ppxscal/embedding_network huggingface dataset:
Hybrid ANN reducing training time requirements and decision delay for equalization in presence of co-channel interference
Authors: Siba Prasada Panigrahi, Santanu Kumar Nayak, Sasmita Kumari Padhy
Venue: Applied Soft Computing (2008)

Bayesian equalizer is known to be the optimum equalizer. This paper proposes a Hybrid Artificial Neural Network (Hybrid ANN) and an algorithm to modify Decision Feedback Equalizer (DFE) function of Bayesian equalizer while equalizing in presence of co-channel interference (CCI). A combination of Artificial Neural Network and Decision Feedback Equalizer (DFE) is termed as Neural-DFE (NDFE). The results show that the decision delay and training time requirement reduces significantly by use of NDFE. This creates an advantage specifically for a mobile environment where the CCI is varying in nature and the Bayesian equalizer requires a lot of training time.
Augmented reality for games
Authors: Filipe Costa Luz, Vasco Bila, José Maria Dinis
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

In this text, we intend to explore augmented reality as a means to visualise interactive communication projects. With ARToolkit, Virtools and 3ds Max applications, we aim to show how to create a portable interactive platform that resorts to the environment and markers for constructing the game's scenario. We plan to show that the realism of simulation, together with the merger of artificial objects with the real world, can generate interactive empathy between players and their avatars.
Computational methods in protein structure comparison and analysis of protein interaction networks
Authors: Dianne P. O'Leary, Teresa M. Przytycka
Venue:  (2007)

Proteins are versatile biological macromolecules that perform numerous functions in a living organism. For example, proteins catalyze chemical reactions, store and transport various small molecules, and are involved in transmitting nerve signals. As the number of completely sequenced genomes grows, we are faced with the important but daunting task of assigning function to proteins encoded by newly sequenced genomes. In this thesis we contribute to this effort by developing computational methods for which one use is to facilitate protein function assignment. Functional annotation of a newly discovered protein can often be transferred from that of evolutionarily related proteins of known function. However, distantly related proteins can still only be detected by the most accurate protein structure alignment methods. As these methods are computationally expensive, they are combined with less accurate but fast methods to allow large-scale comparative studies. In this thesis we propose a general framework to define a family of protein structure comparison methods that reduce protein structure comparison to distance computation between high-dimensional vectors and therefore are extremely fast. Interactions among proteins can be detected through the use of several mature experimental techniques. These interactions are routinely represented by a graph, called a protein interaction network, with nodes representing the proteins and edges representing the interactions between the proteins. In this thesis we present two computational studies that explore the connection between the topology of protein interaction networks and protein biological function. Unfortunately, protein interaction networks do not explicitly capture an important aspect of protein interactions, their dynamic nature. In this thesis, we present an automatic method that relies on graph theoretic tools for chordal and cograph graph families to extract dynamic properties of protein interactions from the network topology. An intriguing question in the analysis of biological networks is whether biological characteristics of a protein, such as essentiality, can be explained by its placement in the network. In this thesis we analyze protein interaction networks for Saccharomyces cerevisiae to identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality.
Learning from Positive and Unlabeled Examples: A Survey
Authors: Bangzuo Zhang, Wanli Zuo
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

This paper surveys the existing method of learning from positive and unlabeled examples. We divide the existing methods into three families, and review the main algorithms, respectively. The first Family of methods takes a two-step strategy, extracting some reliable negative examples, and then applying the supervised or semi-supervised learning method. The second family of methods estimates statistical queries over positive and unlabeled examples. The third family of methods reduces this problem to the problem of learning with high one-sided noise by treating the unlabeled set as noisy negative examples. Finally, we conclude and issue future works.
Modular System Verification by Inference, Testing and Reachability Analysis
Authors: Roland Groz, Keqin Li, Alexandre Petrenko, Muzammil Shahbaz
Venue: Proceedings of the 20th IFIP TC 6/WG 6.1 international conference on Testing of Software and Communicating Systems: 8th International Workshop (2008)

Verification of a modular system composed of communicating components is a difficult problem, especially when the models of the components are not available. Conventional testing techniques are not efficient in detecting erroneous interactions of components because such interactions often occur as interleavings of events that are difficult to reproduce in a modular system. The problem of detecting intermittent errors in the absence of models of components is addressed in this paper. A method to infer a controllable approximation of components through testing is elaborated. The inferred finite state models of components are used to detect intermittent errors and other compositional problems in the system through reachability analysis. The models are refined at each analysis step thus making the approach iterative.
Discovering Relations Among Entities from XML Documents
Authors: Yangyang Wu, Qing Lei, Wei Luo, Harou Yokota
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

