ppxscal/embedding_network huggingface dataset:
Camouflaged fraud detection in domains with complex relationships
Authors: Sankar Virdhagriswaran, Gordon Dakin
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

We describe a data mining system to detect frauds that are camouflaged to look like normal activities in domains with high number of known relationships. Examples include accounting fraud detection for rating and investment, insider attacks on corporate networks, and health care insurance fraud. Our goal is to help analysts who are overwhelmed with information about companies or on-line system access logs or insurance claims to focus their attentions on features that cause damage in the future. We focused on accounting fraud where the task is to detect the subset of companies that were potentially committing accounting fraud within the total population of public companies that file quarterly and annual filings with the Securities and Exchange Commission (SEC). Using (a) Representation of changes, (b) A mix of decision tree learning, locally weighted logistic regression, k-means clustering, and constant regression in a two phase pipe line, we developed models that rank companies based on the probability of forecasting future damaging performance. The learned models were tested extensively over four years with public data available from SEC filings and private data available from rating companies and investment firms. Cross validation experiments and analyst based validation of private experiments were found to show that the approach performed as well as or better than domain experts and discovered new relationships that domain experts did not use on a regular basis. Finally, the detections preceded public knowledge of such problems by six to eighteen months.
Key research issues in grid workflow verification and validation
Authors: Jinjun Chen, Yun Yang
Venue: Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54 (2006)

In the grid architecture, a grid workflow system is a type of high-level grid middleware which is supposed to support modelling, redesign and execution of large-scale sophisticated e-science and e-business processes in many complex scientific and business applications. To ensure the correctness of grid workflow specification and execution, grid workflow verification and validation must be conducted so that we can identify any violations and consequently take proper action to remove them in time. However, current research on the grid workflow verification and validation is just at the early stage and very few projects focus on them. Therefore, a systematic identification of key research issues in the grid workflow verification and validation field is no doubt helpful and should be presented so that we can be on the right track and reduce unnecessary work as far as possible. Hence, in this paper, we systematically analyse the grid workflow verification and validation and investigate their key research issues. Especially, we identify some important open research points which are not discussed by the current research and hence need further investigation. All these analyses form a big picture for the grid workflow verification and validation.
The Failure-rate Aware Scheduling Policies for Large-scale Cluster Systems
Authors: Linping Wu, Chao Ren, Dan Meng, Zhan Jianfeng, Bibo Tu
Venue: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (2006)

With the scale expanding, node failures become one of the important obstacles when using large-scale cluster systems. The traditional scheduling policies of cluster only took into account the factors such as jobs priority and node load with the node failure rate omitted. The function of job scheduling in cluster system can be divided into two sub-processes: job selection process and node allocation process. In this paper, we introduce several scheduling policies considering the node failure rate with which the more dependable nodes are selected during the node allocation process. In the end, we use the Discrete Event-Driven Simulation method to evaluate the policies and the simulation results show that the failure-rate aware scheduling policies do better than random node allocation policy for the system performance.
Flexible Process Control - A Neuro-Fuzzy Approach in Intelligent Workflow Model Based on Extended Petri Nets
Authors: Jianchuan Xing, Zhishu Li, Jingyu Zhang
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

With business applications going toward collectivization and internationalization, many researchers have focused their attention on intelligent workflow for inter-organizational cooperation. This paper mainly introduces a neuro-fuzzy approach for the realization of intelligent workflow management systems (WfMSs). The formal model of intelligent workflow is called Intelligent Neuro-Fuzzy Extended Petri Nets (INFEPN). INFEPN not only takes the descriptive advantages of Petri Nets, but also has learning ability like neural networks and comprehensive structure like fuzzy systems. The INFEPN model is an innovative method for intelligent workflow. The workflow management systems based on INFEPN can support process execution in the virtual enterprises and help organizations quickly react on market changes.
Fluid based simulation model for high volume DC conveyor systems
Authors: Ying Wang, Chen Zhou
Venue: Proceedings of the 37th conference on Winter simulation (2005)

In this paper, we present a fluid simulation methodology applying to high volume large conveyor networks operating in a slowly changing environment, often found in large distribution centers. Traditional discrete-event cell-based approach to simulate such networks becomes computationally challenging due to large number of events resulting from high WIP level, complex network and large conveyor footprint. The fluid simulation model is built on a Petri-Net based framework. We present the model and investigate the feasibility in modeling capability in terms of input and release control logics, performance evaluation and computational savings.
Simulating Staffing Needs for Surgical Instrument Distribution in Hospitals
Authors: Arun Kumar, Sung J. Shim
Venue: Journal of Medical Systems (2006)

