ppxscal/embedding_network huggingface dataset:
Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks
Authors: Avi Rosenfeld, Sarit Kraus, Charlie Ortiz
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no polynomial algorithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a general approach where local agents can estimate their problem tightness, or how constrained their local subproblem is. This allows these agents to immediately identify many problems which are not constrained, and will not benefit from sending or receiving further information. Next, agents use traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained problems will most benefit from human attention. We evaluated this approach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective.
Decentralized Synchronization and Estimation in Wireless Networks
Authors: Nikolai Nefedov
Venue: Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking (2008)

In this paper we address estimation/control methods in complex networks where a global estimate (or decision) is obtained in a distributed fashion without fusion or centralized control centers. The suggested approach is based on local exchange of information among the nearby nodes within a connected (wireless) network that allows, under certain conditions, to reach a global decision based on locally available decisions/measurements. In particular, we consider network nodes as local dynamical systems with impulse-like coupling to establish time synchronization among the transmitted packets together with phase-coupling during packet durations to achieve distributed estimation/control. The suggested method may be used for distributed spectral sensing in cognitive radio and wireless sensor networks.
Efficient XQuery Evaluation of Grouping Conditions with Duplicate Removals
Authors: Norman May, Guido Moerkotte
Venue: Proceedings of the 5th international Symposium on XML Database: Database and XMLTechnologies (2007)

Currently, grouping in XQuery must be expressed implicitly with nested FLWOR expressions. With XQuery 1.1, an explicit group by clause will be part of this query language. As users integrate this new construct into their applications, it becomes important to have efficient evaluation techniques available to process even complex grouping conditions. Among them, the removal of distinct values or distinct nodes in the partitions defined by the group by clause is not well-supported yet. The evaluation technique proposed in this paper is able to handle duplicate removal in the partitions efficiently. Experiments show the superiority of our solution compared to state-of-the-art query processing.
On-Line Performance Modeling for MPI Applications
Authors: Oleg Morajko, Anna Morajko, Tomàs Margalef, Emilio Luque
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

To develop an efficient parallel application is not an easy task. Applications rarely achieve a good performance immediately therefore, a careful performance analysis and optimization are crucial. These tasks are difficult and require a thorough understanding of the program's behavior. In this paper, we propose an on-line performance modeling technique, which enables the automated discovery of causal execution flows, composed of communication and computational activities, in MPI parallel programs. Our model reflects an application behavior and is made up of elements correlated with high-level program structures, such as loops and communication operations. Moreover, our approach enables an assortment of on-line diagnosis techniques which may further automate the performance understanding process.
Adaptive Dynamic Clustered Particle Filtering for Mobile Robots Global Localization
Authors: Zhibin Liu, Zongying Shi, Mingguo Zhao, Wenli Xu
Venue: Journal of Intelligent and Robotic Systems (2008)

This article presents an adaptive dynamic clustered particle filtering method for mobile robot global localization. The posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment and ambiguous detected features. Moreover, the multimodal distribution of the posterior varies as the robot moves and observations be obtained. Considering these characteristics, we use a set of clusters of particles to represent the posterior. These clusters are dynamically evolved corresponding to the varying posterior by merging the overlapping clusters and splitting the diffuse clusters or those whose particles gather to some sub-clusters inside. Further, in order to improve computational efficiency without sacrificing estimation accuracy, a mechanism for adapting the sample size of clusters is proposed. The theoretical lower bound of the number of particles needed to limit the estimation error is derived, based on the central limit theorem in multi-dimensional space and the statistic theory of Importance Sampling. Then, a method for tuning the sample size for each cluster according to the derived lower bound is presented. Experiment results show the effectiveness of the proposed method, which is sufficient to achieve robust tracking of robot's real pose and meanwhile significantly enhance the computational efficiency.
Bayesian Combinatorial Auctions
Authors: George Christodoulou, Annamária Kovács, Michael Schapira
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We study the following Bayesian setting: m items are sold to n selfish bidders in m independent second-price auctions. Each bidder has a private valuation function that expresses complex preferences over all subsets of items. Bidders only have beliefs about the valuation functions of the other bidders, in the form of probability distributions. The objective is to allocate the items to the bidders in a way that provides a good approximation to the optimal social welfare value. We show that if bidders have submodular valuation functions, then every Bayesian Nash equilibrium of the resulting game provides a 2-approximation to the optimal social welfare. Moreover, we show that in the full-information game a pure Nash always exists and can be found in time that is polynomial in both m and n.
Adding Dependability Analysis Capabilities to the MARTE Profile
Authors: Simona Bernardi, José Merseguer, Dorina C. Petriu
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