This paper addresses relation information extraction problem and proposes a method of discovering relations among entities which is buried in different nest structures of XML documents. The method first identifies and collects XML fragments that contain all types of entities given by users, then computes similarity between fragments based on semantics of their tags and their structures, and clusters fragments by similarity so that the fragments containing the same relation are clustered together, finally extracts relation instances and patterns of their occurrences from each cluster. The results of experiments show that the method can identify and extract relation information among given types of entities correctly from all kinds of XML documents with meaningful tags.
Volta: Developing Distributed Applications by Recompiling
Authors: Dragos Manolescu, Brian Beckman, Benjamin Livshits
Venue: IEEE Software (2008)

Mainstream languages and tools are tailored for sequential, nondistributed applications, with support for distributed computing provided only in library APIs. Such programming environments force developers to make decisions early in the application life cycle about where the code should run, structuring the entire application around partitioning decisions. Performance measurement might reveal that the original partitioning was wrong, but redistributing the application is expensive because redistributing is restructuring. This article describes a new kind of tool suite that recompiles executables into a distributed form on the basis of declarative user annotations, inserting most of the necessary remoting and synchronization boilerplate code and facilitating post hoc instrumentation to drive quantitative redistribution. Because the tools operate on the intermediate language CIL (.NET Common Intermediate Language), they're compatible with a wide variety of .NET programming languages and eventual execution environments, even those that don't support CIL directly, such as JavaScript.
Modeling of the Students Scenario on a Learning Course
Authors: Yuji Shinoda, Kenji Yoshida, Hirotaka Nakayama
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Adjusting the content to each student is a major issue in e-Learning. From this viewpoint, a learning course as a series of content also must be adjusted according to the performance of the students. We propose a method that combines clustering and decision tree learning for constructing scenarios of the students' actions. The global statuses of the students are reflected to the clusters, and the local and sequential actions of the students are reflected to the decision trees. The results of e-Learning tests gathered from Japanese junior high school students was processed by our proposed method. We graded the clusters by adaptation to the trees, and selected a set of clusters as a scenario for the students. These scenarios have a possibility of aiding the adjustment, and revision of learning courses.
Generating context-related feedback for teachers
Authors: Jelena Jovanovic
Venue: International Journal of Technology Enhanced Learning (2008)

The paper presents LOCO-Analyst, an educational tool for providing teachers with fine-grained and context-rich feedback. Such an advanced feedback enables teachers to rethink the content and structure of their courses and helps them customise the courses to the students' needs. The feedback provision is based on analysis of learning context data captured and integrated from online learning systems and tools. Here, we illustrate some of the functionalities of LOCO-Analyst (using real world data) and present the semantic technologies that make LOCO-Analyst a generic feedback provisioning tool. We also sketch our current and planned future efforts for further improving LOCO-Analyst.
Coregistration of serial breast mr studies for augmenting neoadjuvant chemotherapy response evaluation in breast cancer patients
Authors: Orhan Nalcioglu, Min-Ying Su
Venue:  (2008)

