ppxscal/embedding_network huggingface dataset:
Segmenting Brain Tumors Using Pseudo---Conditional Random Fields
Authors: Chi-Hoon Lee, Shaojun Wang, Albert Murtha, Matthew R. Brown, Russell Greiner
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Locating Brain tumor segmentation within MR (magnetic resonance) images is integral to the treatment of brain cancer. This segmentation task requires classifying each voxel as either tumor or non-tumor, based on a description of that voxel. Unfortunately, standard classifiers, such as Logistic Regression (LR) and Support Vector Machines (SVM), typically have limited accuracy as they treat voxels as independent and identically distributed (iid). Approaches based on random fields, which are able to incorporate spatial constraints, have recently been applied to brain tumor segmentation with notable performance improvement over iid classifiers. However, previous random field systems involved computationally intractable formulations, which are typically solved using some approximation. Here, we present pseudo-conditional random fields (PCRFs), which achieve accuracy similar to other random fields variants, but are significantly more efficient. We formulate a PCRF as a regularized discriminative classifier that relaxes the classification decision for each voxel by considering the labels and features of neighboring voxels.
Lifecycle of Scientific Workflows and their Provenance: A Usage Perspective
Authors: Ilkay Altintas
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

Scientific workflows are representations of generally one, but sometimes more, process(es) in the scientific method. They combine data and computational procedures into a configurable, structured set of steps that implement semi-automated computational solutions of a scientific problem. Each atomic step in a scientific workflow uses a technology to carry out the computation or data processing. Thus, as technology progresses the requirements and motivations for usage of scientific workflows evolve with it. The first part of this invited talk aims to explore this evolution of how scientific workflows are used from late 1990s to date and discusses the advantages gained from this usage. In the second half we delve into current and expected advantages of scientific workflow systems as they mature from art to commodity, with a focus on provenance of scientific workflows and related products.As has been proven by recent workshops, challenges and community interest, capturing provenance information for computational experiments and simulations is a significant advantage of using scientific workflows to conduct computational studies. Many scientific workflow systems today provide provenance recording functionality. However, lack of generic tools to support usage of the collected information limits the usage of the provenance functionality by different users. This talk concludes by presenting a vision for creating a provenance framework to support a series of steps in the lifecycle of provenance information starting from data collection, which could serve multiple data and computational models, to the usages of this data.
Fast Multi-command SSVEP Brain Machine Interface without Training
Authors: Pablo Martinez Vasquez, Hovagim Bakardjian, Montserrat Vallverdu, Andrezj Cichocki
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

We propose a new multi-stage procedure for a real time brain machine/computer interface (BMI) based on the Steady State Visual Evoked Potentials (SSVEP) paradigm elicited by means of flickering checkerboards. The developed system work in asynchronous mode and it does not require training phase and its able to detect fast multiple independent visual commands. Up to 8 independent commands, were tested at the presented work and the proposed BMI system could be extended to more independent commands easily. The system has been extensively experimented with 4 young healthy subjects, confirming the high performance of the proposed procedure and its robustness in respect to artifacts.
Learning Partially Observable Markov Models from First Passage Times
Authors: Jérôme Callut, Pierre Dupont
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

We propose a novel approach to learn the structure of Partially Observable Markov Models (POMMs) and to estimate jointly their parameters. POMMs are graphical models equivalent to Hidden Markov Models (HMMs). The model structure is built to support the First Passage Times (FPT) dynamics observed in the training sample. We argue that the FPT in POMMs are closely related to the model structure. Starting from a standard Markov chain, states are iteratively added to the model. A novel algorithm POMMPHit is proposed to estimate the POMM transition probabilities to fit the sample FPT dynamics. The transitions with the lowest expected passage times are trimmed off from the model. Practical evaluations on artificially generated data and on DNA sequence modeling show the benefits over Bayesian model induction or EM estimation of ergodic models with transition trimming.
A Multi-Sensor Model to Improve Automated Attack Detection
Authors: Magnus Almgren, Ulf Lindqvist, Erland Jonsson
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