Timely and accurate delivery of surgical instruments to operating rooms is critical for success in surgical operations in hospitals. The National Hospital of Singapore was facing several problems in the process of surgical instrument distribution on ad hoc orders from operating rooms. To solve the problems, the Hospital management considered adopting a new process of surgical instrument distribution on ad hoc orders that involves staffing new healthcare assistants for delivery of surgical instruments to operating rooms. Using computer simulation, this study assessed the efficiency of the new process and recommended the optimal number of healthcare assistants needed for delivery of surgical instruments on ad hoc orders, at which healthcare assistants as well as surgical instruments could be most efficiently utilized. The results show that computer simulation is an effective tool supporting decisions on staffing needs for surgical instrument distribution in hospitals.
Content-based Dissemination of Fragmented XML Data
Authors: Chee-Yong Chan, Yuan Ni
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

Content-based dissemination of data using pub/sub systems is an effective means to deliver relevant data to interested data consumers. With the emergence of XML as the standard for data representation and exchange, a lot of attention has been focused on pub/sub systems for XML-based dissemination, where subscriptions are specified using more expressive XML-based languages (e.g., XPath). In this paper, we address the problem of matching XPath-based subscriptions on fragmented XML data, which is motivated by both the prevalance of resource-constrained mobile devices for accessing/monitoring data as well as by the optimization opportunities from processing data in terms of fragments. We investigate efficient strategies to schedule and optimize the evaluation of XPath-based subscriptions on XML fragments. Our experimental results not only demonstrate the effectiveness of our proposed optimizations but also reveal several interesting performance tradeoffs.
An evaluation of Boolean expression testing techniques
Authors: R. K. Singh, Pravin Chandra, Yogesh Singh
Venue: ACM SIGSOFT Software Engineering Notes (2006)

Increase in the size and complexity of the software developed has made software testing a challenging exercise. A number of testing techniques are available but they differ in terms of statement coverage, condition coverage and particularly in fault detection capabilities. The size of the test suite also differs from one technique to other. Fault that has propagated into the system inadvertently, especially into the branch statements, have severe effects as they affect the logic of the program. In this paper, an experimental evaluation of the popular branch-testing techniques (Elmendorf's method, Boolean Operator (BOR), Modified Condition/Decision Coverage (MCDC), and Reinforced Criteria/Decision Coverage (RCDC)) is presented. These techniques are evaluated on the basis of types of faults they identify, size of the test suite and their effectiveness in fault detection. For experiments, various branch statements used and referred in literature are selected. Test cases and mutants were prepared for these branch statements. Mutants were prepared by seeding single operator and operand faults into the statements. The results indicate that for a subset of fault types BOR is effective. A variant of MCDC and RCDC demonstrate better performance on the full class of faults and are only slightly worse than Elmendorf's (CEG) method test suite.
Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach
Authors: Nizar Bouguila, Djemel Ziou
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

This paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determining the number of clusters which best describe the data. We extend the minimum message length (MML) principle to determine the number of clusters in the case of Dirichlet mixtures. Parameter estimation is done by the expectation-maximization algorithm. The resulting method is validated for one-dimensional and multidimensional data. For the one-dimensional data, the experiments concern artificial and real SAR image histograms. The validation for multidimensional data involves synthetic data and two real applications: shadow detection in images and summarization of texture image databases for efficient retrieval. A comparison with results obtained for other selection criteria is provided.
Automatic cognitive style identification of digital library users for personalization
Authors: Enrique Frias-Martinez, Sherry Y. Chen, Xiaohui Liu
Venue: Journal of the American Society for Information Science and Technology (2007)

Digital libraries have become one of the most important Web services for information seeking. One of their main drawbacks is their global approach: In general, there is just one interface for all users. One of the key elements in improving user satisfaction in digital libraries is personalization. When considering personalizing factors, cognitive styles have been proved to be one of the relevant parameters that affect information seeking. This justifies the introduction of cognitive style as one of the parameters of a Web personalized service. Nevertheless, this approach has one major drawback: Each user has to run a time-consuming test that determines his or her cognitive style. In this article, we present a study of how different classification systems can be used to automatically identify the cognitive style of a user using the set of interactions with a digital library. These classification systems can be used to automatically personalize, from a cognitive-style point of view, the interaction of the digital library and each of its users. &copy; 2007 Wiley Periodicals, Inc.
Formal Concept Analysis for Digital Ecosystem
Authors: Huaiguo Fu
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

