ppxscal/embedding_network huggingface dataset:
Asynchronous Messaging between Web Services Using SSDL
Authors: Savas Parastatidis, Simon Woodman, Jim Webber, Dean Kuo, Paul Greenfield
Venue: IEEE Internet Computing (2006)

The SOAP Service Description Language (SSDL) is designed for describing asynchronous, message-oriented, and multimessage interactions between Web services. SSDL provides the basis for a range of protocol description frameworks. At one end of the spectrum, such frameworks can be simple, SOAP-centric replacements for the Web Services Description Language. At the other end, they're a more expressive contract-definition language enabling formal verification of asynchronous application protocol properties. This is possible because SSDL focuses on the "message" abstraction as the building block for service-oriented applications.
The history of SIGSIM
Authors: Dave Brandin
Venue: ACM SIGSIM Simulation Digest (1973)

This brief article describes the history of SIGSIM during its formative years (1967--1969) and explains the motivation and forces surrounding the creation of the SIG.
An Agile Programming Model for Grid End Users
Authors: Zhiwei Xu, Chengchun Shu, Haiyan Yu, Haozhi Liu
Venue: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (2005)

Grid and service computing technologies have been explored by enterprises to promote integration, sharing, and collaboration. However, quick response to business environment changes is still a challenging issue. For end users, developing, customizing, and reengineering applications remain a difficult and timeconsuming task. Users still need to deal with excessive low-level details of platform-specific APIs. We present a high-level programming model together with a descriptive glueing language called GSML, to facilitate end-user programming. In this approach, applications could be visually composed from welldefined software components called "funnels" in an event-driven fashion. Application examples have shown that, by raising the level of abstraction as well as simplifying the programming model, GSML could empower end users to build grid applications on demand with improved productivity.
Negative bias temperature instability in CMOS devices
Authors: S. Mahapatra, M. A. Alam, P. Bharath Kumar, T. R. Dalei, D. Varghese, D. Saha
Venue: Microelectronic Engineering (2005)

This paper reviews the experimental and modeling efforts to understand the mechanism of Negative Bias Temperature Instability (NBTI) in p-MOSFETs, which is becoming a serious reliability concern for analog and digital CMOS circuits. Conditions for interface and bulk trap generation and their dependence on stress voltage and oxide field, temperature and time are discussed. The role of inversion layer holes, hot-holes and hot-electrons are also discussed. The recovery of generated damage and its bias, temperature and AC frequency dependence are discussed. The degradation and recovery is modeled using the standard Reaction-Diffusion theory, and some unique data scaling features are pointed out. The impact of gate-oxide nitridation is also reviewed.
Aspects of the Binary CMAC: Unimodularity and Probabilistic Reconstruction
Authors: Anil Menon
Venue: Neural Processing Letters (2005)

The CMAC is a neural net for computing real-valued functions. Conceptually, the CMAC maps a point in the function's domain to a set of locations in an associative memory. Each location "contains" a weight, and the sum of the weights is taken to be the function's value at that point. The overall process may be modeled as the multiplication of an input vector by an "association matrix." This paper highlights some aspects of the CMAC's mapping and function computation procedures. Regarding the mapping procedure, it is shown that the associative matrix of a univariate CMAC has the consecutive-ones property; this implies that the matrix is totally-unimodular, a property of great importance in integer optimization. For a multivariate CMAC, the association matrix can be partitioned into sub-matrices, each with the consecutive-ones property. Regarding the function computation procedure, it is shown that a univariate CMAC can compute a function exactly iff a certain distribution is reconstructible from its one dimensional marginals. A CMAC extension, free of this limitation and derived from the theory of balanced matrices, is briefly discussed. These results are generalizable to multivariate CMACs in a relatively straightforward manner.
Football Predictions Based on a Fuzzy Model with Genetic and Neural Tuning
Authors: A. P. Rotshtein, M. Posner, A. B. Rakityanskaya
Venue: Cybernetics and Systems Analysis (2005)

A model is proposed for predicting the result of a football match from the previous results of both teams. This model underlies the method of identifying nonlinear dependencies by fuzzy knowledge bases. Acceptable simulation results can be obtained by tuning fuzzy rules using tournament data. The tuning procedure implies choosing the parameters of fuzzy-term membership functions and rule weights by a combination of genetic and neural optimization techniques.
An investigative framework for studying the growth and evolution of complex supply networks
Authors: Surya Dev Pathak
Venue:  (2005)