Most intrusion detection systems available today are using a single audit source for detection, even though attacks have distinct manifestations in different parts of the system. In this paper we investigate how to use the alerts from several audit sources to improve the accuracy of the intrusion detection system (IDS). Concentrating on web server attacks, we design a theoretical model to automatically reason about alerts from different sensors, thereby also giving security operators a better understanding of possible attacks against their systems. Our model takes sensor status and capability into account, and therefore enables reasoning about the absence of expected alerts. We require an explicit model for each sensor in the system, which allows us to reason about the quality of information from each particular sensor and to resolve apparent contradictions in a set of alerts.Our model, which is built using Bayesian networks, needs some initial parameter values that can be provided by the IDS operator. We apply this model in two different scenarios for web server security. The scenarios show the importance of having a model that dynamically can adapt to local transitional traffic conditions, such as encrypted requests, when using conflicting evidence from sensors to reason about attacks.
Real-Time Task Assignment in Rechargeable Multiprocessor Systems
Authors: Jian Lin, Albert M. K. Cheng
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

This paper introduces the scheduling of frame-based real-time tasks in partitioning schemes for multiprocessor systems powered by rechargeable batteries. In frame-based real-time systems, a set of tasks must execute in a frame, and the whole frame is repeated. This system model is widely used in real-time communication, real-time imaging and a lot of other real-time/embedded systems. Nowadays, many of these systems are powered by rechargeable batteries. Scheduling real-time tasks on these rechargeable systems is an important yet largely ignored issue. The problem for uniprocessor systems had been studied in [1], in which an algorithm of complexity O(N) was proposed for determining the feasibility of the task set. However, it poses a challenge when doing so in a rechargeable multiprocessor system considering different characteristics of the batteries. In this paper, we first show this problem to be NP-Hard, and then propose efficient algorithms to overcome it. The simulation results have shown that our algorithms exhibit very good behaviors and they can be considered as solutions to the problem.
The Use of Photo Retrieval for EEG-Based Personal Identification
Authors: Hideaki Touyama, Michitaka Hirose
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

A research on biometry based on human brain activities has lately become attracted and emerging. In this study, we investigate the feasibility of personal identification based on photo retrieval using three-channel electroencephalogram. Nine photo images were randomly presented one after another to five subjects without training. The Principal Component Analysis and the Linear Discriminant Analysis were applied to perform the simulation of the personal identification. The algorithm correctly identified 82.5, 93.0, and 100.0 % of the subject using EEG activities with 5, 10, and 20-times averaging, respectively. This study reveals a future possibility of photo retrieval tasks to realize the personal identification system using human brain activities, which will yield rich controls of machine for the users of brain-computer interface.
Real-time adaptive robot motion planning
Authors: Jing Xiao
Venue:  (2007)

In order for a robot to perform a task, its motion is usually planned beforehand. This assumes that nothing in the environment changes as the robot performs the task. In the real world, there are uncertainties and changes in a robot's environment that cannot be known a priori. Offline robot motion planning is not enough and will not work in a highly unpredictable and dynamic environment. This dissertation introduces a novel and general paradigm for real-time adaptive motion planning (RAMP) for high-degrees-of-freedom (DOF) robots such as mobile manipulators or other high-DOF robots. The RAMP approach enables simultaneous path and trajectory planning and simultaneous planning and execution of motion in real-time. It effectively deals with drastic changes in the environment through global planning of diverse trajectories and through further preservation of diversity if needed. It facilitates real-time optimization of trajectories under various optimization criteria, such as minimizing energy and time and maximizing manipulability. It also accommodates partially specified task goals of robots easily. The approach exploits loose-coupling of locomotion and manipulation of a mobile manipulator, taking advantage of the redundancy to best achieve obstacle avoidance and various optimization objectives. The RAMP paradigm is suitable not only for planning motion of a single robot in an environment of unknown dynamics, but also for planning the motion of a team of robots in the same environment. It enables distributed, real-time planning of motions for a team of robots to collaborate spontaneously in the same environment for a common task objective. It can also plan motions for two or more robots in tight coordination to manipulate a common object in a dynamically unknown environment.
Parameterized Computational Complexity of Dodgson and Young Elections
Authors: Nadja Betzler, Jiong Guo, Rolf Niedermeier
Venue: Proceedings of the 11th Scandinavian workshop on Algorithm Theory (2008)

We show that, other than for standard complexity theory with known NP-completeness results, the computational complexity of the Dodgson and Young election systems is completely different from a parameterized complexity point of view. That is, on the one hand, we present an efficient fixed-parameter algorithm for determining a Condorcet winner in Dodgson elections by a minimum number of switches in the votes. On the other hand, we prove that the corresponding problem for Young elections, where one has to delete votes instead of performing switches, is W[2]-complete. In addition, we study Dodgson elections that allow ties between the candidates and give fixed-parameter tractability as well as W[2]-hardness results depending on the cost model for switching ties.
Designing financial market intelligent monitoring system based on OWA
Authors: Benjamin Fonooni, Seied Javad Mousavi Moghadam
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