Dependability is a non-functional property that should be assessed early in the software lifecycle. Although several UML profiles exist for quantitative annotations of non-functional properties, none of them provides concrete capabilities for dependability analysis of UML system models. In this paper, we propose a dependability analysis and modeling profile. The objective is twofold: to reuse proposals from the literature on deriving dependability models from UML annotated specifications and to be compliant with the recently adopted MARTE profile, which provides a framework for general quantitative analysis concepts that can be specialized to a particular analysis domain. The profile definition process was done in several steps. Firstly, an in depth analysis of the literature has been carried out to collect the information requirements for the profile. Secondly, a domain model for dependability analysis was defined independently of UML. Thirdly, the domain model was mapped to UML extensions by specializing MARTE.
The Average State Complexity of the Star of a Finite Set of Words Is Linear
Authors: Frédérique Bassino, Laura Giambruno, Cyril Nicaud
Venue: Proceedings of the 12th international conference on Developments in Language Theory (2008)

We prove that, for the uniform distribution over all sets X of m (that is a fixed integer) non-empty words whose sum of lengths is n, $\mathcal{D}_X$, one of the usual deterministic automata recognizing X *, has on average $\mathcal{O}(n)$ states and that the average state complexity of X * is ¿(n). We also show that the average time complexity of the computation of the automaton $\mathcal{D}_X$ is $\mathcal{O}(n\log n)$, when the alphabet is of size at least three.
Tutorial on Separation Logic (Invited Tutorial)
Authors: Peter O'Hearn
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

Separation logic is an extension of Hoare's logic for reasoning about programs that manipulate pointers. Its assertion language extends classical logic with a separating conjunction operator A*B, which asserts that A and B hold for separate portions of memory.In this tutorial I will first cover the basics of the logic, concentrating on highlights from the early work [1,2,3,4].(i) The separating conjunction fits together with inductive definitions in a way that supports natural descriptions of mutable data structures [1].(ii) Axiomatizations of pointer operations support in-place reasoning, where a portion of a formula is updated in place when passing from precondition to postcondition, mirroring the operational locality of heap update [1,2].(iii) Notorious "dirty" features of low-level programming (pointer arithmetic, explicit deallocation) are dealt with smoothly, even embraced [2,3].(iv) Frame axioms, which state what does not change, can be avoided when writing specifications [2,3].These points together enable specifications and proofs of pointer programs that are dramatically simpler than was possible previously, in many cases approaching the simplicity associated with proofs of pure functional programs. I will describe how that is, and where rough edges lie (programs whose proofs are still more complex than we would like).
Factors' correlation in the Heath&ndash;Jarrow&ndash;Morton interest rate model
Authors: Leonard Tchuindjo
Venue: Applied Stochastic Models in Business and Industry (2008)

We propose a new derivation of the Heath&ndash;Jarrow&ndash;Morton risk-neutral drift restriction that takes into account nonzero instantaneous correlations between factors. The result allows avoiding the orthogonalization of factors and provides an approach by which interest rate derivatives can be priced by preserving the economic meaning of each underlying factor. An application is given for the term structure of credit-risky bonds, driven by two correlated factors&mdash;the risk-free forward rate and the forward credit spreads. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Introducing Preferences into Commitment Protocols
Authors: Ashok U. Mallya, Munindar P. Singh
Venue: Agent Communication II: International Workshops on Agent Communication, AC 2005 and AC 2006, Utrecht, Netherlands, July 25, 2005, and Hakodate, Japan, May 9, 2006, Selected and Revised Papers (2006)

