ppxscal/embedding_network huggingface dataset:
Agent-Supported Web Service Composition for Supply Chain Management
Authors: Minhong Wang, William K. Cheung, Jiming Liu, Zongwei Lou
Venue: Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (2006)

Web service composition requires allocating suitable resources to a set of services that constitute a composite service. The problem is complicated by the fact that the availability of resources may be uncertain and change over time, and the constraints of component services may not be completely known in advance. It needs the ability to rapidly and dynamically identify suitable resources and coordinate them under various constraints. In this paper, an agent mediated coordination framework for web service composition is proposed in the context of e-supply chain management (e-SCM). Global constraints to be satisfied by the composite service are first translated into local constraints of its component services. Service brokering agents dedicated to discovery of component services interact autonomously with each others for refining the local constraints based on the candidate services' instantaneous status until a sufficiently good composition is identified. Although presented in the context of supply chain management, this framework is applicable to other problem areas where a set of services are composed as a result of a large resource repository to be searched, scheduled and coordinated, especially in a real-time fashion.
An adaptive algorithm for image restoration using combined penalty functions
Authors: Daan Zhu, Moe Razaz, Mark Fisher
Venue: Pattern Recognition Letters (2006)

In this paper, we present an adaptive gradient based method to restore images degraded by the effects of both noise and blur. The approach combines two penalty functions. The first derivative of the Canny operator is employed as a roughness penalty function to improve the high frequency information content of the image and a smoothing penalty term is used to remove noise. An adaptive algorithm is used to select the roughness and smoothing control parameters. We evaluate our approach using the Richardson-Lucy EM algorithm as a benchmark. The results highlight some of the difficulties in restoring blurred images that are subject to noise and show that in this case an algorithm that uses a combined penalty function is able to produce better quality results.
Soft Tissue Deformation and Cutting Simulation for the Multimodal Surgery Training
Authors: Yi-Je Lim, John Hu, Chu-Yin Chang, Neil Tardella
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

Energid is developing a realistic surgery simulator that delivers high fidelity visual and haptic feedback based on the physics of deformable objects. Modeling the interaction of surgical tools with soft biological tissue in real time poses challenges because the precise physical models of organs are not readily available, and the simulation of the behavior of tissue has a high computational burden. In this paper we present a realistic surgery simulation technique which inlcudes novel algorithms for simulating surgical palpation and cutting. We implement a meshfree numerical technique for realistic surgery palpation simulation. Simulation of surgical cutting is one of the most challenging tasks in the development of a surgery simulator. Changes in topology during simulation render precomputed data unusable. Moreover, the process is nonlinear and the underlying physics is complex. We propose a hybrid approach to the simulation of surgical cutting procedures by combining a node snapping technique with a physically based meshfree computational scheme.
Efficient Bit-Parallel Multiplier for Irreducible Pentanomials Using a Shifted Polynomial Basis
Authors: Sun-Mi Park, Ku-Young Chang, Dowon Hong
Venue: IEEE Transactions on Computers (2006)

In this paper, we present a bit-parallel multiplier for GF(2^m) defined by an irreducible pentanomial x^m+x^{k_3}+x^{k_2}+x^{k_1}+1, where 1\leq k_1
A hierarchical approach to efficient reinforcement learning in deterministic domains
Authors: Carlos Diuk, Alexander L. Strehl, Michael L. Littman
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-scale state spaces. We bring these three ideas together in a new algorithm. Our algorithm tackles two open problems from the reinforcement-learning literature, and provides a solution to those problems in deterministic domains. First, it shows how models can improve learning speed in the hierarchy-based MaxQ framework without disrupting opportunities for state abstraction. Second, we show how hierarchies can augment existing factored exploration algorithms to achieve not only low sample complexity for learning, but provably efficient planning as well. We illustrate the resulting performance gains in example domains. We prove polynomial bounds on the computational effort needed to attain near optimal performance within the hierarchy.
Odds ratio based multifactor-dimensionality reduction method for detecting gene--gene interactions
Authors: Yujin Chung, Seung Yeoun Lee, Robert C. Elston, Taesung Park
Venue: Bioinformatics (2007)

