ppxscal/embedding_network huggingface dataset:
An Algorithm for Inferring Mitogenome Rearrangements in a Phylogenetic Tree
Authors: Matthias Bernt, Daniel Merkle, Martin Middendorf
Venue: Proceedings of the international workshop on Comparative Genomics (2008)

Given the mitochondrial gene orders and the phylogenetic relationship of a set of unichromosomal taxa, we study the problem of finding a plausible and parsimonious assignment of genomic rearrangement events to the edges of the given phylogenetic tree. An algorithm called algorithm TreeREx (tree rearrangement explorer) is proposed for solving this problem heuristically. TreeREx is based on an extended version of algorithm CREx (common interval rearrangement explorer, [4]) that heuristically computes pairwise rearrangement scenarios for gene order data. As phylogenetic events in such scenarios reversals, transpositions, reverse transpositions, and tandem duplication random loss (TDRL) operations are considered. CREx can detect such events as patterns in the signed strong interval tree, a data structure representing gene groups that appear consecutively in a set of two gene orders. TreeREx then tries to assign events to the edges of the phylogenetic tree, such that the pairwise scenarios are reflected on the paths of the tree. It is shown that TreeREx can automatically infer the events and the ancestral gene orders for realistic biological examples of mitochondrial gene orders. In an analysis of gene order data for teleosts, algorithm TreeREx is able to identify a yet undocumented TDRL towards species Bregmaceros nectabanus.
Engineering Privacy Requirements in Business Intelligence Applications
Authors: Annamaria Chiasera, Fabio Casati, Florian Daniel, Yannis Velegrakis
Venue: Proceedings of the 5th VLDB workshop on Secure Data Management (2008)

In this paper we discuss the problem of engineering privacy requirements for business intelligence applications, i.e., of eliciting, modeling, testing, and auditing privacy requirements imposed by the source data owner on the business intelligence applications that use these data to compute reports for analysts. We describe the peculiar challenges of this problem, propose and evaluate different solutions for eliciting and modeling such requirements, and make the case in particular for what we experienced as being the most promising and realistic approach: eliciting and modeling privacy requirements on the reports themselves, rather than on the source or as part of the data warehouse.
Horizontal Case Representation
Authors: Rosina Weber, Sidath Gunawardena, Craig Macdonald
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

We present a new case representation that seeks to make case-based reasoning (CBR) more suited to real world applications. We propose a horizontal representation that is composed of two features, one to represent the problem and one to represent the solution. We also present a similarity metric tailored to our representation. Rather than parametrizing the distance function with weights, it requires one parameter that recommends the cardinality of values for new problems to be solved by the system. Our representation is less restrictive during case acquisition as it does not constrain how non-experts can populate cases and it requires less knowledge engineering effort than the traditional method. We compare our representation to the traditional case representation and show that it is superior when cases are incomplete. Finally, we illustrate the effectiveness of our representation in a real world application, where the demarcation between problem and solution is blurred.
Fast Computations of Gröbner Bases and Blind Recognitions of Convolutional Codes
Authors: Peizhong Lu, Yan Zou
Venue: Proceedings of the 1st international workshop on Arithmetic of Finite Fields (2007)

This paper provides a fast algorithm for Gröbner bases of homogenous ideals of the ring $\Bbb{F}[x,y]$ over a field $\Bbb{F}$. The computational complexity of the algorithm is O(N 2), where N is the maximum degree of the input generating polynomials. The new algorithm can be used to solve a problem of blind recognition of convolutional codes. This is a new generalization of the important problem of synthesis of a linear recurring sequence.
Driver Assistance System Based on Monocular Vision
Authors: Yu-Min Chiang, No-Zen Hsu, Kuan-Liang Lin
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

The rapid growing volume of traffic has a critical affect on economic development but causes large amount of traffic accidents. The paper attempts to develop a driver assistance system based on monocular vision. Main function of the system is to find a collision-free path by lane tracking and obstacle detection. The paper proposes a lane markings detection method which is applicable to variant illumination and complex outdoor environment. The built system will issue a warning signal when it detects a lane departure of the vehicle. For obstacle detection, we use gradient information to find the feature points of the object first and estimate the distance of the object by means of triangulation. Experimental results demonstrate the effectiveness of the proposed approach.
Arctic Termination ...Below Zero
Authors: Adam Koprowski, Johannes Waldmann
Venue: Proceedings of the 19th international conference on Rewriting Techniques and Applications (2008)