The need for intelligent monitoring systems for financial markets especially Foreign Exchange has become a necessity to keep track of this market. Financial markets conform to some mathematical concepts and cause it to be analyzed with different Artificial Intelligence (AI) algorithms. Data Fusion has been applied in different fields and the corresponding applications utilize numerous mathematical tools. This paper headed for applying Ordered Weighted Averaging (OWA) operator in order to support trading decisions based on technical analysis in Foreign Exchange Market.
Distributed Activity Recognition with Fuzzy-Enabled Wireless Sensor Networks
Authors: Mihai Marin-Perianu, Clemens Lombriser, Oliver Amft, Paul Havinga, Gerhard Tröster
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

Wireless sensor nodes can act as distributed detectors for recognizing activities online, with the final goal of assisting the users in their working environment. We propose an activity recognition architecture based on fuzzy logic, through which multiple nodes collaborate to produce a reliable recognition result from unreliable sensor data. As an extension to the regular fuzzy inference, we incorporate temporal order knowledge of the sequences of operations involved in the activities. The performance evaluation is based on experimental data from a car assembly trial. The system achieves an overall recognition performance of 0.81 recall and 0.79 precision with regular fuzzy inference, and 0.85 recall and 0.85 precision when considering temporal order knowledge. We also present early experiences with implementing the recognition system on sensor nodes. The results show that the algorithms can run online, with execution times in the order of 40ms, for the whole recognition chain, and memory overhead in the order of 1.5kB RAM.
Ubiquitous and Robust Text-Independent Speaker Recognition for Home Automation Digital Life
Authors: Jhing-Fa Wang, Ta-Wen Kuan, Jia-Chang Wang, Gaung-Hui Gu
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

This paper presents a ubiquitous and robust text-independent speaker recognitionarchitecture for home automation digital life. In this architecture, a multiple microphone configuration is adopted to receive the pervasive speech signals. The multi-channel speech signals are then added together with a mixer. In a ubiquitous computing environment, the received speech signal is usually heavily corrupted by background noises. An SNR-aware subspace speech enhancement approach is used as a pre-processing to enhance the mixed signal. Considering the text-independent speaker recognition, this paper applies a multi-class support vectors machine (SVM)[10][11] instead of conventional Gaussian mixture models (GMMs)[12]. In our experiments, the speaker recognition rate can averagely reach 97.2% with the proposed ubiquitous speaker recognitionarchitecture.
A Grey-Rough Set Approach for Interval Data Reduction of Attributes
Authors: Daisuke Yamaguchi, Guo-Dong Li, Masatake Nagai
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

Reduction in rough set theory is useful to compact given attributes of large-scale decision tables in data mining. In this paper a new method called grey-rough reduction is proposed for decision tables containing non-interval data and interval data complexly called grey-decision tables. First of all, a grey-rough approximation is introduced after summarized grey numbers, their operations and functions. Two sorts of reduction based on grey-rough sets, a basic approach and advanced approach are proposed with several illustrative examples. Three experiments, compatibility with the classical model, an application of the basic approach to decision-making and influence of the parameter in the advanced approach are shown. The advantages of the proposal are (1) it is compatible with the classical reduction model for non-interval data, (2) it is useful for complex decision tables and (3) it provides a possible reduction of attributes with a parameter by the advanced approach.
Flexible Resolution of Authorisation Conflicts in Distributed Systems
Authors: Changyu Dong, Giovanni Russello, Naranker Dulay
Venue: Proceedings of the 19th IFIP/IEEE international workshop on Distributed Systems: Operations and Management: Managing Large-Scale Service Deployment (2008)

Managing security in distributed systems requires flexible and expressive authorisation models with support for conflict resolution. Models need to be hierarchical but also non-monotonic supporting both positive and negative authorisations. In this paper, we present an approach to resolve the authorisation conflicts that inevitably occur in such models, with administrator specified conflict resolution strategies (rules). Strategies can be global or applied to specific parts of a system and dynamically loaded for different applications. We use Courteous Logic Programs (CLP) for the specification and enforcement of strategies. Authorisation policies are translated into labelled rules in CLP and prioritised. The prioritisation is regulated by simple override rules specified or selected by administrators. We demonstrate the capabilities of the approach by expressing the conflict resolution strategy for a moderately complex authorisation model that organises subjects and objects hierarchically.
Bio-inspired Event Coded Configurable Analog Circuit Block
Authors: Thomas Jacob Koickal, Luiz Carlos Gouveia, Alister Hamilton
Venue: Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware (2008)