Motivation: The identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases is a challenging task in genetic association studies. The multifactor dimensionality reduction (MDR) method has been proposed and implemented by Ritchie et al. (2001) to identify the combinations of multilocus genotypes and discrete environmental factors that are associated with a particular disease. However, the original MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups in an ad hoc manner based on a simple comparison of the ratios of the number of cases and controls. Hence, the MDR approach is prone to false positive and negative errors when the ratio of the number of cases and controls in a combination of genotypes is similar to that in the entire data, or when both the number of cases and controls is small. Hence, we propose the odds ratio based multifactor dimensionality reduction (OR MDR) method that uses the odds ratio as a new quantitative measure of disease risk. Results: While the original MDR method provides a simple binary measure of risk, the OR MDR method provides not only the odds ratio as a quantitative measure of risk but also the ordering of the multilocus combinations from the highest risk to lowest risk groups. Furthermore, the OR MDR method provides a confidence interval for the odds ratio for each multilocus combination, which is extremely informative in judging its importance as a risk factor. The proposed OR MDR method is illustrated using the dataset obtained from the CDC Chronic Fatigue Syndrome Research Group. Availability: The program written in R is available. Contact: tspark@snu.ac.kr
Facilitating the Self-Formation of Collaborative Groups, Online
Authors: Michael Lawrence-Slater
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Many factors come into play in the creation of collaborative groups. These include the assessment of skills and abilities of potential members as well as social cues. In online courses, when groups are to be formed over the network, participants know nothing of each other - not even what they look like. This paper reports on an online course where the participants, situated in three institutions, over two continents, formed groups online and successfully completed a collaborative project. To enable this, participants posted a brief biography showing their interests, the degree being studied, their institutional affiliation, an email address, name and an image of themselves (ideally, but not necessarily, their proper name and a photograph). Participants then accessed these details and "met" others with a view to forming a group. This technique not only enabled the formation of groups, but also, at the outset of the course, demonstrated that participants could successfully collaborate online. In the course experience survey, the participants rated this method highly both for its function and as a community-building facility.
A Generic Mobility Model for Resource Prediction in Mobile Grids
Authors: Umar Farooq, Wajeeha Khalil
Venue: Proceedings of the International Symposium on Collaborative Technologies and Systems (2006)

Grid Computing has emerged as an efficient problem solution paradigm since the last decade. Most of the research and implementation of grid computing environments collaborate and share heterogeneous resources in static manner. Scheduling is the most challenging and primitive issue that should be addressed effectively to achieve minimized resource utilization and maximum performance. This way reduced average execution time is achieved. To realize the above mentioned goal, many prediction models have been presented based on different parameters such as network bandwidth, computing capability and replica management. Recent years have seen a keen interest of research community and practitioners for the development and deployment of mobile grids. This paper aims to present a generic mobility model to extend the conventional prediction models. The model visualizes best resources and hence improves scheduler performance in mobile grid environments. A sample implementation mechanism and two possible scenarios have been demonstrated.
A theory of regression testing for behaviourally compatible object types: Research Articles
Authors: Anthony J. H. Simons
Venue: Software Testing, Verification Reliability (2006)

A behavioural theory of object compatibility is presented, which has implications for object-oriented regression testing. The theory predicts that only certain models of state refinement yield compatible types, dictating the legitimate design styles to be adopted in object statecharts. The theory also predicts that conformity-testing using regression tests is inadequate. Functionally complete test-sets that are applied as regression tests to subtype objects are usually expected to cover the functionality of the original type, even though they are clearly not expected to cover extra functionality introduced in the subtype. However, such regression testing is proven to cover strictly less than the original state-space in the new context and so provides much weaker confidence than expected. A different retesting model is proposed, based on full automatic test regeneration from the subtype's specification. This method can guarantee equivalent levels of confidence after retesting. The behavioural conformity desired by regression testing can then be proven by verification in the theory. Copyright &copy; 2006 John Wiley & Sons, Ltd.A version of this paper was originally presented at UKTest 2005: The Third U.K. Workshop on Software Testing Research, held at the University of Sheffield, U.K., 5&ndash;6 September 2005. It is reproduced here in revised and extended form with the permission of the Workshop organizers
Limit theorems for the multilevel splitting algorithm in the simulation of rare events
Authors: Frédéric Cérou, François LeGland, Pierre Del Moral, Pascal Lezaud
Venue: Proceedings of the 37th conference on Winter simulation (2005)