We introduce the arctic matrix method for automatically proving termination of term rewriting. We use vectors and matrices over the arctic semi-ring: natural numbers extended with -¿,with the operations "max" and "plus". This extends the matrix method for term rewriting and the arctic matrix method for string rewriting. In combination with the Dependency Pairs transformation, this allows for some conceptually simple termination proofs in cases where only much more involved proofs were known before. We further generalize to arctic numbers "below zero": integers extended with -¿.This allows to treat some termination problems with symbols that require a predecessor semantics. The contents of the paper has been formally verified in the Coq proof assistant and the formalization has been contributed to the CoLoR library of certified termination techniques. This allows formal verification of termination proofs using the arctic matrix method. We also report on experiments with an implementation of this method which, compared to results from 2007, outperforms TPA (winner of the certified termination competition for term rewriting), and in the string rewriting category is as powerful as Matchbox was but now all of the proofs are certified.
A Hybrid Case-Based Approach for Retrieving User's Preference and Strategy in Buyer-Seller Negotiation
Authors: Fang Fang, Ye Xin
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The problem of buyer-seller negotiation is extensive, which occurs not only during the negotiation period itself (the gaming problem), but also at the pre and post negotiation phases (the knowledge elicitation and commitment problems respectively). This paper focuses on the part of pre-negotiation phase and proposes a hybrid case-based approach (including CBR, ANN and PSO) in order to effectively elicit user’s negotiation preference and strategy for preparing the formal bargaining period. Based on the proposed CBR approach, past negotiation experiences are sufficiently recorded and effectively stored in the case database; similar previous cases are precisely retrieved and their solutions are adapted to suit the new negotiation problem. The proposed approach for retrieving negotiation knowledge in the pre-negotiation phase can share of past experience and knowledge, provide a fast but reasonable solution, and avoid unnecessary mistakes.
Intentional motion on-line learning and prediction
Authors: Dizan Vasquez, Thierry Fraichard, Olivier Aycard, Christian Laugier
Venue: Machine Vision and Applications (2008)

Predicting motion of humans, animals and other objects which move according to internal plans is a challenging problem. Most existing approaches operate in two stages: (a) learning typical motion patterns by observing an environment and (b) predicting future motion on the basis of the learned patterns. In existing techniques, learning is performed off-line, hence, it is impossible to refine the existing knowledge on the basis of the new observations obtained during the prediction phase. We propose an approach which uses hidden Markov models (HMMs) to represent motion patterns. It is different from similar approaches because it is able to learn and predict in a concurrent fashion thanks to a novel approximate learning approach, based on the growing neural gas algorithm, which estimates both HMM parameters and structure. The found structure has the property of being a planar graph, thus enabling exact inference in linear time with respect to the number of states in the model. Our experiments demonstrate that the technique works in real-time, and is able to produce sound long-term predictions of people motion.
The AIT Multimodal Person Identification System for CLEAR 2007
Authors: Andreas Stergiou, Aristodemos Pnevmatikakis, Lazaros Polymenakos
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

This paper presents the person identification system developed at Athens Information Technology and its performance in the CLEAR 2007 evaluations. The system operates on the audiovisual information (speech and faces) collected over the duration of gallery and probe videos. It comprises of an audio-only (speech), a video-only (face) and an audiovisual fusion subsystem. Audio recognition is based on the Gaussian Mixture modeling of the principal components of composite feature vectors, consisting of Mel-Frequency Cepstral Coefficients and Perceptual Linear Prediction coefficients of speech. Video recognition is based on combining three different classification algorithms: Principal Components Analysis with a modified Mahalanobis distance, sub-class Linear Discriminant Analysis (featuring automatic sub-class generation) with cosine distance and Bayesian classifier based on Gaussian modeling of intrapersonal differences. A nearest neighbor classification rule is applied. A decision fusion scheme across time and classifiers returns the video identity. The audiovisual subsystem fuses the unimodal identities into the multimodal one, using a suitable confidence metric.
Dynamic Buffer Areas Obtained by EFCM Method in GIS Environment
Authors: Ferdinando Martino, Salvatore Sessa
Venue: Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management (2008)

Impact areas in spatial analysis are determined by using density clustering methods which have an elevated computational complexity. We propose an alternative method based on the Extended Fuzzy C-Means (EFCM) and we derive dynamic buffer areas as hypersphere volume prototypes in a GIS environment.
On Lipschitz Embeddings of Graphs
Authors: Kaspar Riesen, Horst Bunke
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

