ppxscal/embedding_network huggingface dataset:
Retrieval of Case Law to Provide Layman with Information about Liability: Preliminary Results of the BEST-Project
Authors: Elisabeth M. Uijttenbroek, Arno R. Lodder, Michel C. Klein, Gwen R. Wildeboer, Wouter Steenbergen, Rory L. Sie, Paul E. Huygen, Frank Harmelen
Venue: Computable Models of the Law: Languages, Dialogues, Games, Ontologies (2008)

This paper describes the experiments carried out in the context of the BEST-project, an interdisciplinary project with researchers from the Law faculty and the AI department of the VU University Amsterdam. The aim of the project is to provide laymen with information about their legal position in a liability case, based on retrieved case law. The process basically comes down to (1) analyzing the input of a layman in terms of a layman ontology, (2) mapping this ontology to a legal ontology, (3) retrieve relevant case law based, and finally (4) present the results in a comprehensible way to the layman. This paper describes the experiments undertaken regarding step 4, and in particular step 3.
An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification
Authors: Koichiro Doi, Tetsuya Yamashita, Akihiro Yamamoto
Venue: Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers (2007)

In this paper, we give an algorithm for computing the value of the kernel function K TERM , which takes a pair of terms in first-order logic as its inputs, and facilitates Support Vector Machines classifying terms in a higher dimension space. The value of K TERM (s,t) is given as the total number of terms which subsume both s and t. The algorithm presented in the paper computes K TERM (s,t) without enumerating all such terms. We also implement the algorithm and present some experimental examples of classification of first-order terms with K TERM . Furthermore, we also propose the concept of intentional kernels as a generalization of K TERM .
Predicting Human Immunodeficiency Virus (HIV) Drug Resistance Using Recurrent Neural Networks
Authors: Isis Bonet, María M. García, Yvan Saeys, Yves Peer, Ricardo Grau
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%.
Application of KPCA and PNN for Robust Speaker Identification
Authors: Xue-Hui Ren, Ya-Fen Zhang, Yu-Juan Xing, Ming Li
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

This paper presents a robust speaker identification approach basing on kernel principle component analysis (KPCA) and probabilistic neural network (PNN). KPCA is exploited to reduce the dimension of input vector and to denoise speech signal by extracting the nonlinear principle components of the feature vector. The extracted principle components are utilized as the input feature vector of the classifier and a probabilistic neural network (PNN) is designed as the classifier of identification system. We have tested our system on KING corpus and the experimental result shows that our system outperforms PNN and GMM approach in terms of robustness and training time.
A Discrete Binary Version of the Electromagnetism-Like Heuristic for Solving Traveling Salesman Problem
Authors: Nikbakhsh Javadian, Mohsen Gol Alikhani, Reza Tavakkoli-Moghaddam
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

The electromagnetism-like method (EM) is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points (i.e., our solutions) towards the optimality. In general, the EM method has been initially used for solving continuous optimization problems and could not be applied on combinatorial optimization ones. This paper proposes a discrete binary version of the electromagnetism-like method (EM) for solving the combinatorial optimization problems. To show the efficiency of our proposed EM, we use it for solving the traveling salesman problem and compare our computational results with those reported in the literature. Finally we conclude that our method is capable of solving such well-known problems more efficiently than the previous works.
Model-less pose tracking
Authors: Wong Kin Hong
Venue:  (2007)