In this article, a genetic-type algorithm based on interacting particle systems is presented, together with a genealogical model, for estimating a class of rare events arising for instance in telecommunication networks, nuclear engineering, etc. The distribution of a Markov process hitting a rare but critical set is represented in terms of a Feynman-Kac model in path space. Approximation results obtained previously for these models are applied here to estimate the probability of the rare events as well as the probability distribution of the critical trajectories.
Improve Decision Trees for Probability-Based Ranking by Lazy Learners
Authors: Han Liang, Yuhong Yan
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

Existing work shows that classic decision trees have inherent deficiencies in obtaining a good probability-based ranking (e.g. AUC). This paper aims to improve the ranking performance under decision-tree paradigms by presenting two new models. The intuition behind our work is that probability-based ranking is a relative metric among samples, therefore, distinct probabilities are crucial for accurate ranking. The first model, Lazy Distance-based Tree (LDTree), uses a lazy learner at each leaf to explicitly distinguish the different contributions of leaf samples when estimating the probabilities for an unlabeled sample. The second model, Eager Distance-based Tree (EDTree), improves LDTree by changing it into an eager algorithm. In both models, each unlabeled sample is assigned a set of unique probabilities of class membership instead of a set of uniformed ones, which gives finer resolution to differentiate samples and leads to the improvement of ranking. On 34 UCI sample sets, experiments verify that our models greatly outperform C4.5, C4.4 and other standard smoothing methods designed for better ranking.
Music Mood Wheel: Improving Browsing Experience on Digital Content through an Audio Interface
Authors: Andreja Andric, Pierre-Louis Xech, Andrea Fantasia
Venue: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (2006)

Mobile devices for listening to digital music can now contain a huge number of musical titles. Search for appropriate music content becomes difficult and time-consuming. The interfaces on MP3 CD players or flash MP3 players available on the market usually combine buttons, jog-wheels and small displays of one to three lines. The most common approach to music browsing is still either one-step forward and backward movement with two buttons, or moving inside a tree of options. In our approach, we shift from the "tree of options" paradigm to a "search by ear" browsing experience. Following the construction of a first rough prototype we found that our interface performed well in an experiment with 40 participants, on a collection of 3500 songs. Ongoing and future works cover the development of an advanced prototype on a smartphone running the Windows Mobile operating system and a new experiment in similar conditions.
On the Signature Tree Construction and Analysis
Authors: Yibin Chen
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

Advanced database application areas, such as computer aided design, office automation, digital libraries, data-mining, as well as hypertext and multimedia systems, need to handle complex data structures with set-valued attributes, which can be represented as bit strings, called signatures. A set of signatures can be stored in a file, called a signature file. In this paper, we propose a new method to organize a signature file into a tree structure, called a signature tree, to speed up the signature file scanning and query evaluation. In addition, the average time complexity of searching a signature tree is analyzed and how to maintain a signature tree on disk is discussed. We also conducted experiments, which show that the approach of signature trees provides a promising index structure.
Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications
Authors: Bogdan C. Matei, Peter Meer
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

In an errors-in-variables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errors-in-variables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the Levenberg-Marquardt method, the standard approach toward estimating nonlinear models.
Simulation modelling for a bus maintenance facility
Authors: Manivannan Ramadass, Jay Rosenberger, Brian Huff, Stephanie Gonterman, Rajesh N. Subramanian
Venue: Proceedings of the 36th conference on Winter simulation (2004)

The Greyhound Lines Dallas Maintenance Facility was congested during peak operating periods. A stochastic model of this facility was developed to determine the resource requirements needed to provide adequate service during periods of peak demand. The structure of the simulation model is described. A representative sensitivity analysis is presented to discuss how this model was used to support facility sizing decisions. Based on our simulation experiments, we concluded that the existing site, with appropriate modifications, could accommodate peak traffic with some room for growth.
Toward a Unified Security-Safety Model
Authors: Gary Stoneburner
Venue: Computer (2006)

A common risk taxonomy would yield more dependable real-world systems.
A Hash-based Pseudonymization Infrastructure for RFID Systems
Authors: Dirk Henrici, Joachim Gotze, Paul Muller
Venue: Proceedings of the Second International Workshop on Security, Privacy and Trust in Pervasive and Ubiquitous Computing (2006)