A Supply Network (SN) is a collection of firms that maintain local autonomy but who interact together to fulfill customer requirements. SN researchers have focused on the need to understand the reasons behind the diversity in the number and types of supply networks, as well as how these diverse networks interact, change and adapt over time. My research focuses on the dynamic growth aspect of SN's and addresses two fundamental questions: (1) how do Supply Networks grow and emerge and (2) are there simple rules and conditions that control the growth and emergence process? The dissertation presents an investigative framework for studying these and other questions concerning the growth and evolution of complex supply networks. An inductive approach is used to answer these questions. It starts by creating a new theory-based unified model of supply network (called UMSN) that incorporates four theoretical lenses, namely (1) industrial growth theory, (2) network growth theory, (3) game market structure theory and (4) complex adaptive systems theory. The UMSN provides a holistic view for modeling growth and emergence in SNs. A generic rule-based modeling framework and a simulation-based computational framework using software agent technology was developed to operationalize and implement the UMSN. For investigating the growth phenomenon data and parameters from the US automobile industry over the last 80 years were utilized. The results and analysis show similar growth trends as empirically published data of well-structured industries such as the US automobile industry. The SN system grew and emerged as a complex adaptive system. The research also presents statistically significant results that supply networks grow and emerge based on interactive effects of local decision-making rules and environmental conditions, and that there is an underlying order to the emergence process. This research also develops chaos theory analysis techniques for predicting the SN system behavior over time; showing how such techniques can generate insights for policy makers and managers. My research contributes by extending the current state of knowledge of SN's as a dynamic system (network) and developing novel classification and analysis techniques. The insights drawn can aid managers/decision makers towards a better understanding of how SN emerge and grow.
Automated population of causal models for improved software risk assessment
Authors: Peter Hearty, Norman Fenton, Martin Neil, Patrick Cates
Venue: Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering (2005)

Recent work in applying causal modeling (Bayesian networks) to software engineering has resulted in improved decision support systems for software project managers. Once the causal models are built there are commercial tools that can run them. However, data to populate the models is typically entered manually and this is an impediment to their more widespread use. Hence, here we present a prototype tool for automatically extracting a range of relevant software metrics from popular project management and CASE tools. This information is used to populate Bayesian networks with the aim of providing better real world predictions of the risks associated with software costs, timescales and reliability.
Hybrid Micropower Supply for Wearable-Pervasive Sensor nodes
Authors: Nagendra B. Bharatula, Rolf Zinniker, Gerhard Troster
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

This article presents an architecture of hybrid micro power supply for wearable-pervasive sensor nodes. A lithium-ion button battery and a photo voltaic module with efficient power management circuitry are implemented as a miniaturised system. The performance was investigated in an office worker scenario with real-time power consumption measurments. It is shown that overall system efficiency in terms of battery life, low power dissipation and suitability for integration into a sensory system are highly improved. The measured power efficiencies in well-lit indoors and outdoors were reported to be in the range of 91-95%.
Proceedings of the 2005 ACM workshop on Formal methods in security engineering
Venue: Workshop on Formal Methods in Security Engineering (2005)

It is our great pleasure to welcome you to the Third ACM Workshop on Formal Methods in Security Engineering (FMSE 2005) held in conjunction with the 12th ACM Conference on Computer and Communications Security.The purpose of FMSE is to bring together researchers and practitioners from both the security and the software engineering communities, from academia and industry, who are working on applying formal methods to designing and validating large-scale security-critical systems. The scope of the workshop covers security and formal-methods related aspects of security specification techniques, formal trust models, combination of formal techniques with semi-formal techniques like UML, formal analyses of specific security properties relevant to software development, security-preserving composition and refinement of processes, faithful abstractions of cryptographic primitives and protocols in process abstractions, integration of formal security specifications, as well as refinement and validation techniques in development methods and tools.The paper selection process was very competitive this year. The call for papers attracted 22 submissions from Australia, Asia, Europe, New Zealand, and the United States. The program committee accepted 8 papers for presentation at the workshop, which means that many high-quality papers had to be rejected. In addition, the program includes invited talks by Virgil Gligor and Andrew Myers.
Implicit---Explicit Runge---Kutta Schemes and Applications to Hyperbolic Systems with Relaxation
Authors: Lorenzo Pareschi, Giovanni Russo
Venue: Journal of Scientific Computing (2005)

