ppxscal/embedding_network huggingface dataset:
Towards a better understanding of the it program at a female saudi university
Authors: Hend S. Al-Khalifa, Nadia S. Al-Ghreimil, Maha M. Al-Yahya
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

Four years ago the College of Computer and Information Sciences at King Saud University started the development of a new Bachelor of Science degree majoring in Information Technology for female students. As the program is now in its second year of application, this paper presents the current program implementation, a comparison of its curriculum against national and international programs and the compliance of the current program with international standards.
Multi-dimensional features reduction of PCA on SVM classifier for imaging surveillance application
Authors: Tan Chue Poh, Nur Fateha Muhamad Lani, Lai Weng Kin
Venue: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (2008)

This paper presents the application of multi dimensional feature reduction of Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the popularly used SVM classifier is performed to focus on the need of detecting dangerous weapons. However, PCA is used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the SVM classifier, several classifiers are used to compare the overall accuracy and computational speed of the system with the compliment from the features reduction of PCA. These classifiers include Neural Network, Decision Trees, Naïve Bayes and k-Nearest Neighbor methods. The final outcome of this research clearly indicates that SVM has the ability in improving the classification accuracy using the extracted features. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features while maintaining the classification accuracy.
A study of hydrodynamic mass-consistent models
Authors: Marco A. Núñez, Ciro Flores, Héctor Juárez
Venue: Journal of Computational Methods in Sciences and Engineering (2006)

Variational mass consistent models (MCM's) provide a solenoidal field V in a region &Omega; by minimizing a metric $||V-V^0||_{&Omega;S}^2$ subject to &nabla; &middot; V=0, where $S_{ij}=&alpha; _{i}^{2}&delta; _{ij}$ is a symmetric and positive definite matrix. A least-squares approach suggests that $S^{-1}$ can be estimated with a Gaussian statistics. This approach is not valid in general, instead, it is shown that MCM's constitute a general scheme to get solenoidal fields V where the $&alpha; _i $'s are distributed parameters that can be estimated by means regularization methods. The range of values of $&alpha; ^{2}=&alpha; _1^2/&alpha;_3^2$ considered in the literature is $[10^{-12},&infin;)$. It is shown that V becomes singular as $&alpha; ^{2}&rarr; 0$ or &infin; and this behavior together with the loss of regularity of &lambda; on the boundary of &Omega; produce a spurious sensitivity of the residual divergence, which was proposed to estimate the optimal ratio $&alpha;^2$. Several simulations with MCM's use the boundary condition &part; &lambda;/&part; n=0 but it is not valid general. A deduction of MCM's in terrain-following coordinates is given and it is shown that such coordinates may hide the use of inconsistent boundary conditions. Numerical examples illustrate significative changes in the field V when the condition &part; &lambda;/&part; n=0 is used.
Distributed Lyapunov Functions in Analysis of Graph Models of Software
Authors: Mardavij Roozbehani, Alexandre Megretski, Emilio Frazzoli, Eric Feron
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

In previous works, the authors introduced a framework for software analysis, which is based on optimization of Lyapunov invariants. These invariants prove critical software properties such as absence of overflow and termination in finite time. In this paper, graph models of software are introduced and the software analysis framework is further developed and extended on graph models. A distributed Lyapunov function is assigned to the software by assigning a Lyapunov function to every node on its graph model. The global decremental condition is then enforced by requiring that the Lyapunov functions on each node decrease as transitions take place along the arcs. The concept of graph reduction and optimality of graphs for Lyapunov analysis is briefly discussed.
Second International Workshop on Multi-Paradigm Modeling: Concepts and Tools
Authors: Juan Lara, Tihamér Levendovszky, Pieter J. Mosterman, Hans Vangheluwe
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

The comprehensive use of models in design has created a set of challenges beyond that of supporting one isolated design task. In particular, the need to combine, couple, and integrate models at different levels of abstraction and in different formalisms is posing a set of specific problems that the field of Computer Automated Multi-Paradigm Modeling (CAMPaM) is aiming to address. This paper summarizes the results of the 2nd Workshop on Multi-Paradigm Modeling: Concepts and Tools.
The potential of synchronous communication to enhance participation in online discussions: A case study of two e-learning courses
Authors: Stefan Hrastinski
Venue: Information and Management (2008)

