ppxscal/embedding_network huggingface dataset:
Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset
Authors: Zhiheng Huang, Masoud Nikravesh, Ben Azvine, Tamás D. Gedeon
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined.
The Orbit Problem Is in the GapL Hierarchy
Authors: V. Arvind, T. C. Vijayaraghavan
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

The Orbit problem is defined as follows: Given a matrix A ¿¿ n×n and vectors x,y ¿ ¿ n , does there exist a non-negative integer i such that A i x = y. This problem was shown to be in deterministic polynomial time by Kannan and Lipton in [7]. In this paper we put the problem in the logspace counting hierarchy GapLH. We also show that the problem is hard for C=L.
To Share or Not to Share: Supporting the User Decision in Mobile Social Software Applications
Authors: Giuseppe Lugano, Pertti Saariluoma
Venue: Proceedings of the 11th international conference on User Modeling (2007)

User's privacy concerns represent one of the most serious obstacles to the wide adoption of mobile social software applications. In this paper, we introduce a conceptual model which tackles the problem from the perspective of trade-off between privacy and trust, where the user takes the decision with minimal privacy loss. To support the user decision, we introduce the Mobile Access Control List (Macl), a privacy management mechanism which takes into account the user attitude towards mobile sharing, his communication history and social network relationships.
ShapeShifting Documentary: A Golden Age
Authors: Vilmos Zsombori, Marian F. Ursu, John Wyver, Ian Kegel, Doug Williams
Venue: Proceedings of the 6th European conference on Changing Television Environments (2008)

ShapeShifting TV denotes an approach to interactive television programmes that can adapt during delivery to the preferences of the active viewers. ShapeShifting TV is based on a declarative language called Narrative Structure Language (NSL) and is accompanied by a set of genre and production independent authoring and delivery tools. ShapeShifting TV was validated via a number of productions. A Golden Age --- a configurable interactive documentary --- is one of them. This paper presents the production of A Golden Age within the ShapeShifting paradigm.
Financial Prediction with Neuro-fuzzy Systems
Authors: Agata Pokropińska, Rafał Scherer
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

An application of neuro-fuzzy systems to supporting trading decisions is presented. The system has the ability to use expert knowledge and to be fitted to the learning data by various machine learning techniques. The proposed approach uses the backpropagation algorithm to determine system parameters on the basis of several indices. Experiments were made on past data showing relatively good performance of the proposed approach.
Ugo Montanari in a Nutshell
Authors: Rocco Nicola, Pierpaolo Degano, José Meseguer
Venue: Concurrency, Graphs and Models: Essays Dedicated to Ugo Montanari on the Occasion of His 65th Birthday (2008)

Ugo was born in Besana Brianza in 1943 where his parents had moved to escape from the Milan bombings during the Second World War. Immediately after the war he went back to Milan were he completed all his studies. Ugo got his Laurea degree in Electronic Engineering from the Politecnico di Milano in 1966, three years before the first Laurea curriculum and seventeen years before the first PhD curriculum in Computer Science started in Pisa.
A Model-Based Approach to Calculating and Calibrating the Odometry for Quadruped Robots
Authors: Haitao He, Xiaoping Chen
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

This paper presents a model-based odometry calculation and calibration method (MBO) for quadruped robots. Instead of establishing the direct relation between target and actual speeds as previous methods did, MBO sets up a "parametric physical model" incorporating various properties of the robot and environment such as friction and inertia, through optimization with locomotor data. Based on this optimized model, one can compute the loci of robot legs' movement by forward kinematics and finally obtain odometric readings by analyzing the loci. Experiments on Sony AIBO ERS-7 robots demonstrate that the odometry error of MBO is generally 50% less than the existing methods. In addition, the calibration complexity is low.
Acoustic Modeling Using Continuous Density Hidden Markov Models in the Mercer Kernel Feature Space
Authors: R. Anitha, C. Chandra Sekhar
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, we propose an approach for acoustic modeling using Hidden Markov Models (HMMs) in the Mercer kernel feature space. Acoustic modeling of subword units of speech involves classification of varying length sequences of speech parametric vectors belonging to confusable classes. Nonlinear transformation of the space of parametric vectors into a higher dimensional space using Mercer kernels is expected to lead to better separability of confusable classes. We study the performance of continuous density HMMs trained using the varying length sequences of feature vectors obtained from the kernel based transformation of parametric vectors. Effectiveness of the proposed approach to acoustic modeling is demonstrated for recognition of spoken letters in E-set of English alphabet, and for recognition of consonant-vowel type subword units in continuous speech of three Indian languages.
Pullback Grammars Are Context-Free
Authors: Michel Bauderon, Rui Chen, Olivier Ly
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