We consider new implicit---explicit (IMEX) Runge---Kutta methods for hyperbolic systems of conservation laws with stiff relaxation terms. The explicit part is treated by a strong-stability-preserving (SSP) scheme, and the implicit part is treated by an L-stable diagonally implicit Runge---Kutta method (DIRK). The schemes proposed are asymptotic preserving (AP) in the zero relaxation limit. High accuracy in space is obtained by Weighted Essentially Non Oscillatory (WENO) reconstruction. After a description of the mathematical properties of the schemes, several applications will be presented
Error limiting reductions between classification tasks
Authors: Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

We introduce a reduction-based model for analyzing supervised learning tasks. We use this model to devise a new reduction from multi-class cost-sensitive classification to binary classification with the following guarantee: If the learned binary classifier has error rate at most &epsilon; then the cost-sensitive classifier has cost at most 2&epsilon; times the expected sum of costs of all possible lables. Since cost-sensitive classification can embed any bounded loss finite choice supervised learning task, this result shows that any such task can be solved using a binary classification oracle. Finally, we present experimental results showing that our new reduction outperforms existing algorithms for multi-class cost-sensitive learning.
Real time advertisement insertion in baseball video based on advertisement effect
Authors: Yiqun Li, Kong Wah Wan, Xin Yan, Changsheng Xu
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

In this paper, we propose a novel method to detect baseball video scene for commercial advertisement insertion. The method uses the criteria based on better advertisement effect to generate a set of rules. From these rules, proper timing (starting time and ending time) and location are identified to insert the advertisement in the broadcast baseball video automatically. The proper timing is detected based on the consistent existence of simple background objects in the video for a period of time. The proper location is identified by less-informative-region detection in the video. The rules make sure that the critical information of the video is not blocked by the advertisement and the advertisement is always stable, clear and viewable on the stationary background of the video. The experimental results are encouraging.
A Locating Mechanism for Multiple Mobile Nodes in Wireless Sensor Networks
Authors: Sung-Min Lee, Hojung Cha
Venue: Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2005)

Current tracking applications in sensor networks lack bi-directional communications with mobile objects. They have a passive communication relationship with uni-directional location data messages routed from mobile objects to the central processing node. In order for an object tracking system to be an "active" tracking system, where the user of the system is able to control the mobile objects in various ways, the system first needs an efficient way to deliver control messages to a mobile object, other than a simple flooding of the control messages. This paper presents an Efficient Detection Method for Multiple Mobile Nodes (EDMM), which yields a reduction in the number of flooding messages by adopting the hybrid solution of combining a full-scale flooding and a constrained flooding for the delivery of control messages. Simulation shows that our solution provides about 90% to 10% of reduction on both energy consumption and network traffic comparing to full-scale flooding when the number of mobile nodes increases by 1 to 9.
A Dynamic Bayesian Network for Handling Uncertainty in a Decision Support System Adapted to the Monitoring of Patients Treated by Hemodialysis
Authors: Cedric Rose, Cherif Smaili, Francois Charpillet
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

Telemedicine is a mean of facilitating the distribution of human resources and professional competences. It can speed up diagnosis and therapeutic care delivery and allow peripheral healthcare providers to receive continuous assistance from specialized centers. The need of specialized human resources becomes critical with the aging of the population. The treatment of renal failure is an example where telemedicine can help to increase care quality. Over the last decades Bayesian networks has become a popular representation for encoding uncertain expert knowledge. Dynamic Bayesian networks are an extension of Bayesian networks for modeling dynamic processes. We developed a dynamic Bayesian network adapted to the monitoring of the dry weight of patients suffering from chronic renal failure treated by hemodialysis. An experimentation conducted at dialysis units indicated that the system is reliable and gets the approbation of its users.
A totally distributed and adjustable scheduling algorithm in wireless Ad-Hoc networks
Authors: M. GILG, P. LORENZ
Venue: Proceedings of the Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services (2005)

In a half duplex mobile Ad-Hoc network, fair allocation of bandwidth and maximization of channel utilization are two antagonistic parameters. In this paper, we propose to use a totally distributed scheduling algorithm with one control parameter. This algorithm permits to maintain a minimum transmission rate for the nodes and to preserve the throughput of the network.
Context derivation sets and context-free normal forms
Authors: Charlotte Miller
Venue: Journal of Automata, Languages and Combinatorics (2004)

A set of transformations is presented that will convert an arbitrary context-tree grammar to six of the normal forms for which the right hand side of any production has at most two occurrences of nonterminal symbols. These transformations form the basis of a meta-normal form algorithm for context-free grammars. The algorithm takes as input an arbitrary context-free grammar and a target normal form, expressed as an extended two-symbol grammar form, and converts the grammar to that normal form. The number of nonterminals and productions in the output grammars of each of the base transformations is minimal.
Energy aware memory architecture configuration
Authors: Hanene Ben Fradj, Asmaa el Ouardighi, Cécile Belleudy, Michel Auguin
Venue: Proceedings of the 2004 workshop on MEmory performance: DEaling with Applications , systems and architecture (2004)