Formal Concept Analysis (FCA) is an effective tool for data analysis and knowledge discovery. Concept lattice, which is derived from mathematical order theory and lattice theory, is the core of FCA. Many research works of various areas show that concept lattices structures is an effective platform for data mining, machine learning, information retrieval, software engineer, etc. This paper offers a brief overview of FCA and proposes to apply FCA as a tool for analysis and visualization of data in Digital ecosystem, and also discusses the applications of data mining for Digital ecosystem.
A Matlab-Based Simulation of System Stability In Frequency-Field Analysis
Authors: Zhigang Xu, Changsheng Xu
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

The frequency-field analysis to a control system says the system's steady-state response when it has a sine signal input. Use this analytic method, control system's specification can be found directly and the method is very simple. Under this we can solve many questions such as avoiding resonance, restraining molest, improving system stability and so on. Matlab(Matrix Laboratory) is a software designed for researching of matrix theory, linear algebra and numerical value accounting by PH.D Clever Moler, the doyen scientist of MathWorks. Now Matlab software has developed into an edition of 7.1 and has been adopted widely by more and more researchers and engineers for its wide account ability and OOP GUI. The application of system stability simulation in frequency-field analysis to a linear system is described. We will share our experiences and intuitions gained and encourage other control process to develop simulation- based system analysis.
Force feedback in surgery: physical constraints and haptic information
Authors: Robert D. Howe
Venue:  (2006)

Force feedback is widely assumed to enhance performance in robotic surgery, but its benefits have not yet been systematically assessed. Further, the cost of force sensors that allow force feedback is prohibitive, due to the stringent specifications imposed by the surgical environment. We address both sides of this cost benefit analysis of force feedback by investigating the specific benefits allowed by force feedback and by proposing a novel force sensor design. We demonstrate the benefit of force feedback in surgery through a series of psychophysical experiments. By investigating performance on tasks with and without force feedback, we find that the primary benefit of force feedback is that interaction forces are reduced. This results in an increase in patient safety, because high forces correlate directly with tissue trauma. Two mechanisms enable force reduction and other benefits: (1) force feedback transforms environmental interaction forces into mechanical constraints and (2) forces act as a source of information. Mechanical constraints passively reduce intrusions into environmental structures (and, thereby, forces) due to the interactions between hand compliance and environment stiffness. Because this benefit is passive, it happens without a cognitive response. Accordingly, these benefits occur instantaneously, on the time scale of mechanical interactions. Force feedback also allows additional manipulation strategies that take advantage of these physical interactions, potentially reducing mental workload of the surgeon. We also find that force feedback provides information to the surgeon. While the number of ways that forces can potentially inform surgeons is large, the interaction between training and other sources of information is complex. We find that training is necessary, in some contexts, to take full advantage of the informational benefits of force feedback in surgery. We propose a three axis force sensor design using strain gages and Shape Deposition Manufacturing (SDM), where components are embedded inside a pourable epoxy. The resulting sensor (+/-2 N range, 0.15 RMS calibration error, 0.15 N drift over five minutes) is small enough for minimally invasive surgery, waterproof, and temperature insensitive. Adapting the SDM design for mass production is straightforward (sensors are cast inreusable molds), resulting in high performance, low cost, disposable sensors.
Probabilistic policy reuse in a reinforcement learning agent
Authors: Fernando Fernández, Manuela Veloso
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

We contribute Policy Reuse as a technique to improve a reinforcement learning agent with guidance from past learned similar policies. Our method relies on using the past policies as a probabilistic bias where the learning agent faces three choices: the exploitation of the ongoing learned policy, the exploration of random unexplored actions, and the exploitation of past policies. We introduce the algorithm and its major components: an exploration strategy to include the new reuse bias, and a similarity function to estimate the similarity of past policies with respect to a new one. We provide empirical results demonstrating that Policy Reuse improves the learning performance over different strategies that learn without reuse. Interestingly and almost as a side effect, Policy Reuse also identifies classes of similar policies revealing a basis of core policies of the domain. We demonstrate that such a basis can be built incrementally, contributing the learning of the structure of a domain.
Subcontracting in a make-to-stock production system, IPA gradients for an SFM
Authors: Sameh Al-Shihabi
Venue: Proceedings of the 37th conference on Winter simulation (2005)