Acquiring 3-D motion of a camera from image sequences is one of the key components in a wide range of applications such as human computer interaction. Given the 3-D structure, the problem of camera motion recovery can he solved using the model-based approaches, which are well-known and have good performance under a controlled environment. If prior information on the scene is not available, traditional Structure from Motion (SFM) algorithms, which simultaneously estimate the scene structure and pose information, are required. The research presented in this thesis belongs to a different category: Motion from Motion (MFM), in which the main concern is the camera position and orientation. To be more precise, MFM algorithms have the capability of estimating 3-D camera motion directly from 2-D image motion without the explicit reconstruction of the scene structure, even though the 3-D model structure is not known in prior. As keeping track of the structural information is no longer required, putting these types of algorithms into real applications is relatively easy and convenient. The objective of this thesis is to develop a high-speed recursive approach that tackles the MFM problem. On the way to the final goal, a series of methods, each having its own strengths and characteristics, have been studied. (1) The first algorithm computes the camera pose from a monocular image sequence. The trifocal tensor is incorporated into the Extended Kalman Filter (EKF) formulation. The step of computing the 3-D models can thus be eliminated. (2) The proposed approach is then extended to the recovery of motion from a stereo image sequence. By applying the trifocal tensor to a stereo vision framework, the trifocal constraint becomes more robust and is not likely to be degenerate. In addition, the twist motion model is adopted to parameterize the 3-D motion. It does not suffer from singularities as Euler angles, and is minimal as opposed to quaternion and the direct use of rotation matrix. (3) The third method introduces the Interacting Multiple Model Probabilistic Data Association Filter (IMMPDAF) to the MFM problem. The Interacting Multiple Model (IMM) technique allows the existence of more than one dynamic system and in return leads to improved accuracy and stability even under abrupt motion changes. The Probabilistic Data Association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. As the PDA associates stereo correspondences probabilistically, the explicit establishment of stereo matches is not necessary except during initialization, and the point features present in the outer region of the stereo images can be utilized. It is demonstrated in the experiments that the proposed algorithms are efficient, stable and accurate compared to several existing approaches. Furthermore, they have been put into applications such as mixed reality, virtual reality, robotics and super-resolution to show their performance in real situations.
Lower bounds for the transition complexity of NFAs
Authors: Michael Domaratzki, Kai Salomaa
Venue: Journal of Computer and System Sciences (2008)

We construct regular languages L"n, n>=1, such that any NFA recognizing L"n needs @W(nsc(L"n)@?nsc(L"n)) transitions. Here nsc(L"n) is the nondeterministic state complexity of L"n. Also, we study trade-offs between the number of states and the number of transitions of an NFA. We show that adding one additional state can result in significant reductions in the number of transitions and that there exist regular languages L"n, n>=2, where the transition minimal NFA for L"n has more than c@?nsc(L"n) states, for some constant c>1.
Feature Extraction and Classification for Graphical Representations of Data
Authors: Jinjia Wang, Jing Li, Wenxue Hong
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

The barycentre graphical feature extraction method of the star plot is proposed, based on the graphical representation of multi-dimensional data. Because for the different feature order the same multi-dimensional data lead to the different star plots, and extract the different barycentre graphical features, which affect the classification error of the classifiers. The novel feature order method based on the improved genetic algorithm (GA) is proposed. Meanwhile the traditional feature order method based on the feature selection is researched and the traditional vector feature extraction methods are researched. The experiments results of the 4 real data sets show the classification effectiveness of the new graphical representation and graphical features.
An Audio-Haptic Interface Concept Based on Depth Information
Authors: Delphine Devallez, Davide Rocchesso, Federico Fontana
Venue: Proceedings of the 3rd international workshop on Haptic and Audio Interaction Design (2008)

We present an interaction tool based on rendering distance cues for ordering sound sources in depth. The user interface consists of a linear position tactile sensor made by conductive material. The touch position is mapped onto the listening position on a rectangular virtual membrane, modeled by a bidimensional Digital Waveguide Mesh and providing distance cues. Spatialization of sound sources in depth allows a hierarchical display of multiple audio streams, as in auditory menus. Besides, the similar geometries of the haptic interface and the virtual auditory environment allow a direct mapping between the touch position and the listening position, providing an intuitive and continuous interaction tool for auditory navigation.
Modeling of Thermo-Mechanical Behavior of Saturated Clays
Authors: E. L. Liu, H. L. Xing
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