In pattern recognition and related fields, graph based representations offer a versatile alternative to the widely used feature vectors. Therefore, an emerging trend of representing objects by graphs can be observed. This trend is intensified by the development of novel approaches in graph based machine learning, such as graph kernels or graph embedding techniques. These procedures overcome a major drawback of graphs, which consists in a serious lack of algorithms for classification and clustering. The present paper is inspired by the idea of representing graphs by means of dissimilarities and extends previous work to the more general setting of Lipschitz embeddings. In an experimental evaluation we empirically confirm that classifiers relying on the original graph distances can be outperformed by a classification system using the Lipschitz embedded graphs.
Meta-heuristic Algorithm for the Transshipment Problem with Fixed Transportation Schedules
Authors: Zhaowei Miao, Ke Fu, Qi Fei, Fan Wang
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this work, we develop an efficient meta-heuristic genetic algorithm for a different type of transshipment problem with supplier and customer time windows, where flow through the crossdock (a kind of transshipment center) is constrained by fixed transportation schedules and crossdocks capacities, and which was proved to be NP-complete in the strong sense. And computational experiments are conducted to show the proposed heuristic outperforms CPLEX.
CLIVE --- An Artificially Intelligent Chat Robot for Conversational Language Practice
Authors: John Zakos, Liesl Capper
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

This paper presents an artificially intelligent chat robot called CLIVE. The aim of CLIVE is to provide to a useful and engaging method for people learning a foreign language, to practice their conversational skills. Unlike other systems that focus on providing a limited or structured tutoring experience for language learning, CLIVE has the ability of holding open, natural human-like conversations with people on a wide range of topics. This provides users with a life-like experience that is a more natural way of learning a new language. Experiments were conducted between CLIVE and real human users and an analysis of the conversations shows that CLIVE performs with accuracy and is an accepted method of language practice amongst users.
Real Time GPU-Based Fuzzy ART Skin Recognition
Authors: Mario Martínez-Zarzuela, Francisco Javier Díaz Pernas, David González Ortega, José Fernando Díez Higuera, Míriam Antón Rodríguez
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Graphics Processing Units (GPUs) have evolved into powerful programmable processors, becoming increasingly used in many research fields such as computer vision. For non-intrusive human body parts detection and tracking, skin filtering is a powerful tool. In this paper we propose the use of a GPU-designed implementation of a Fuzzy ART Neural Network for robust real-time skin recognition. Both learning and testing processes are done on the GPU using chrominance components in TSL color space. Within the GPU, classification of several pixels can be made simultaneously, allowing skin recognition at high frame rates. System performance depends both on video resolution and number of neural network committed categories. Our application can process 296 fps or 79 fps at video resolutions of 320x240 and 640x480 pixels respectively.
The Kernelized Geometrical Bisection Methods
Authors: Xiaomao Liu, Shujuan Cao, Junbin Gao, Jun Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

In this paper, we developed two new classifiers: the kernelized geometrical bisection method and its extended version. The derivation of our methods is based on the so-called "kernel trick" in which samples in the input space are mapped onto almost linearly separable data in a high-dimensional feature space associated with a kernel function. A linear hyperplane can be constructed through bisecting the line connecting the nearest points between two convex hulls created by mapped samples in the feature space. Computational experiments show that the proposed algorithms are more competitive and effective than the well-known conventional methods.
An Intelligent Aide for Interpreting a Patient's Dialysis Data Set
Authors: Derek Sleeman, Nick Fluck, Elias Gyftodimos, Laura Moss, Gordon Christie
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

Many machines used in the modern hospital settings offer real time physiological monitoring. Haemodialysis machines combine a therapeutic treatment system integrated with sophisticated monitoring equipment. A large array of parameters can be collected including cardiovascular measures such as heart rate and blood pressure together with treatment related data including relative blood volume, ultrafiltration rate and small molecule clearance. A small subset of this information is used by clinicians to monitor treatment and plan therapeutic strategies but it is not usually analysed in any detail. The focus of this paper is the analysis of data collected over a number of treatment sessions with a view to predicting patient physiological behaviour whilst on dialysis and correlating this with clinical characteristics of individual patients.One of the commonest complications experienced by patients on dialysis is symptomatic hypotension. We have taken real time treatment data and outline a program of work which attempts to predict when hypotension is likely to occur, and which patients might be particularly prone to haemodynamic instability. This initial study has investigated: the rate of change of blood pressure versus rate of change of heart rate, rate of fluid removal, and rate of uraemic toxin clearance. We have used a variety of machine learning techniques (including hierarchical clustering, and Bayesian Network analysis algorithms). We have been able to detect from this dataset, 3 distinct groups which appear to be clinically meaningful. Furthermore we have investigated whether it is possible to predict changes in blood pressure in terms of other parameters with some encouraging results that merit further study.
Outlier detection by network flow
Authors: Alan P. Sprague
Venue:  (2007)