Many proposals have been made to solve the privacy implications of RFID systems: The main idea to ensure location privacy is to change the identifiers of RFID tags regularly. For building inter-organizational RFID systems, pseudonyms can be used to provide a link to the respective owner of a tag without affecting location privacy. Based on these considerations, in this paper a pseudonymization infrastructure is presented that is based on one-way hash functions and thus is a better fit for the specific demands of resource scarce tags than approaches based on public key cryptography.
A comprehensive service discovery solution for mobile ad hoc networks
Authors: Jerry Tyan, Qusay H. Mahmoud
Venue: Mobile Networks and Applications (2005)

Service discovery is an integral part of constructing self-configuring Mobile Ad Hoc Networks (MANETs). While several service discovery protocols have been developed, most of them are designed for infrastructure-based networks and thus not suitable to be used in MANETs. Due the volatile nature of MANET, service discovery protocols designed for MANETs often suffer from problems. Firstly, they have limited scalability due to the extensive use of broadcast communication. Secondly, they usually lack context-aware selection mechanisms and feature only reduced interaction standards. This paper proposes a network layer supported comprehensive service discovery solution that addresses the above issues and provides a solution in two parts. First it discusses a location-aware network layer routing protocol that groups mobile nodes into clusters while a gateway in each cluster is responsible for routing. Secondly, this paper proposes a service discovery protocol that utilizes directories for service discovery that interact with lower network layer gateway configurations. In this architecture, gateways are also used as directories for service discovery to improve performance. In doing so, we improve the system's overall efficiency by eliminating the use of complex graph-based algorithms for maintaining clusters and establishing virtual backbones. Furthermore, this service discovery solution includes an agent-based context-aware service selection, and provides standard mechanisms for different types of service interaction.
Analyzing incentives and scheduling in a major metropolitan hospital operating room through simulation
Authors: David M. Ferrin, Martin J. Miller, Sherry Wininger, Michael S. Neuendorf
Venue: Proceedings of the 36th conference on Winter simulation (2004)

This paper discusses the application of simulation to analyze the value proposition and construction of an incentive program in an Operating Room (OR) environment. The model was further used to evaluate operational changes including scheduling processes within the OR and utilization rates in areas such as Post Anesthesia Care Unit (PACU) and the Ambulatory Surgery Department (ASD). Lessons learned are presented on developing multiple simulation models from one application as well as issues regarding model transition to a client.
Model checking knowledge, strategies, and games in multi-agent systems
Authors: Alessio Lomuscio, Franco Raimondi
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

We present an OBDD-based methodology for verifying time, knowledge, and strategies in multi-agent systems specified by the formalism of interpreted systems. To this end, we investigate the interpretation of ATL and epistemic formulae in various classes of interpreted systems, we present model checking algorithms and their implementation, and report experimental results.
Efficient iteration in admissible combinatorial classes
Authors: Conrado Martínez, Xavier Molinero
Venue: Theoretical Computer Science (2005)

The exhaustive generation of combinatorial objects has a vast range of practical applications and is a common theme in the combinatorial research field. But most previous works in this area concentrate in the efficient generation of particular families of combinatorial objects. The novel approach of the work presented here is to provide efficient generic algorithms, where the input is not just the size n of the objects to be generated but a finite specification of the combinatorial class whose objects we want to list. Since the algorithms are generic, they do not exploit any particular feature of the class to be generated; nevertheless, they work in constant amortized time per generated object, that is, they generate all N objects of a given size in Θ(N) time. These algorithms are useful for both rapid prototyping and for inclusion into general purposes libraries because of their flexibility, with only a relatively modest penalty on efficiency. Furthermore, the framework presented in this paper nicely combines with the framework developed by Flajolet et al. for the enumeration and random generation of combinatorial objects, and with the framework developed by the authors for the unranking of combinatorial objects.
Policy-based routing with non-strict preferences
Authors: Chi-kin Chau
Venue: Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications (2006)

Traditional studies of routing problems often assumed strict preferences on paths, by eliminating ambiguity in path comparisons, or imposing a priori deterministic tie-breaking. Such an assumption is outpaced by today's common practice of non-deterministic,multi-path routing, which is crucial to traffic engineering, QoS routing, multicasting and virtual private networking. A pair of paths may be incomparable or equally preferred. In the presence of ambiguous preferences at pairs, or even multiple collections of paths, a challenge is to ensure robustness in the complex and sophisticated situations of policy-based routing where heterogeneous routing policies are allowed among routing systems. This paper presents an extensive study of policy-based routing with non-strict preferences, deriving sufficient conditions that ensure the existence, optimality and asynchronous convergence of stable routings.