Computer-mediated communication (CMC) has been adopted in most e-learning settings. However, few research studies have considered the effect of different CMC. This study examined how and why synchronous communication affected participation in online discussions. Two online classes that participated in two asynchronous and two synchronous online discussions were examined. Actual and perceived measures of participation indicated that synchronous communication induced personal participation, which could be regarded as a complement to cognitive participation. Personal participation involves more intense interaction better supported by synchronous communication while cognitive participation is a more reflective type of participation supported by asynchronous communication. In synchronous discussions, the e-learners felt that they worked together and were not restricted to only discuss course content. This was likely to induce arousal and motivation and increased convergence on meaning, especially in small groups.
Neural Network Mapping of Magnet Based Position Sensing System for Autonomous Robotic Vehicle
Authors: Dae---Yeong Im, Young-Jae Ryoo, Jang-Hyun Park, Hyong-Yeol Yang, Ju-Sang Lee
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

In this paper a neural network mapping of magnet based position sensing system for an autonomous robotic vehicle. The position sensing system using magnetic markers embedded under the surface of roadway pavement. An important role of magnetic position sensing is identification of vehicle's location. The magnetic sensor measures lateral distance when the vehicle passes over the magnetic marker. California PATH has developed a table-look-up as an inverse map. But it's requires too many memories to store the vast magnetic field data. Thus we propose the magnetic guidance system with simple mapping using neural network.
Specifying Flexible Charging Rules for Composable Services
Authors: Brendan Jennings, Lei Xu, Eamonn de Leastar
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Where services are offered on a commercial basis, the manner in which charges for service usage are calculated is of key importance. Services typically have associated with them a charging scheme specifying the rules for charge calculation; schemes can range from the simple (such fixed charge per service invocation) to highly complex (where charges are calculated dynamically in order to influence customer demand and thereby optimise overall system performance). Typically, charging schemes are manually configured and verified prior to services being made available to customers – typcially, a time consuming and expensive process. In environments where service compositions can be rapidly built and offered to customers, manual specification of a charging scheme for the service composition becomes untenable. In this paper we describe how charge modification rules associated with individual servies can be used to flexibly govern how a service is charged for when it is used in the context of a composed service.
The JIGSAW Algorithm for Word Sense Disambiguation and Semantic Indexing of Documents
Authors: P. Basile, M. Degemmis, A. L. Gentile, P. Lops, G. Semeraro
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. In fact, a breakthrough in this field would have a significant impact on many relevant fields, such as information retrieval and information extraction. This paper describes JIGSAW, a knowledge-based WSD algorithm that attemps to disambiguate all words in a text by exploiting WordNet senses. The main assumption is that a Part-Of-Speech (POS)-dependent strategy to WSD can turn out to be more effective than a unique strategy. Semantics provided by WSD gives an added value to applications centred on humans as users. Two empirical evaluations are described in the paper. First, we evaluated the accuracy of JIGSAW on Task 1 of SEMEVAL-1 competition. This task measures the effectiveness of a WSD algorithm in an Information Retrieval System. For the second evaluation, we used semantically indexed documents obtained through a WSD process in order to train a naïve Bayes learner that infers "semantic" sense-based user profiles as binary text classifiers. The goal of the second empirical evaluation has been to measure the accuracy of the user profiles in selecting relevant documents to be recommended within a document collection.
A Remote Analysis Server - What Does Regression Output Look Like?
Authors: Christine M. O'Keefe, Norm M. Good
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

This paper is concerned with the problem of balancing the competing objectives of allowing statistical analysis of confidential data while maintaining standards of privacy and confidentiality. Remote analysis servers have been proposed as a way to address this problem by delivering results of statistical analyses without giving the analyst any direct access to data. Several national statistical agencies operate successful remote analysis servers, see for example [1,12].Remote analysis servers are not free from disclosure risk, and current implementations address this risk by "confidentialising" the underlying data and/or by denying some queries. In this paper we explore the alternative solution of "confidentialising" the output of a server so that no confidential information is revealed or can be inferred.In this paper we first review relevant results on remote analysis servers, and provide an explicit list of measures for confidentialising the output from a single regression query to a remote server, as suggested by Sparks et al. [22,23]. We give details of a fully worked example, and compare the confidentialised output from the query to a remote server with the output from a traditional statistical package.
Parallel Numerical Algorithms for 3D Parabolic Problem with Nonlocal Boundary Condition
Authors: Raimondas Čiegis
Venue: Informatica (2006)