A new thermo-elasto-plastic constitutive model of saturated clay is proposed here based on the double hardening model for clays and available experimental results, which describes the effects of temperature and over consolidation ratio on the mechanical properties of saturated clays by introducing the two hardening parameters -$\sigma_{c}^\prime$ a. Finally, the proposed model is applied to model the related important features of saturated clays under the different temperatures and loadings and compared with the available experimental results to demonstrate its accuracy.
Talk to the Hand: An Agenda for Further Research on Tactile Graphics
Authors: Frances Aldrich
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Tactile graphics are the primary means by which blind people access maps, graphs, diagrams and other graphical representations. Tactile graphics are made up of raised lines, areas, textures and symbols, and are intended to be felt rather than seen [1], [2] and [3].Major obstacles to the successful use of tactile graphics are that: touch cannot discriminate the fine detail that sight can; extracting information through a sequence of touches, then re-integrating it, imposes a heavy memory load; and many graphical representations need visual experience for interpretation.
The Smoothed Complexity of Edit Distance
Authors: Alexandr Andoni, Robert Krauthgamer
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We initiate the study of the smoothed complexity of sequence alignment, by proposing a semi-random model of edit distance between two input strings, generated as follows. First, an adversary chooses two binary strings of length d and a longest common subsequence A of them. Then, every character is perturbed independently with probability p, except that A is perturbed in exactly the same way inside the two strings. We design two efficient algorithms that compute the edit distance on smoothed instances up to a constant factor approximation. The first algorithm runs in near-linear time, namely d1 + &epsilon; for any fixed &epsilon;> 0. The second one runs in time sublinear in d, assuming the edit distance is not too small. These approximation and runtime guarantees are significantly better then the bounds known for worst-case inputs, e.g. near-linear time algorithm achieving approximation roughly d1/3, due to Batu, Erg&uuml;n, and Sahinalp [SODA 2006]. Our technical contribution is twofold. First, we rely on finding matches between substrings in the two strings, where two substrings are considered a match if their edit distance is relatively small, a prevailing technique in commonly used heuristics, such as PatternHunter of Ma, Tromp and Li [Bioinformatics, 2002]. Second, we effectively reduce the smoothed edit distance to a simpler variant of (worst-case) edit distance, namely, edit distance on permutations (a.k.a. Ulam's metric). We are thus able to build on algorithms developed for the Ulam metric, whose much better algorithmic guarantees usually do not carry over to general edit distance.
MPI in Wireless Sensor Networks
Authors: Yannis Mazzer, Bernard Tourancheau
Venue: Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2008)

This paper presents the possible usage of MPI in our Wireless Sensor Networking software stack project.
Collaborative Learning Tool Applying to C Programming Language
Authors: Wen-Chih Chang, Kuen-Chi Chen
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

Collaborative learning, which includes activities of interaction between learners, share knowledge each other, and cooperate in finishing some tasks, is a popular research topic in the past decades. The essence of collaborative learning is that active participation is significant in the learning process and that learners share the valuable knowledge to the other learners in traditional classroom. Nowadays, computers and information technology (IT) become a general component on a lot of aspects of education. The combination of collaborative learning and information technology is commonly called "Computer Supported Collaborative Learning" (CSCL), and that is currently having much attention. Therefore, we have developed a friendly server / client tool, which embedded voice and text chat communication to support collaborative learning via internet. Learner can study from group's collaborative learning, find and solve the problem of C programming language designing through communicating and discussing. Besides, it makes users gained the experience and knowledge of program designing efficiently.
Reconfigurable hardware implementation of a fast and efficient motion detection algorithm
Authors: E. M. Saad, A. Hamdy, M. M. Abutaleb
Venue: Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (2008)

In this paper, we propose a fast motion detection algorithm based on a multi-modal distribution to detect the moving objects in image sequence of size 768 × 576 pixels taken from a static camera with a small number of calculations to achieve a high frame rate. We show that the elapsed time per frame is reduced by applying our algorithm. Two major features make our algorithm a good candidate for hardware implementation. First, it is primarily composed of linear operations that are easily implemented in hardware. Linear operations can be represented efficiently in terms of the number of logic blocks required, and can be computed in one clock cycle. Second, there is no iteration for any explicit coarse-to-fine control strategy. This property makes the real time flow of data possible through the hardware. This paper describes also an FPGA-based implementation of the proposed motion detection algorithm at a very high frame rate that reaches to 1130 frames per second in a single FPGA chip, which is adequate for most real-time vision applications.
Modelling and Mining of Networked Information Spaces
Authors: William Aiello, Andrei Broder, Jeannette Janssen, Evangelos Milios
Venue: Algorithms and Models for the Web-Graph: Fourth International Workshop, WAW 2006, Banff, Canada, November 30 - December 1, 2006. Revised Papers (2007)