An event coded configurable analog circuit block that forms the building block of a programmable analog array is presented. The design of the event block is inspired from the behavior of biological neurons that process signals in analog domain and transmit them as spike events. In the configurable event block implemented here, no events are transmitted when the signals are relatively constant thereby leading to lower energy dissipation and better utilization of resources in analog arrays. The block diagram and the circuit schematics of the event coded configurable analog block are described. Application examples are presented to demonstrate the reconfigurability and functionality of the event coded circuit block.
Dot Counting Dimension, a New Parameter of Fractal Dimension or Complexity
Authors: Zhi-min Li
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

A new tool, Dot Counting Dimension (DCD) for measuring the fractal dimension or complexity of 1-dimension random vibration signal or time series data is put forward in this paper. The definition and algorithm of DCD are given out. DCD of six pulse segments of microseismic records are calculated, and linear result of DCD is achieved. The algorithm of DCD may be applied broadly in processing 1-dimension random vibration signal or time series data because of its implementing easy with less calculation, and fast calculation velocity, And the algorithm of DCD does not need too much more data for achieving convergence.
Strategic advantages of interoperability for global manufacturing using CNC technology
Authors: S. T. Newman, A. Nassehi, X. W. Xu, R. S. U. Rosso,  Jr., L. Wang, Y. Yusof, L. Ali, R. Liu, L. Y. Zheng, S. Kumar, P. Vichare, V. Dhokia
Venue: Robotics and Computer-Integrated Manufacturing (2008)

In the domain of manufacturing, computer numerically controllers (CNC) technology is a major contributor to the production capacity of the enterprises. The advances in CNC technology coupled with enhancements in computing systems have provided the basis to re-examine the way in which computer-aided systems (CAx) can be used to enable global manufacturing. Interoperability of the various components of the CAx chain is therefore a major prerequisite for manufacturing enterprises for becoming strategically agile and consequently globally competitive. Being interoperable, resources can be utilized interchangeably in a plug-and-produce manner. Over the last 8 years the eminence of a STEP standard for machining entitled STEP-NC (numerical control) has become a well-known vehicle for research to improve the level of information availability at the CNC machine tool. In this paper, the authors introduce the background to the evolution of CNC manufacturing over the last 50 years and the current standards available for programming. A review of the literature in interoperable CNC manufacturing is then provided relating to milling, turn-mill and other NC processes. The major part of the paper provides a strategic view of how interoperability can be implemented across the CAx chain with a range of standards used to regulate the flow of information. Finally, the paper outlines the advantages and major issues for future developments in interoperability, identifying future key requirements and limiting factors.
Brief paper: Robust adaptive H∞ control using integral quadratic constraints
Authors: Ian R. Petersen
Venue: Automatica (Journal of IFAC) (2008)

This paper introduces a new approach to robust adaptive H^~ control. The main idea of the paper is to extend an existing approach to robust H^~ control to allow for constant or slowly varying unknown parameters in an adaptive way. This is achieved by treating the presence of the unknown parameters as states resulting in a bilinear nonlinearity. A copy of this nonlinearity is repeated in the controller and the presence of the repeated nonlinearity is exploited using a special integral quadratic constraint.
Generating functions of multi-symplectic RK methods via DW Hamilton&#x2013;Jacobi equations
Authors: Jialin Hong, Yajuan Sun
Venue: Numerische Mathematik (2008)

The De Donder&#x2013;Weyl (DW) Hamilton&#x2013;Jacobi equation is investigated in this paper, and the connection between the DW Hamilton&#x2013;Jacobi equation and multi-symplectic Hamiltonian system is established. Based on the DW Hamilton&#x2013;Jacobi theory, generating functions for multi-symplectic Runge&#x2013;Kutta (RK) methods and partitioned Runge&#x2013;Kutta (PRK) methods are presented.
A Data Quality and Data Confidentiality Assessment of Complementary Cell Suppression
Authors: Lawrence H. Cox
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

Complementary cell suppression has been used for disclosure limitation of magnitude data such as economic censuses data for decades. This paper examines data quality and data confidentiality characteristics of cell suppression. We demonstrate that when cell suppression is not performed using a proper mathematical model, it can fail to protect. Moreover, we demonstrate that properly executed suppression based on standard disclosure definitions can be vulnerable to other attacks, sometimes fatally.