This work extends the single product, single facility and single warehouse Make-to-Stock (MTS) model (Zhao and Melamed 2004), by allowing subcontracting. In addition to the inventory triggering level employed in their model, a new subcontracting triggering level is used to manage the subcontracted amount. A stochastic fluid model (SFM) is used to model the MTS system with subcontracting. In the current model, the subcontractor is assumed capable of satisfying any difference between the demand and the inhouse production to keep the inventory at the subcontracting triggering level. The sensitivity of the inventory onhand, backorders and amounts subcontracted with respect to the two employed triggering levels are found using infinitesimal perturbation analysis (IPA).
Fast parameter estimation algorithms for linear FM signals
Authors: P. O'Shea
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

This paper describes computationally efficient algorithms for estimating the parameters of a complex linear FM signal in white Gaussian noise. Algorithm I deals with the high signal to noise ratio (SNR) case (above -4 dB for asymptotically long signals). Algorithms II and III are for the low SNR range. Algorithm II is based on the maximum likelihood (ML) approach, but takes advantage of the numerical conditioning of the parameter estimation problem to yield a computationally efficient algorithm. Algorithm III also has its roots in the ML procedure, but uses a suboptimal implementation to avert the difficult 2D search procedure. Simulations are provided to illustrate performance.
Simulating coated paper production and distribution
Authors: Gail Kenny, Tim Rech
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Stora Enso, domiciled in Finland, is an integrated paper, packaging and forest products company. In North America, Stora Enso is a leading producer of coated and supercalendered papers for the printing and publishing industries, and a premier producer of specialty papers. Stora Enso's North American operation continually examines its supply chain and the functions involved in sourcing, producing, and delivering its products. Because of the complexity of its manufacturing process, a strategic planning tool was needed to help management determine the appropriate improvements to the existing process. The primary objective was to balance capacity, customer service, and inventory levels on a continuing basis. The project scope encompassed two Stora Enso North American paper mills, a large sheeting operation, and up to five distribution centers. This presentation describes how Arena Simulation Software was used to develop this decision tool.
Adaptive Mobile Learning
Authors: Anastasios A. Economides
Venue: Proceedings of the Fourth IEEE International Workshop on Wireless, Mobile and Ubiquitous Technology in Education (2006)

This paper presents a general framework for adaptive mobile learning. The mobile learner performs an educational activity using the infrastructure (e.g. handheld devices, networks) in an environment (e.g. outdoors). An adaptation engine personalizes the educational activity and the infrastructure to the learner according to the context. The adaptation engine may be deterministic or probabilistic. Learning automata are employed as probabilistic adaptation engines. The context is described by the learner's state, the educational activity's state, the infrastructure's state, and the environment's state.
Modeling Hesitation and Conflict: A Belief-Based Approach for Multi-class Problems
Authors: Thomas Burger, Oya Aran, Alice Caplier
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

Support Vector Machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method does not use the entire information from the classifier responses. In this paper, we present a method which partially prevents these two losses of information by applying Belief Theories (BTs) to SVM fusion, while keeping the efficient aspect of the classical methods.
The wavelet transform of higher dimension and the Radon transform
Authors: D. M. Etter, D. P. K. Lun
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Presents a fast algorithm for the computation of the wavelet transform in higher dimensional Euclidean space R/sup n/ with arbitrary shaped wavelets. The algorithm is a direct consequence of the convolution property of the Radon transform and shows significant improvement in speed. The authors also present a novel approach for the computation of the Daubechies type wavelet transform under the Radon transform domain where the n-dimensional multiresolution analysis (MRA) is reduced to one-dimensional MRA. They found applications of this approach on, for instance, multiresolution reconstruction of a tomographic image with the standard methods of denoising, where determination of wavelet coefficients is required under the Radon transform domain. Along with the possibility of reducing samples angularly with decreasing resolution, the efficiency can be further improved. Also, extra properties such as the "rotated" wavelet can be easily implemented with this algorithm.