In recent years, the emergence of the Web and the dramatic increase in computing, storage and networking capacity has given rise to the concept of networked information spaces. The prime example of a networked information space is the World Wide Web itself. The Web, in its pure form, is a set of hypertext documents, with links in one document pointing to another document.
An Estimation of the Optimal Gaussian Kernel Parameter for Support Vector Classification
Authors: Wenjian Wang, Liang Ma
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

The selection of kernel function and its parameters has heavy influence on the generalization performance of support vector machine (SVM), and it becomes a focus on SVM researches. At present, there are not general rules to select an optimal kernel function for a given problem yet, alternatively, Gaussian and Polynomial kernels are commonly used for practice applications. Based on the relationship analysis of Gaussian kernel support vector machine and scale space theory, this paper proves the existence of a certain range of the parameter ¿ , within the range the generalization performance is good. An appropriate ¿ within the range can be achieved via dynamic evaluation as well. Simulation results demonstrate the feasibility and effectiveness of the presented approach.
The Shape of Technical and Economical Conditioning of Distribution Centers in Optimization Problem of Supply Chain
Authors: Fijalkowski Janusz, Ambroziak Tomasz, Jacyna Marianna
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The paper presents a designing approach to the supply chain optimization problem in a distribution systems. The supply chain is treated as the distribution system (DS). Authors present parameters and indicators for designing and evaluating distribution system.
A method for validating the aggregated signaling traffic model using simulations in ns-2 platform
Authors: Jordi Mongay Batalla, Robert Janowski
Venue: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (2008)

In the architectures of the networks, which offer Quality of Service (QoS), the setup procedure is the exchange of signaling messages to agree the codec of the multimedia applications and to reserve the resources in the network. This exchange of packets between two or more signaling entities causes that this traffic strongly depends on the network scenario and network conditions. Therefore, it is very difficult to find general signaling traffic models, which are valid for any scenario. In this paper we propose a method for validating the aggregated signaling traffic model. This validation is performed using our own developed module of the ns-2 simulator, which simulates the signaling message exchange during the connection setup in an IP based network with QoS guaranties. For this new simulation tool, we provide technical description and discuss some implementation issues under ns-2 platform. The final results of the validation process show under what traffic conditions the versatile Poisson model is valid. The drawn conclusions provide some guidelines for the usage of analytical models in the network provisioning process concerned with signaling traffic.
REFACING: An autonomic approach to network security based on multidimensional trustworthiness
Authors: F. Oliviero, L. Peluso, S. P. Romano
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

Several research efforts have recently focused on achieving distributed anomaly detection in an effective way. As a result, new information fusion algorithms and models have been defined and applied in order to correlate information from multiple intrusion detection sensors distributed inside the network. In this field, an approach which is gaining momentum in the international research community relies on the exploitation of the Dempster-Shafer (D-S) theory. Dempster and Shafer have conceived a mathematical theory of evidence based on belief functions and plausible reasoning, which is used to combine separate pieces of information (evidence) to compute the probability of an event. However, the adoption of the D-S theory to improve distributed anomaly detection efficiency generally involves facing some important issues. The most important challenge definitely consists in sorting the uncertainties in the problem into a priori independent items of evidence. We believe that this can be effectively carried out by looking at some of the principles of autonomic computing in a self-adaptive fashion, i.e. by introducing support for self-management, self-configuration and self-optimization functionality. In this paper, we intend to tackle some of the above mentioned issues by proposing the application of the D-S theory to network information fusion. This will be done by proposing a model for a self-management supervising layer exploiting the innovative concept of multidimensional reputation, which we have called REFACING (RElationship-FAmiliarity-Confidence-INteGrity).
New results of periodic solutions for forced Rayleigh-type equations
Authors: Yaqiong Li, Lihong Huang
Venue: Journal of Computational and Applied Mathematics (2008)

In this paper, we use the coincidence degree theory to establish new results on the existence and uniqueness of T-periodic solutions for a kind of forced Rayleigh equation of the form x^''+f(t,x^'(t))+g(t,x(t))=e(t).