Following earlier work on pullback rewriting, we describe here the notion of graph grammar relevant to our formalism. We then show that pullback grammars are context-free and provide a surprising example, namely the context-free generation of square grids.
A Linear Delay Algorithm for Building Concept Lattices
Authors: Martin Farach-Colton, Yang Huang
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

Concept lattices (also called Galois lattices) have been applied in numerous areas, and several algorithms have been proposed to construct them. Generally, the input for lattice construction algorithms is a binary matrix with size |G||M| representing binary relation I ¿ G ×M . In this paper, we consider polynomial delay algorithms for building concept lattices. Although the concept lattice may be of exponential size, there exist polynomial delay algorithms for building them. The current best delay-time complexity is O(|G||M|2). In this paper, we introduce the notion of irregular concepts, the combinatorial structure of which allows us to develop a linear delay lattice construction algorithm, that is, we give an algorithm with delay time of O(|G||M|). Our algorithm avoids the union operation for the attribute set and does not require checking if new concepts are already generated. In addition, we propose a compact representation for concept lattices and a corresponding construction algorithm. Although we are not guaranteed to achieve optimal compression, the compact representation can save significant storage space compared to the full representation normally used for concept lattices.
Invariant Post-tabular Protection of Census Frequency Counts
Authors: Natalie Shlomo, Caroline Young
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

Some countries use forms of random rounding as a post-tabular method to protect Census frequency counts disseminated in tables. These methods typically result in inconsistencies between aggregated internal cells to marginal totals and across same cells in different tables. A post-tabular method for perturbing frequency counts is proposed which preserves totals and corrects to a large extent inconsistencies. The perturbation is based on invariant probability transition matrices and the use of microdata keys. This method will be compared to common pre and post-tabular methods for protecting Census frequency counts.
Error Detection and Correction Based on Chinese Phonemic Alphabet in Chinese Text
Authors: Chuen-Min Huang, Mei-Chen Wu, Ching-Che Chang
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

Misspelling and misconception resulting from similar pronunciation appears frequently in Chinese texts. Without double check-up, this situation is getting even worse with the help of Chinese input method editor. It is hoped that the quality of Chinese writing would be enhanced if an effective automatic error detection and correction mechanism embedded in text editor. Therefore, the burden of manpower to proofread shall be released. Until recently, researches on automatic error detection and correction of Chinese text have undergone many challenges and suffered from bad performance compared with that of Western text editor. In view of the prominent phenomenon in Chinese writing problem, this study proposes a learning model based on Chinese phonemic alphabet. The experimental results demonstrate this model is effective in finding out most of words spelled incorrectly, and furthermore this model improves detection and correction rate.
Comparing Grid Computing Solutions for Reverse-Engineering Gene Regulatory Networks
Authors: Martin Swain, Johannes J. Mandel, Werner Dubitzky
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

Models of gene regulatory networks encapsulate important features of cell behaviour, and understanding gene regulatory networks is important for a wide range of biomedical applications. Network models may be constructed using reverse-engineering techniques based on evolutionary algorithms. This optimisation process can be very computationally intensive, however its computational requirements can be met using grid computing techniques. In this paper we compare two grid infrastructures. First we implement our reverse-engineering software on an opportunistic grid computing platform. We discuss the advantages and disadvantages of this approach, and then go on to describe an improved implementation using the QosCosGrid, a quasi-opportunistic supercomputing framework (Qos) for complex systems applications (Cos). The QosCosGrid is able to provide advanced support for parallelised applications, across different administrative domains and this allows more sophisticated reverse-engineering approaches to be explored.
Prediction of Protein-Protein Binding Hot Spots: A Combination of Classifiers Approach
Authors: Roberto Hiroshi Higa, Clésio Luis Tozzi
Venue: Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology (2008)