Commitment protocols enable flexibility in agent interactions by utilizing the semantics of commitments to develop succinct declarative specifications for protocols that allow a large number of executions. As a consequence, commitment protocols enable agents to accommodate varying local policies and respond to exceptions. A consequent weakness of such protocols is that commitment protocols thus fail to distinguish between possible executions that are normal and those that may be allowed but are not ideal. This paper develops an approach for specifying preferences among executions that are allowed by a protocol. It captures sets of executions via a simple language and gives them a denotational characterization based on branching-time models. It shows how to incorporate the specifications into rulesets, thereby giving the specifications a natural operational characterization. The rulesets embed into a recent practical framework for protocols called OWL-P. The paper shows that the operational and denotational characterizations coincide.
Segmentation of Ultrasound Imaging by Fuzzy Fusion: Application to Venous Thrombosis
Authors: Mounir Dhibi, Renaud Debon
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this work we propose a new method for ultrasound imaging segmentation in objective to help doctors and specialists to interpret anatomical structure. The proposed method is based on fuzzy fusion theory in objective to extract the venous thrombosis contour in ultrasound images acquired in vivo case. The first obtained results by optimization algorithm, adapted to our particular problem case are presented.
A Robust Overlay Network for Real-Time Multimedia Dissemination
Authors: Qiang Zeng, Hua Miao, Zhaoliang Zhu
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

Robustness of overlay networks is an importantproperty as well as short delay for real-time media transmission. Unlike previous schemes, which achieve robustness by restoring spanning trees afternodes or links fail, we focus on reducing possible data loss when failure occurs. According to analysis of the relation between tree depth and data loss, we first present a spanning tree which minimizes dataloss but results in large delay. By introducing a factor, we propose a spanning tree which exhibits fine flexibility and tradeoff between data loss and delay. Simulation results show that it achieves relatively low data loss and delay simultaneously, as is optimum for a large range of applications.
Learning from Relevant Tasks Only
Authors: Samuel Kaski, Jaakko Peltonen
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

We introduce a problem called relevant subtask learning, a variant of multi-task learning. The goal is to build a classifier for a task-of-interest having too little data. We also have data for other tasks but only some are relevant, meaning they contain samples classified in the same way as in the task-of-interest. The problem is how to utilize this "background data" to improve the classifier in the task-of-interest. We show how to solve the problem for logistic regression classifiers, and show that the solution works better than a comparable multi-task learning model. The key is to assume that data of all tasks are mixtures of relevant and irrelevant samples, and model the irrelevant part with a sufficiently flexible model such that it does not distort the model of relevant data.
A Concise Introduction to Languages and Machines, 1 edition
Authors: Alan P. Parkes
Venue: Undergraduate Topics In Computer Science (2008)

This easy-to-follow text provides an accessible introduction to the key topics of formal languages and abstract machines within Computer Science. The author follows the successful formula of his first book on this subject, this time making these core computing topics more fundamental and providing an excellent foundation for undergraduates. The book is divided into two parts, Languages and Machines and Machines and Computation. The first part is concerned with formal language theory, as it applies to Computer Science, whereas Part 2 considers the computational properties of the machines in more detail. This text is deliberately non-mathematical and, wherever possible, links theory to practical considerations, in particular the implications for programming, computation and problem solving. Written in an informal style, this textbook assumes only a basic knowledge of programming on the part of the reader. Features: Clear explanations of formal notation and jargon Extensive use of examples to illustrate algorithms and proofs Pictorial representations of key concepts Chapter-opening overviews providing an introduction and guidance to each topic An introductory chapter supplies the reader with a solid overview End-of-chapter exercises and solutions This reader-friendly textbook has been written with undergraduates in mind and will be suitable for use on courses covering formal languages, computability, automata theory and computational linguistics. It will also make an excellent supplementary text for courses on algorithm complexity and compilers.
The mixture of K-Optimal-Spanning-Trees based probability approximation: Application to skin detection
Authors: Sanaa El Fkihi, Mohamed Daoudi, Driss Aboutajdine
Venue: Image and Vision Computing (2008)