Neoadjuvant chemotherapy (NAC) is gaining popularity as an important part of breast conservation surgery in breast cancer patients and MRI is the primary modality of choice for predicting residual disease after therapy. By comparing residual tumor sizes in the follow-up (F/U) MR scans with the pre-treatment tumor size from baseline (B/L) scan, weighted decisions can be made regarding appropriate changes to the chemo regimen or effectiveness of the treatment for the particular patient. But due to the deformable nature of the breast, its shape in breast MR acquisitions of the same patient in different studies (taken 2-4 weeks from baseline study) is significantly different. Thus, the exact correspondence between the B/L and F/U scans is lost. In the presence of multi-centric or multi-focal breast cancer, an objective evaluation of tumor regression may be challenging. Also, inflammation due to treatment, i.e. mastitis, is a common occurrence and may be misdiagnosed as residual disease. Transporting the clinical results of neoadjuvant chemotherapy to the operating table is also arduous. Clinical evaluation using MR is done when the patient is in the prone position, while in the operating room, the patient is lying on her back, in the supine position. The obvious difference in both coordinate frames combined with the elastic nature of the breast makes the transition a challenging problem. In this dissertation, we proposed a registration framework to augment quantitative assessment of patient response to NAC. By estimating the motion between consecutive temporal volumes of the patient, the precise location of the B/L tumor in the F/U scan can be determined. For this, we use image-based volumetric approaches that address boundary mapping as well as internal mapping. An important criteria involving tumor volume preservation was also incorporated to avoid tumor scaling due to non-rigid transformations which may convey a false sense of positive or negative response to chemotherapy. Another framework for registration of prone and supine datasets is also outlined. The registration framework has been successfully tested against well-calibrated phantoms and synthetic images and diverse patient data reflecting typical clinical scenarios.
All-Pairs Shortest Paths with a Sublinear Additive Error
Authors: Liam Roditty, Asaf Shapira
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We show that for every 0 &le; p &le; 1 there is an algorithm with running time of O(n 2.575 -p/(7.4 -2.3p)) that given a directed graph with small positive integer weights, estimates the length of the shortest path between every pair of vertices u,v in the graph to within an additive error &delta; p (u,v), where &delta;(u,v) is the exact length of the shortest path between u and v. This algorithm runs faster than the fastest algorithm for computing exact shortest paths for any 0 < p &le; 1. Previously the only way to "bit" the running time of the exact shortest path algorithms was by applying an algorithm of Zwick [FOCS '98] that approximates the shortest path distances within a multiplicative error of (1 + &epsilon;). Our algorithm thus gives a smooth qualitative and quantitative transition between the fastest exact shortest paths algorithm, and the fastest approximation algorithm with a linear additive error. In fact, the main ingredient we need in order to obtain the above result, which is also interesting in its own right, is an algorithm for computing (1 + &epsilon;) multiplicative approximations for the shortest paths, whose running time is faster than the running time of Zwick's approximation algorithm when &epsilon; << 1 and the graph has small integer weights.
Petri Nets, Discrete Physics, and Distributed Quantum Computation
Authors: Samson Abramsky
Venue: Concurrency, Graphs and Models: Essays Dedicated to Ugo Montanari on the Occasion of His 65th Birthday (2008)

We shall describe connections between Petri nets, quantum physics and category theory. The view of Net theory as a kind of discrete physics has been consistently emphasized by Carl-Adam Petri. The connections between Petri nets and monoidal categories were illuminated in pioneering work by Ugo Montanari and José Meseguer. Recent work by the author and Bob Coecke has shown how monoidal categories with certain additional structure (dagger compactness) can be used as the setting for an effective axiomatization of quantum mechanics, with striking applications to quantum information. This additional structure matches the extension of the Montanari-Meseguer approach by Marti-Oliet and Meseguer, motivated by linear logic.
Extracting Decision Rules from Sigmoid Kernel
Authors: Quanzhong Liu, Yang Zhang, Zhengguo Hu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Sigmoid kernel is widely applied in neural networks for classification tasks. SVM classifier, which is applied with sigmoid kernel, has excellent classification accuracy. However, as sigmoid kernel has complicated structure, it is generally difficult for human expert to interpret and understand how the sigmoid kernel makes its classification decision. As decision rule classifier is understandable to human expert, in this paper, we present our InterSIG algorithm, which mines decision rules from the classification hyper-plane which is constructed by SVM with sigmoid kernel. InterSIG expands sigmoid kernel into its Maclaurin series, and then mines classification rules which make great contribution to classification from the classification hyper-plane. Experiment results show that InterSIG classifier is more understandable to human experts without jeopardizing the accuracy than the original SVM with sigmoid kernel. Furthermore, compared with 3 association classifiers, CMAR, CBA, CPAR and C4.5, a decision tree classifier, InterSIG classifier is very encouraging over the 9 datasets.
A framework for building haptic interactions for teleoperation systems
Authors: Huynh-Phong Pham, Mehdi Ammi, Jean-Guy Fontaine, Patrick Bourdot
Venue: Proceedings of the 2008 Ambi-Sys workshop on Haptic user interfaces in ambient media systems (2008)

From the nano/micro-manipulation domain to the intervention in the nuclear field, haptics has become essential in actual teleoperation systems. The active property of this modality makes gestures more reliable and accurate. Nowadays, there is much research concerning the integration of haptic modality. The proposed solutions give, according to the adopted approaches, various efficiency levels. However, this work doesn't integrate generally and systematically psychophysics studies and ergonomics considerations. These elements are very important if we want to include effectively the human operator in any teleoperation system. Otherwise, various new applications present to the human operator several unfamiliar phenomena (e.g., nano-environments, underwater environments and the outer space environments). It's thus necessary in this type of application to transform virtually the remote environment to present to the operator nature-like haptic interactions. In this paper, we present a framework for building haptic interactions for teleoperation systems. This framework integrates the psychophysics studies and ergonomics elements, and presents a method to project the remote environment in the intuitive perception space.
Parameterized Complexity of Candidate Control in Elections and Related Digraph Problems
Authors: Nadja Betzler, Johannes Uhlmann
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