Three parallel algorithms for solving the 3D problem with nonlocal boundary condition are considered. The forward and backward Euler finite-difference schemes, and LOD scheme are typical representatives of three general classes of parallel algorithms used to solve multidimensional parabolic initial-boundary value problems. All algorithms are modified to take into account additional nonlocal boundary condition. The algorithms are implemented using the parallel array object tool ParSol, then a parallel algorithm follows semi-automatically from the serial one. Results of computational experiments are presented and the accuracy and efficiency of the presented parallel algorithms are tested.
Content-Based Identifying and Classifying Traditional Chinese Painting Images
Authors: Guanming Lu, Zhong Gao, Danni Qin, Xin Zhao, Mengjue Liu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

As traditional Chinese painting (TCP) occupies an important place in the life of modern Chinese, there are a lot of TCP images digitalized and exhibited on the Internet. However, effective identification and classification in them are an imperative problem need to be addressed. The paper proposes a content-based identification and classification scheme that represents the visual content of TCP images by chromatic and textural feature set. Four kinds of classifier implemented in the scheme learn the characteristics of fundamental TCP style, art movements and painters. The experimental results show that the scheme is capable of identifying the TCP image and classifying them based on painters as well as art movements with an accuracy of greater than 85%.
Mean Weight Behavior of Coupled LMS Adaptive Systems Applied to Acoustic Feedback Cancellation in Hearing Aids
Authors: Yasmín Montenegro M., José C. Bermudez
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

This paper presents a transient analysis of a hearing aids adaptive feedback canceller. The system employs an LMS adaptive estimator and an LMS adaptive predictor operating simultaneously. The nature of the practical problem makes the input to the adaptive estimator and the interference to its output statistically correlated. First, a modification is proposed to the original structure that accelerates convergence without compromising the cancellation level. The modified structure is then analyzed for slow adaptation and for an autoregressive input signal. Analytical models are derived for the mean behavior of the adaptive weights. Monte Carlo simulations verify the accuracy of the derived model.
Adaptive Image Segmentation Using Modified Pulse Coupled Neural Network
Authors: Wei Cai, Gang Li, Min Li, Xiaoyan Li
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

This paper proposes a novel image segmentation algorithm based on Pulse Coupled Neural Network (PCNN).Unlike the traditional PCNN image segmentation methods, the presented algorithm can achieve the optimum parameters automatically. Experimental results show its good performance and robustness. The research fruits have great importance both on the theory research and practical application of PCNN.
Architectural Decisions and Patterns for Transactional Workflows in SOA
Authors: Olaf Zimmermann, Jonas Grundler, Stefan Tai, Frank Leymann
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

An important architectural style for constructing enterprise appli ca ti ons is to use transactional workflows in SOA. In this setting, workflow ac ti vi ties invoke distributed services in a co or di nated manner, using transaction con text-pro pa ga ting messages, coordi na tion pro to cols, and compensation logic. De signing such trans actional workflows is a time-con su ming and error-prone task re quiring deep subject matter exper tise. Aiming to alleviate this prob lem, we in tro duce a new analysis and design method that (a) identifies recurring ar chi tec tural de ci si ons in analysis-level process models, (b) models alter na tives for these decisions as reusable, plat form-independent pat terns and pri mitives, and (c) maps the pat terns and pri mi tives into technology- and plat form-specific set tings in BPEL and SCA. Our method accelerates the identification of decisions, empowers pro cess modelers to make informed de ci sions, and au to ma tes the enforcement of the decisions in de ployment artifacts; tool support is available. We de mon strate value and fea si bi li ty of our method in an industry case study.
Experience Sharing on SOA Based Heterogeneous Systems Integration
Authors: Geetha Presenna Kumari, Balaji Kandan, Asish Kumar Mishra
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