In this work we approach the problem of predicting protein binding hot spot residues through a combination of classifiers. We consider a comprehensive set of structural and chemical properties reported in the literature for characterizing hot spot residues. Each component classifier considers a specific set of properties as feature set and their output are combined by the mean rule. The proposed combination of classifiers achieved a performance of 56.6%, measured by the F-Measure with corresponding Recall of 72.2% and Precision of 46.6%. This performance is higher than those reported by Darnel et al. [4] for the same data set, when compared through a t-test with a significance level of 5%.
Automating the Analysis of Honeypot Data (Extended Abstract)
Authors: Olivier Thonnard, Jouni Viinikka, Corrado Leita, Marc Dacier
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

We describe the on-going work towards further automating the analysis of data generated by a large honeynet architecture called Leurre.com and SGNET. The underlying motivation is helping us to integrate the use of honeypot data into daily network security monitoring. We propose a system based on two automated steps: i) the detection of relevant attack events within a large honeynet traffic data set, and ii) the extraction of highly similar events based on temporal correlation.
Chaotic Communications in MIMO Systems
Authors: Karuna Thapaliya, Qinghai Yang, Kyung Sup Kwak
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

In wireless communications, chaotic communications have been a field of interest due to its low complexity in hardware implementation and low power consumption in chaotic signal generation. Among the modulation schemes using the chaotic signal, Differential Chaos Shift Keying (DCSK) is a robust noncoherent technique. Multiple-input multiple-output (MIMO) system is a technology that uses multiple transmit and/or multiple receive antennas in order to improve the system performance in wireless systems. In our paper, we have proposed a new scheme of MIMO-DCSK which utilizes the benefits of MIMO system into the chaotic communication system by transmitting and receiving DCSK modulated signals through multiple antennas. Our analysis and simulation results show how the chaotic communications in the new MIMO-DCSK benefits over the single input single output (SISO) system and the BER performance of DCSK in additive white Gaussian noise (AWGN) channels using Alamouti space-time code and the maximum likelihood decoding is analyzed.
Coronary Motion Modelling for Augmented Reality Guidance of Endoscopic Coronary Artery Bypass
Authors: Michael Figl, Daniel Rueckert, David Hawkes, Roberto Casula, Mingxing Hu, Ose Pedro, Dong Ping Zhang, Graeme Penney, Fernando Bello, Philip Edwards
Venue: Proceedings of the 4th international symposium on Biomedical Simulation (2008)

The overall aim of our project is to guide totally endoscopic coronary artery bypass. This requires construction of a 4D preoperative model of the coronary arteries and myocardium. The model must be aligned with the endoscopic view of the patient's beating heart and presented to the surgeon using augmented reality. We propose that the model can be constructed from coronary CT. Segmentation can be performed for one phase of the cardiac cycle only and propagated to the others using non-rigid registration. We have compared the location of the coronaries produced by this method to hand segmentation.Registration of the model to the endoscopic view of the patient is achieved in two phases. Temporal registration is performed by identification of corresponding motion between model and video. Then we calculate photo-consistency between the two da Vinci endoscope views and average over the frames of the motion model. This has been shown to improve the shape of the cost function. Phantom results are presented.The model can then be transformed to the calibrated endoscope view and overlaid using two video mixers.
Combined Effect of Topology and Synchronism Perturbation on Cellular Automata: Preliminary Results
Authors: Jean-Baptiste Rouquier, Michel Morvan
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

The aim of this paper is to experimentally study the combined effect of the introduction of two kinds of structural perturbations to the behavior of cellular automata. We present the results obtained by simultaneously perturbing synchronism and topology of elementary cellular automata. We show that very interesting and different behaviors appear, including phase transitions and non monotonicity (i.e. introduction of both perturbations is less effective than the introduction of only one of them). These results lead us to think that this study is worth to be now developed more accurately.
Partially Blind Threshold Signature Based on RSA
Authors: Hung-Yu Chien, Jinn-Ke Jan, Yuh-Min Tseng
Venue: Informatica (2003)

A partially blind signature scheme allows the signer to inoculate a non-removable common information into his blind signature. This common information may represent the date or the amount of e-cash. Due to its un-traceablility and partial blindness property, the partially blind signature plays an important role in many e-commerce applications. Based on the RSA scheme, we propose a partially blind threshold signature with low-computational load for the client.
Learning Balls of Strings with Correction Queries
Authors: Leonor Becerra Bonache, Colin Higuera, Jean-Christophe Janodet, Frédéric Tantini
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