There are different ways for an external agent to influence the outcome of an election. We concentrate on "control" by adding or deleting candidates of an election. Our main focus is to investigate the parameterized complexity of various control problems for different voting systems. To this end, we introduce natural digraph problems that may be of independent interest. They help in determining the parameterized complexity of control for different voting systems including Llull, Copeland, and plurality votings. Devising several parameterized reductions, we provide a parameterized complexity overview of the digraph and control problems with respect to natural parameters.
Designing an assistive device for the blind based on object localization and augmented auditory reality
Authors: Florian Dramas, Bernard Oriola, Brian G. Katz, Simon J. Thorpe, Christophe Jouffrais
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

There are many electronic devices for the visually impaired but few actually get used on a daily basis. This is due in part to the fact that many devices often fail to address the real needs of the users. In this study, we begin with a review of the existing literature followed by a survey of 54 blind people which suggests that one particular function could be particularly useful in a new device, namely, the ability to localize objects. We have tested the possibility of using a sound rendering system to indicate a particular spatial location, and propose to couple this with a biologically inspired image processing system that can locate visual patterns that correspond to particular objects and places. We believe that such a system can address one of the major problems faced by the visually handicapped, namely their difficulty in localizing specific objects.
A GP Based Approach to the Classification of Multiclass Microarray Datasets
Authors: Chun-Gui Xu, Kun-Hong Liu
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper, we propose a genetic programming (GP) based approach to analyze multiclass microarray datasets. Here, a multiclass problem is divided into a set of two-class problems. Instead of applying a tree for each two-class problem, a small-scale ensemble system containing a set of trees is deployed and denoted by sub-ensemble (SE). The SEs tackling the respective two-class problems are combined to construct an individual of the GP, so that an individual can deal with a multiclass problem directly. In the experiments, the GP implements classification and feature selection at the same time. The results obtained at independent test sets show that our method is efficient in the search of genes with great biological significance, and achieves high classification accuracy at the same time.
Process Model Abstraction: A Slider Approach
Authors: Artem Polyvyanyy, Sergey Smirnov, Mathias Weske
Venue: Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference (2008)

Process models provide companies efficient means for managing their business processes. Tasks where process models are employed are different by nature and require models of various abstraction levels. However, maintaining several models of one business process involves a lot of synchronization effort and is erroneous. Business process model abstraction assumes a detailed model of a process to be available and derives coarse grained models from it. The task of abstraction is to tell significant model elements from insignificant ones and to reduce the latter. In this paper we argue that process model abstraction can be driven by different abstraction criteria. Criterion choice depends on a task which abstraction facilitates. We propose an abstraction slider—a mechanism that allows user control of the model abstraction level. We discuss examples of combining the slider with different abstraction criteria and sets of process model transformation rules.
A Model of Re-use of E-Learning Content
Authors: Paul Libbrecht
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Re-using e-learning content is a solution often proposed against the high-cost of the creation high-quality e-learning material, a solution with a neglectable price. In this paper we propose a content distribution model that addresses the long term evolution of content pieces whose quality raises along with re-uses. A realization of this model is presented for the ActiveMath learning environment platform.
Interactive Separation of Segmented Bones in CT Volumes Using Graph Cut
Authors: Lu Liu, David Raber, David Nopachai, Paul Commean, David Sinacore, Fred Prior, Robert Pless, Tao Ju
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

We present a fast, interactive method for separating bones that have been collectively segmented from a CT volume. Given user-provided seed points, the method computes the separation as a multi-way cut on a weighted graph constructed from the binary, segmented volume. By properly designing and weighting the graph, we show that the resulting cut can accurately be placed at bone-interfaces using only a small number of seed points even when the data is noisy. The method has been implemented with an interactive graphical interface, and used to separate the 12 human foot bones in 10 CT volumes. The interactive tool produced compatible result with a ground-truth separation, generated by a completely manual labelling procedure, while reducing the human interaction time from a mean of 2.4 hours per volume in manual labelling down to approximately 18 minutes.
Functional completeness of bounded structures of fuzzy logic with wvt-operators
Authors: Radomír Halaš
Venue: Fuzzy Sets and Systems (2008)

A simple criterion for a functional completeness of partially ordered commutative integral monoids with a ''weak very true'' operator is presented.