This paper presents a new approach for machine learning to deal with the problem of classification and/or probability approximation. Our contribution is based on the Optimal-Spanning-Tree distributions that are widely used in many optimization areas. The rationale behind this study is that in some cases the approximation of true class probability given by an Optimal-Spanning-Tree is not unique and might be chosen randomly. Furthermore, the user can specify the error tolerance between the tree weights that he/she can accept to manage the information of these kinds of trees. Therefore, the main idea of this work consists in focusing and highlighting the performance of each possible K(K@?N) Optimal-Spanning-Tree and making some assumptions, to propose the mixture of the K-Optimal-Spanning-Trees approximating the true class probability in a supervised algorithm. The theoretical proof of the K-Optimal-Spanning-Trees' mixture is given. Furthermore, the performance of our method is assessed for Skin/Non-Skin classification in the Compaq database by measuring the Receiver Operating Characteristic curve and its under area. These measures have proved better results of the proposed model compared with a random Optimal-Spanning-Tree model and the baseline one.
The 2007 AMI(DA) System for Meeting Transcription
Authors: Thomas Hain, Lukas Burget, John Dines, Giulia Garau, Martin Karafiat, David Leeuwen, Mike Lincoln, Vincent Wan
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

Meeting transcription is one of the main tasks for large vocabulary automatic speech recognition (ASR) and is supported by several large international projects in the area. The conversational nature, the difficult acoustics, and the necessity of high quality speech transcripts for higher level processing make ASR of meeting recordings an interesting challenge. This paper describes the development and system architecture of the 2007 AMIDA meeting transcription system, the third of such systems developed in a collaboration of six research sites. Different variants of the system participated in all speech to text transcription tasks of the 2007 NIST RT evaluations and showed very competitive performance. The best result was obtained on close-talking microphone data where a final word error rate of 24.9% was obtained.
Weaknesses and Improvements of Yang-Chang-Hwang's Password Authentication Scheme
Authors: Wei-Chi Ku, Hao-Chuan Tsai
Venue: Informatica (2005)

In 2001, Tseng, Jan, and Chien proposed an improved version of Peyravian-Zunic's password authentication scheme based on the Diffie-Hellman scheme. Later, Yang, Chang, and Hwang demonstrated that Tseng-Jan-Chien's scheme is vulnerable to a modification attack, and then described an improved scheme. In this paper, we show that Yang-Chang-Hwang's scheme is still vulnerable to a denial-of-service attack and a stolen-verifier attack. In addition, we also propose an improved scheme with better security.
Certified Computer Algebra on Top of an Interactive Theorem Prover
Authors: Cezary Kaliszyk, Freek Wiedijk
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

We present a prototype of a computer algebra system that is built on top of a proof assistant, HOL Light. This architecture guarantees that one can be certain that the system will make no mistakes. All expressions in the system will have precise semantics, and the proof assistant will check the correctness of all simplifications according to this semantics. The system actually proves each simplification performed by the computer algebra system.Although our system is built on top of a proof assistant, we designed the user interface to be very close in spirit to the interface of systems like Maple and Mathematica. The system, therefore, allows the user to easily probe the underlying automation of the proof assistant for strengths and weaknesses with respect to the automation of mainstream computer algebra systems. The system that we present is a prototype, but can be straightforwardly scaled up to a practical computer algebra system.
Learning MDP Action Models Via Discrete Mixture Trees
Authors: Michael Wynkoop, Thomas Dietterich
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

This paper addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree (context-specific dependence) models of the conditional probability distributions of the DBNs. Existing algorithms rely on standard regression tree learning methods (both propositional and relational). However, such methods presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise. This is inappropriate for many RL domains, where the stochasticity takes the form of stochastic choice over deterministic functions. This paper introduces a regression tree algorithm in which each leaf node is modeled as a finite mixture of deterministic functions. This mixture is approximated via a greedy set cover. Experiments on three challenging RL domains show that this approach finds trees that are more accurate and that are more likely to correctly identify the conditional dependencies in the DBNs based on small samples.
Approximation Capability Analysis of Parallel Process Neural Network with Application to Aircraft Engine Health Condition Monitoring
Authors: Gang Ding, Shisheng Zhong
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Parallel process neural network (PPNN) is a novel spatio-temporal artificial neural network. The approximation capability analysis is very important for the PPNN to enhance its adaptability to time series prediction. The approximation capability of the PPNN is analyzed in this paper, and it can be proved that the PPNN can approximate any continuous functional to any degree of accuracy. Finally, the PPNN is utilized to predict the iron concentration of the lubricating oil in the aircraft engine health condition monitoring to highlight the approximation capability of the PPNN, and the application test results also indicate that the PPNN can be used as a well predictive maintenance tool in the aircraft engine condition monitoring.