In the context of battery-driven embedded systems, reducing energy while maintaining performance is one of today's challenges. The on-chip memory count for a great part of the whole system consumption, especially for images and video processing applications that make heavy use of large memory data size.In this paper, we present new technique for efficiently exploiting on-chip memory space (cache, scrathpad) for a specific application to reduce the energy consumption without loss of performance. We configure and compare the impact of three different memory architectures on the energy consumption. The first one is composed of main memory with cache, in the second architecture we find a main memory and scratchpad memory and in the last architecture we combine both cache and scratchpad with the main memory. We show the effectiveness of the last architecture and a saving about 35% in energy consumption.
Supervised Tensor Learning
Authors: Dacheng Tao, Xuelong Li, Weiming Hu, Stephen Maybank, Xindong Wu
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n^th-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.
Numerical solution of the nonlinear Helmholtz equation using nonorthogonal expansions
Authors: G. Fibich, S. Tsynkov
Venue: Journal of Computational Physics (2005)

In [J. Comput. Phys. 171 (2001) 632-677] we developed a fourth-order numerical method for solving the nonlinear Helmholtz equation which governs the propagation of time-harmonic laser beams in media with a Kerr-type nonlinearity. A key element of the algorithm was a new nonlocal two-way artificial boundary condition (ABC), set in the direction of beam propagation. This two-way ABC provided for reflectionless propagation of the outgoing waves while also fully transmitting the given incoming beam at the boundaries of the computational domain. Altogether, the algorithm of [J. Comput. Phys. 171 (2001) 632-677] has allowed for a direct simulation of nonlinear self-focusing without neglecting nonparaxial effects and backscattering. To the best of our knowledge, this capacity has never been achieved previously in nonlinear optics. In the current paper, we propose an improved version of the algorithm. The principal innovation is that instead of using the Dirichlet boundary conditions in the direction orthogonal to that of the laser beam propagation, we now introduce Sommerfeld-type local radiation boundary conditions, which are constructed directly in the discrete framework. Numerically, implementation of the Sommerfeld conditions requires evaluation of eigenvalues and eigenvectors for a non-Hermitian matrix. Subsequently, the separation of variables, which is a key building block of the aforementioned nonlocal ABC, is implemented through an expansion with respect to the nonorthogonal basis of the eigenvectors. Numerical simulations show that the new algorithm offers a considerable improvement in its numerical performance, as well as in the range of physical phenomena that it is capable of simulating.
Improving Parallel Ordering of Sparse Matrices Using Genetic Algorithms
Authors: Wen-Yang Lin
Venue: Applied Intelligence (2005)

In the direct solution of sparse symmetric and positive definite linear systems, finding an ordering of the matrix to minimize the height of the elimination tree (an indication of the number of parallel elimination steps) is crucial for effectively computing the Cholesky factor in parallel. This problem is known to be NP-hard. Though many effective heuristics have been proposed, the problems of how good these heuristics are near optimal and how to further reduce the height of the elimination tree remain unanswered. This paper is an effort for this investigation. We introduce a genetic algorithm tailored to this parallel ordering problem, which is characterized by two novel genetic operators, adaptive merge crossover and tree rotate mutation. Experiments showed that our approach is cost effective in the number of generations evolved to reach a better solution in reducing the height of the elimination tree.
LOCH: Supporting Informal Language Learning Outside the Classroom with Handhelds
Authors: Rosa G. J. Paredes, Hiroaki Ogata, A. Nobuji, Yasuko Oishi, Takahito Ueda
Venue: Proceedings of the IEEE International Workshop on Wireless and Mobile Technologies in Education (2005)

The continuous development of wireless and mobile technologies has allowed the creation of an additional platform for supporting learning, one that can be embedded in the same physical space in which the learning is taking place. This paper describes a computer supported ubiquitous learning environment for language learning, called LOCH (Languagelearning Outside the Classroom with Handhelds). In the environment, the teacher assigns field activities to the students, who go around the town to fulfill them and share their individual experiences. The main aim of this project, called One Day Trip with PDA (Personal Digital Assistant), was to integrate the knowledge acquired in the classroom and the real needs of the students in their daily life.