During the 80's, Angluin introduced an active learning paradigm, using an Oracle, capable of answering both membership and equivalence queries. However, practical evidence tends to show that if the former are often available, this is usually not the case of the latter. We propose new queries, called correction queries, which we study in the framework of Grammatical Inference. When a string is submitted to the Oracle, either she validates it if it belongs to the target language, or she proposes a correction, i.e., a string of the language close to the query with respect to the edit distance. We also introduce a non-standard class of languages: The topological balls of strings. We show that this class is not learnable in Angluin's Mat model, but is with a linear number of correction queries. We conduct several experiments with an Oracle simulating a human Expert, and show that our algorithm is resistant to approximate answers.
Life-Long Learning, Learner Models and Augmented Cognition
Authors: Judy Kay
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Our field of Intelligent Tutoring Systems has long been inspired by the vision of achieving huge improvements in learning via expert personalised teaching. As we now see computers become ubiquitous and pervasive, we can broaden that vision to include new ways to learn what we need to know, when we need to know it, throughout our lives. In this 20th anniversary of the ITS conferences, we can see that the future will bring an ITS vision that is broadened to include augmented cognition, where systems provide, not only teaching, but also the means to augment our memory by facilitating access to information as needed, be that as mediated contact with other people or access to our own external memory, a collection of the things we want to be able to re-find or remember as needed.Central to this vision is the life-long learner model because it bears the responsibility for modelling relevant aspects of the learner so that an ITS can help us access the information we need to meet our needs. This talk draws on the foundations of ITS work to create a view of the nature of that life-long learner model, the processes of life-long learner modelling and the ways that an ITS can make use of these. The talk illustrates the vision in terms of representations of learner models, user interface and other practical concerns such as privacy.
Embodied Agents on Commercial Websites: Modeling Their Effects through an Affective Persuasion Route
Authors: Pablo Diesbach, David Midgley
Venue: Proceedings of the 3rd international conference on Persuasive Technology (2008)

The effects of embodied virtual agents have been widely studied but too often without a theoretical model of such effects. This paper proposes and tests a two-step affective route of persuasion, based on perspectives from psychology and consumer behavior. Step 1 posits effects of the agent on affect. Step 2 posits effects of affect on a construct of website stickiness. The model is tested in an experiment. The construct of stickiness is validated and the affective route is partially confirmed.
How to Succeed in Business Using LinkedIn: Making Connections and Capturing Opportunities on the World's #1 Business Networking Site
Authors: Eric Butow, Kathleen Taylor
Venue:  (2008)

Hailed as the MySpace for professionals, LinkedIn has taken the Internet by storm. It is now estimated that 19 million business professionals log in to this site every day to look for job opportunities, search for potential clients, get recommendations from colleagues, and reconnect with former coworkers. LinkedIn allows users to create a profile detailing their professional experience and accomplishments, which will help them connect with literally thousands of qualified professionals in their field. How to Succeed in Business Using LinkedIn takes users through every aspect of the site, from getting registered and building their network to posing questions and creating groups. Readers will learn how to: create their home page so other users can find them give and receive references search for experts in their field find leads market their business look for and become a service provider find and recruit for jobs conduct business research discover people outside their networks Unique and practical, this is the only book available that shows users how to make the most of LinkedIn and the powerful networking opportunities it offers.
Solving Variational Inequality Problems with Linear Constraints Based on a Novel Recurrent Neural Network
Authors: Youshen Xia, Jun Wang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Variational inequalities with linear inequality constraints are widely used in constrained optimization and engineering problems. By extending a new recurrent neural network [14], this paper presents a recurrent neural network for solving variational inequalities with general linear constraints in real time. The proposed neural network has one-layer projection structure and is amenable to parallel implementation. As a special case, the proposed neural network can include two existing recurrent neural networks for solving convex optimization problems and monotone variational inequality problems with box constraints, respectively. The proposed neural network is stable in the sense of Lyapunov and globally convergent to the solution under a monotone condition of the nonlinear mapping without the Lipschitz condition. Illustrative examples show that the proposed neural network is effective for solving this class of variational inequality problems.