds on k-optima quality for problems with hard constraints; and (iii) k-optimal algorithms for solving DCOPs with hard constraints and detailed experimental results on large-scale networks.
Testing Geodesic Active Contours
Authors: A. Caro, T. Alonso, P. G. Rodríguez, M. L. Durán, M. M. Ávila
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

Active Contours are a widely used Pattern Recognition technique. Classical Active Contours are curves evolutionate by minimizing an energy function. However, they can detect only one o bject within an image with several objects, and the solution is highly dependent on parameters in its formulation. A solution can be found in Geodesic Active Contours (GAC). We have developed a version of this technique and improved some aspects to apply on real and practical cases. The algorithm has been tested with both synthetic and real images.
Ab initio determination of the interaction hyperpolarizability for the H-bond complex NH$_3$-HF
Authors: Wu Di, Li Zhi-Ru, Ding Yi-Hong, Zhang Man, Zheng Zhi-Ren, Wang Bing-Qiang, Hao Xi-Yun
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

The static dipole moment (&mu;$_0$), polarizability (&alpha;$_0$) and first hyperpolarizability (&beta;$_0$) of the hydrogen bond complex NH$_3$-HF with a equivalent triple &pi;-type hydrogen bond are investigated by means of ab initio methods at the MP2 level based on the QCISD potential energy surface. The full counterpoise (CP) method is applied in the studies of the intermolecular interaction contributions to the above properties. The first hyperpolarizability obtained is 31.76 a.u. The results of the intermolecular interaction contributions to the above properties are 26% for &mu;$_0$, -4.2% for &alpha;$_0$ and 10.0% for &beta;$_0$.
Subspace Vector Quantization and Markov Modeling for Cell Phase Classification
Authors: Dat Tran, Tuan Pham, Xiaobo Zhou
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.
An Intelligent Home System as a Development and Test Platform for Ubiquitous Computing
Authors: Keith C. Chan
Venue: Proceedings of the 4th international conference on Autonomic and Trusted Computing (2007)

Ubiquitous Computing is concerned with the thorough integration of information processing into everyday objects and activities. As such, someone engaging in ubiquitous computing should be able to enjoy the many benefits it is supposed to bring about at home. Since 2000, a team at the Department of Computing of The Hong Kong Polytechnic University has been developing an Ubiquitous Intelligent Home (UIH) that can demonstrate how a user can interact with "computers" at home in such a way that the user does not have to be aware that he or she is doing so. The UIH consists of four interconnecting networks: an appliance network, a furniture network, a telehealth network, and a security network. Each of these networks is made up of both hardware and software that are designed and developed to try to achieve the kind of ideal ubiquitous computing environment --- one that is made up of small, inexpensive, robust networked processing devices, distributed at all scales throughout everyday life. The UIH project has so far been "pervasive" not only in terms of its potential applications but also in terms of the researchers involved. Throughout its development, we have involved researchers in almost all areas of computing including those working on wireless sensor networking, sensor data management, data stream processing, RFID, embedded systems design, distributed processing, artificial intelligence, agent theory, speech recognition, image and video analysis, signal processing, data mining, computational intelligence, Chinese computing, data mining and machine learning, text mining, information retrieval, gesture recognition, biometrics, text-to-speech processing, software engineering, etc. In this talk, we will give the details of the UIH.
Algorithms for sparse nonnegative tucker decompositions
Authors: Morten Mørup, Lars Kai Hansen, Sidse M. Arnfred
Venue: Neural Computation (2008)