Outliers are those points which are different from or inconsistent with the rest of the data. Novel, new, abnormal, unusual or noisy information can all be called outliers. Sometimes the outliers are more interesting than the majority of the data, such as the applications of intrusion detection and unusual usage of credit cards. With the increase of the complexity and variety of datasets, the challenges of outlier detection are how to catch similar outliers as a group, and how to evaluate the outliers. The research described in this dissertation has investigated the above challenges and has addressed the solutions. A novel method for outlier and outlier group identification which is based on network flow has been proposed in this dissertation. It uses the Maximum Flow Minimum Cut theorem from graph theory to find the outliers and strong outlier groups, and evaluate the outliers by outlier degrees. This algorithm uses k-nearest neighbors to find density connected points of the dataset. Data relationships are magnified to expose loosely connected outliers by the network settings. It can repair poor quality clusters generated by any clustering algorithm, in particular, to solve the problem that points supposed to be separated are in one cluster. As part of the experimental evaluation, our algorithm has been compared with density-based, distribution-based, and conventional statistic outlier detection algorithms, and it is more effective at finding outlier groups. For high dimensional data, this algorithm is also effective at detecting outliers and outlier groups. The effectiveness of this algorithm is demonstrated by improving the query accuracy of content-based image retrieval.
Mining Frequent Diamond Episodes from Event Sequences
Authors: Takashi Katoh, Kouichi Hirata, Masateru Harao
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In this paper, we introduce a diamond episode of the form s 1¿E¿s 2, where s 1 and s 2 are events and E is a set of events. The diamond episode s 1¿E¿s 2 means that every event of E follows an event s 1 and is followed by an event s 2. Then, by formulating the support of diamond episodes, in this paper, we design the algorithm FreqDmd to extract all of the frequent diamond episodes from a given event sequence. Finally, by applying the algorithm FreqDmd to bacterial culture data, we extract diamond episodes representing replacement of bacteria.
Bi-directional Reasoning Based on BAM Neural Networks
Authors: Min Li, Wen Chen, Kai Li, Xianshan Zhou, Lihui Zhao, Yuncai Zhou
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Neural network is a type of network that carries out information processing through the interaction of neurons. The storage of knowledge and information shows distributed physical connection of mutual-linking network components. Bi-directional associative memories (BAM) neural network is a type of feedback neural network system of bi-directional stability, which exists simple characteristics that can be achieved by large-scale integrate circuit. The paper described general reasoning tactics and their Characteristics, studied the theoretic gist of reasoning using BAM neural networks, and analyzed the running methods of BAM under different reasoning tactics through example. Finally, the problem of network capacity was discussed and the further investigative direction was pointed out.
Efficient Mining of Frequent Itemsets from Data Streams
Authors: Carson Kai-Sang Leung, Dale A. Brajczuk
Venue: Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge (2008)

As technology advances, floods of data can be produced and shared in many applications such as wireless sensor networks or Web click streams. This calls for efficient mining techniques for extracting useful information and knowledge from streams of data. In this paper, we propose a novel algorithm for stream mining of frequent itemsets in a limited memory environment. This algorithm uses a compact tree structure to capture important contents from streams of data. By exploiting its nice properties, such a tree structure can be easily maintained and can be used for mining frequent itemsets, as well as other patterns like constrained itemsets, even when the available memory space is small.
Identifying and Analysing Germany's Top Blogs
Authors: Darko Obradovič, Stephan Baumann
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

Blogs are popular communication instruments in today's web and altogether, they form the so-called blogosphere. This blogosphere has repeatedly been subject to structural analyses, while a compilation of the definitive top blogs is still missing. Using the German blogosphere as an example, we developed a method to identify an accurate representation of the top blogs and used their reciprocal recommendations as the input for social network algorithms. These revealed interesting positions of blogs that are not respected by existing ranking services up to date.