More and more enterprises are realizing that SOA is a reality than a myth. Information system landscape of most enterprises is heterogeneous in nature and comprises of legacy and modern systems. In many cases, applications with in an IT landscape might not be able to communicate or share data among themselves due to lack of common standards. To make SOA a reality, IT requires intelligent service infrastructure that drives and simplifies service reuse and delivers reliable integration across an inherently heterogeneous, multi-vendor computing landscape. This abstract discusses an open standards based reengineered solution employing SOA principles and deployed on a SOA infrastructure for service provisioning business process of a major telecom provider. The solution addressed the complex integration requirements that existed in the customer’s environment.
Clustering Trees with Instance Level Constraints
Authors: Jan Struyf, Sašo Džeroski
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on the set of clusters. We consider instance level constraints, such as must-link and cannot-link. This type of constraints has been successfully used in popular clustering algorithms, such as k-means and hierarchical agglomerative clustering. This paper shows how clustering trees can support instance level constraints. Clustering trees are decision trees that partition the instances into homogeneous clusters. Clustering trees provide a symbolic description for each cluster. To handle non-trivial constraint sets, we extend clustering trees to support disjunctive descriptions. The paper's main contribution is ClusILC, an efficient algorithm for building such trees. We present experiments comparing ClusILC to COP-k-means.
Technology diffusion: analysing the diffusion of agent technologies
Authors: Jez Mckean, Hayden Shorter, Michael Luck, Peter Mcburney, Steven Willmott
Venue: Autonomous Agents and Multi-Agent Systems (2008)

Despite several examples of deployed agent systems, there remain barriers to the large-scale adoption of agent technologies. In order to understand these barriers, this paper considers aspects of marketing theory which deal with diffusion of innovations and their relevance to the agents domain and the current state of diffusion of agent technologies. In particular, the paper examines the role of standards in the adoption of new technologies, describes the agent standards landscape, and compares the development and diffusion of agent technologies with that of object-oriented programming. The paper also reports on a simulation model developed in order to consider different trajectories for the adoption of agent technologies, with trajectories based on various assumptions regarding industry structure and the existence of competing technology standards. We present details of the simulation model and its assumptions, along with the results of the simulation exercises.
Movies Recommendation Networks as Bipartite Graphs
Authors: Jelena Grujić
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

In this paper we investigate the users' recommendation networks based on the large data set from the Internet Movie Database. We study networks based on two types of inputs: first (monopartite) generated directly from the recommendation lists on the website, and second (bipartite) generated through the users' habits. Using a threshold number of votes per movie to filter the data, we actually introduce a control parameter, and then by tuning this parameter we study its effect on the network structure. From the detailed analysis of both networks we find that certain robust topological features occur independently from the value of the control parameter. We also present a comparison of the network clustering and shortest paths on the graphs with a randomized network model based on the same data.
Efficient multi-agent reinforcement learning through automated supervision
Authors: Chongjie Zhang, Sherief Abdallah, Victor Lesser
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision framework to speed up the convergence of MARL algorithms in a network of agents. The framework defines an organizational structure for automated supervision and a communication protocol for exchanging information between lower-level agents and higher-level supervising agents. The abstracted states of lower-level agents travel upwards so that higher-level supervising agents generate a broader view of the state of the network. This broader view is used in creating supervisory information which is passed down the hierarchy. We present a generic extension to MARL algorithms that integrates supervisory information into the learning process, guiding agents' exploration of their state-action space.
Proposal for Agent Platform Dynamic Interoperation Facilitating Mechanism
Authors: Xiaolu Li, Takahiro Uchiya, Susumu Konno, Tetsuo Kinoshita
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Interoperability among agent platforms is important for making full use of each platform's problem-solving capabilities. It is also crucial to the upper layer interoperations (re-organization, negotiation, etc.). Static interoperation as enabled by pre-configuration has been proposed and implemented, although no satisfactory mechanism for dynamic interoperation is available. We propose a mechanism to facilitate the dynamic interoperation between agents situated in different computers by discovering the underlying agent platforms and using that information to enable and support the interoperation process.
Faster algorithms for guided tree edit distance
Authors: Dekel Tsur
Venue: Information Processing Letters (2008)

The guided tree edit distance problem is to find a minimum cost series of edit operations that transforms two input forests F and G into isomorphic forests F^' and G^' such that a third input forest H is included in F^' (and G^'). The edit operations are relabeling a vertex and deleting a vertex. We show efficient algorithms for this problem that are faster than the previous algorithm for this problem of Peng and Ting [Z. Peng, H. Ting, Guided forest edit distance: Better structure comparisons by using domain-knowledge, in: Proc. 18th Symposium on Combinatorial Pattern Matching (CPM), 2007, pp. 28-39].