There is a increasing interest in analysis of large-scale multiway data. The concept of multiway data refers to arrays of data with more than two dimensions, that is, taking the form of tensors. To analyze such data, decomposition techniques are widely used. The two most common decompositions for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Nonnegative matrix factorization (NMF), in conjunction with sparse coding, has recently been given much attention due to its part-based and easy interpretable representation. While NMF has been extended to the PARAFAC model, no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed are nonnegative, it may well be relevant to consider purely additive (i.e., nonnegative) Tucker decompositions). To reduce ambiguities of this type of decomposition, we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse nonnegative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms for Tucker decompositions when the data and interactions can be considered nonnegative. We further illustrate how sparse coding can help identify what model (PARAFAC or Tucker) is more appropriate for the data as well as to select the number of components by turning off excess components. The algorithms for SN-TUCKER can be downloaded from Mørup (2007).
Real-Time Road Signs Tracking with the Fuzzy Continuously Adaptive Mean Shift Algorithm
Authors: Bogusław Cyganek
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Tracking of multiple objects belongs to one of the fundamental tasks of computer vision. In this paper an improvement to the continuously adaptive mean shift tracking method is proposed. It consists in substitution of the probabilistic density function for the especially formed membership function. This makes possible design of tracking systems in terms of fuzzy logic. Additionally, a special data structure was developed to allow tracking of multiple objects at a time. It stores information on image regions which are active for tracking. By this it provides initial conditions for tracking in subsequent frames which also speeds up computations. The method was used and verified in an application of the road signs tracking in real time of 30 frames/s.
A New Quantum Evolutionary Local Search Algorithm for MAX 3-SAT Problem
Authors: Abdesslem Layeb, Djamel-Eddine Saidouni
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

The Max Sat problem is very known problem in computer science. It aims to find the best assignment for a set of Boolean variables that gives the maximum of verified clauses in a Boolean formula. Unfortunately, this problem was showed NP-Hard if the number of variable per clause is higher than 3. In this article, we propose a new iterative stochastic approach called QSAT based on a hybrid algorithm of Quantum Evolutionary Algorithm QEA and Local Search Algorithm LSA. QSAT is based on a basic core defined by a suitable quantum representation and an adapted quantum evolutionary dynamic enhanced by Local Search procedure. The obtained results are encouraging and prove the feasibility and the effectiveness of our approach. QSAT is distinguished by a reduced population size and a reasonable number of iterations to find the best assignment, thanks to the principles of quantum computing.
Discrimination of Reconstructed Milk in Raw Milk by Combining Near Infrared Spectroscopy with Biomimetic Pattern Recognition
Authors: Ming Sun, Qigao Feng, Dong An, Yaoguang Wei, Jibo Si, Longsheng Fu
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

A new method for discriminating reconstructed milk in raw milk is proposed by combining NIRS (Near Infrared Spectroscopy) with the theory model BPR (Biomimetic Pattern Recognition). In order to compare its discrimination performance, we also carry out experiments by using a traditional method combining near infrared spectroscopy with DA (Discrimination Analysis). The results indicate that the accuracy of detection is 98.3% using the new method; while 87.5% using the traditional method. Comparison results show that the proposed new method is superior to the traditional method and the combination of NIRS with BPR has good potential to detect adulteration of raw milk with reconstructed milk.
Gesture interface engine
Authors: Ulrich Neumann
Venue:  (2007)

Gesture interfaces have long been pursued in the context of portable computing and immersive environments. However, such interfaces have been difficult to realize, in part due to the lack of frameworks for their design and implementation. Analogical to a game engine which is used to build computer games, a gesture interface engine is a framework which is used to build gesture interfaces systematically, conveniently, and efficiently. Rather than using a low-level high-dimensional joint angle space, we describe and recognize handposes in a &ldquo;lexical&rdquo; space, in which each handpose is decomposed into elements in a finger state alphabet. A handpose is defined by finger spelling, i.e., by specifying the pose of each finger and the interrelation between any two fingers. Dynamic gestures are defined as handpose transformation, hand translation, and rotation. The alphabet and underlying grammar form a simple but expressive gesture notation system called GeLex. The implementation of the gesture interface engine is based on a Canesta laser camera that produces reliable low-resolution depth images at video rates. By decomposing and recognizing handposes as finger states, we achieve robust handpose recognition in real-time (30 frames/second). Dynamic gestures are recognized using finite state machines and explicit gesture manifold tracking. By implementing a generic gesture recognition algorithm which recognizes a large set of gestures, and a mechanism to adapt it to a specific application based on an interface description (in GeLex), the development of a gesture interface is simplified. Other functionalities provided by the gesture interface engine include: a system called &ldquo;SmartCanvas&rdquo; which enables a user to write and draw on a regular desk with his/her fingers; algorithms to detect and remove conflicts within a gesture set, i.e., whether two gestures used together may cause recognition errors and/or interpretion ambiguity. Also, the thesis explores the development of intuitive gesture interfaces. In an intuitive gesture interface, a user interacts with a system with natural gestures, and the intended meanings are correctly interpreted by the system using a gesture-semantic database. The goal is to minimize the learning efforts and adaptation required from a user when presented with a new gesture interface.
Are P2P Data-Dissemination Techniques Viable in Today's Data-Intensive Scientific Collaborations?
Authors: Samer Al-Kiswany, Matei Ripeanu, Adriana Iamnitchi, Sudharshan Vazhkudai
Venue: Proceedings of the 13th European international conference on Parallel Processing (2007)

The interest among a geographically distributed user base to mine massive collections of scientific data propels the need for efficient data dissemination solutions. An optimal data distribution scheme will find the delicate and often application-specific balance among conflicting success metrics such as minimizing transfer times, minimizing the impact on the network, and uniformly distributing load among participants. We use simulations to explore the performance of classes of data-distribution techniques, some of which successfully deployed in large peer-to-peer communities, in the context of today's data-centric scientific collaborations. Based on these simulations we derive several recommendations for data distribution in real-world science collaborations.
A complete electrical equivalent circuit model for biological cell
Authors: Mohammad Amin, Pradip Peter Dey, Hassan Badkoobehi
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

A theoretical ac impedance model of biological tissue has been proposed after studying a cellular membrane using X-ray diffraction technique [1], a metal/organic system using ac impedance spectroscopy [2] and reviewing two pieces of literature of similar topics [3, 4]. This paper emphasizes ac impedance techniques which can be used to study any heterogeneous materials in order to develop a lumped parameter electrical equivalent circuit. The circuit parameters are determined from the impedance data obtained over a wide range of frequencies using ac small signal (voltage).
An Abstract Framework for Satisfiability Modulo Theories
Authors: Cesare Tinelli
Venue: Proceedings of the 16th international conference on Automated Reasoning with Analytic Tableaux and Related Methods (2007)

Satisfiability Modulo Theories (SMT) studies methods for checking the satisfiability (or, dually, the validity) of first-order formulas with respect to some logical theory T of interest. What distinguishes SMT from general automated deduction is that the background theory T need not be finitely or even first-order axiomatizable, and that specialized inference methods are used for each theory. By being theory-specific and restricting their language to certain classes of formulas (such as, typically but not exclusively, ground formulas), these specialized methods can be implemented into solvers that are more efficient in practice than general-purpose theorem provers. While SMT techniques have been traditionally used to support deductive software verification, they are now finding applications in other areas of computer science such as, for instance, planning, model checking and automated test generation.Theory-specific solvers can be often described conveniently in terms of tableau calculi, especially if one wants to prove that a solver decides a certain fragment of a theory T. In practice, however, most modern SMT solvers are not tableau-based and follow one of two main approaches, both of which exploit the recent technological advances in SAT solving. The "eager" approach uses smart encodings to propositional logic to compile T-satisfiability problems into propositional satisfiability problems, which can then be solved by off-the-self SAT solvers. The "lazy" approach instead uses general run-time mechanisms to separate plain Boolean reasoning from theory reasoning proper, doing the latter with small specialized procedures, and delegating the former to a dedicated SAT engine based on the DPLL procedure.After a brief overview of SMT, this talk focuses on a general and extensible abstract framework, Abstract DPLL Modulo Theories, for modeling lazy STM solvers declaratively and studying some of their theoretical properties. The framework is used to present and discuss a few basic variants of the lazy approach, in the case of a single and of multiple background theories. The talk also presents an extension of the framework that drastically simplifies the implementation of theory-specific components, and could be of interest to implementors of ground tableaux calculi as well.